By: Alifiya Sadikali – Trendmicro August 09, 2023 Read time: 4 min (1179 words)
Discover the core principles and frameworks of Zero Trust, NIST 800-207 guidelines, and best practices when implementing CISA’s Zero Trust Maturity Model.
With the growing number of devices connected to the internet, traditional security measures are no longer enough to keep your digital assets safe. To protect your organization from digital threats, it’s crucial to establish strong security protocols and take proactive measures to stay vigilant.
What is Zero Trust?
Zero Trust is a cybersecurity philosophy based on the premise that threats can arise internally and externally. With Zero Trust, no user, system, or service should automatically be trusted, regardless of its location within or outside the network. Providing an added layer of security to protect sensitive data and applications, Zero Trust only grants access to authenticated and authorized users and devices. And in the event of a data breach, compartmentalizing access to individual resources limits potential damage.
Your organization should consider Zero Trust as a proactive security strategy to protect its data and assets better.
The pillars of Zero Trust
At its core, the basis for Zero Trust is comprised of a few fundamental principles:
Verify explicitly. Only grant access once the user or device has been explicitly authenticated and verified. By doing so, you can ensure that only those with a legitimate need to access your organization’s resources can do so.
Least privilege access. Only give users access to the resources they need to do their job and nothing more. Limiting access in this way prevents unauthorized access to your organization’s data and applications.
Assume breach. Act as if a compromise to your organization’s security has occurred. Take steps to minimize the damage, including monitoring for unusual activity, limiting access to sensitive data, and ensuring that backups are up-to-date and secure.
Microsegmentation. Divide your organization’s network into smaller, more manageable segments and apply security controls to each segment individually. This reduces the risk of a breach spreading from one part of your network to another.
Security automation. Use tools and technologies to automate the process of monitoring, detecting, and responding to security threats. This ensures that your organization’s security is always up-to-date and can react quickly to new threats and vulnerabilities.
A Zero Trust approach is a proactive and effective way to protect your organization’s data and assets from cyber-attacks and data breaches. By following these core principles, your organization can minimize the risk of unauthorized access, reduce the impact of a breach, and ensure that your organization’s security is always up-to-date and effective.
The role of NIST 800-207 in Zero Trust
NIST 800-207 is a cybersecurity framework developed by the National Institute of Standards and Technology. It provides guidelines and best practices for organizations to manage and mitigate cybersecurity risks.
Designed to be flexible and adaptable for a variety of organizations and industries, the framework supports the customization of cybersecurity plans to meet their specific needs. Its implementation can help organizations improve their cybersecurity posture and protect against cyber threats.
One of the most important recommendations of NIST 800-207 is to establish a policy engine, policy administrator, and policy enforcement point. This will help ensure consistent policy enforcement and that access is granted only to those who need it.
Another critical recommendation is conducting continuous monitoring and having real-time risk-based decision-making capabilities. This can help you quickly identify and respond to potential threats.
Additionally, it is essential to understand and map dependencies among assets and resources. This will help you ensure your security measures are appropriately targeted based on potential vulnerabilities.
Finally, NIST recommends replacing traditional paradigms, such as implicit trust in assets or entities, with a “trust but verify” methodology. Adopting this approach can better protect your organization’s assets and resources from internal and external threats.
CISA’s Zero Trust Maturity Model
The Zero Trust Maturity Model (ZMM), developed by CISA, provides a comprehensive framework for assessing an organization’s Zero Trust posture. This model covers critical areas including:
Identity management: To implement a Zero Trust strategy, it is important to begin with identity. This involves continuously verifying, authenticating, and authorizing any entity before granting access to corporate resources. To achieve this, comprehensive visibility is necessary.
Devices, networks, applications: To maintain Zero Trust, use endpoint detection and response capabilities to detect threats and keep track of device assets, network connections, application configurations, and vulnerabilities. Continuously assess and score device security posture and implement risk-informed authentication protocols to ensure only trusted devices, networks and applications can access sensitive data and enterprise systems.
Data and governance: To maximize security, implement prevention, detection, and response measures for identity, devices, networks, IoT, and cloud. Monitor legacy protocols and device encryption status. Apply Data Loss Prevention and access control policies based on risk profiles.
Visibility and analytics: Zero Trust strategies cannot succeed within silos. By collecting data from various sources within an organization, organizations can gain a complete view of all entities and resources. This data can be analyzed through threat intelligence, generating reliable and contextualized alerts. By tracking broader incidents connected to the same root cause, organizations can make informed policy decisions and take appropriate response actions.
Automation and orchestration: To effectively automate security responses, it is important to have access to comprehensive data that can inform the orchestration of systems and manage permissions. This includes identifying the types of data being protected and the entities that are accessing it. By doing so, it ensures that there is proper oversight and security throughout the development process of functions, products, and services.
By thoroughly evaluating these areas, your organization can identify potential vulnerabilities in its security measures and take prompt action to improve your overall cybersecurity posture. CISA’s ZMM offers a holistic approach to security that will enable your organization to remain vigilant against potential threats.
Implementing Zero Trust with Trend Vision One
Trend Vision One seamlessly integrates with third-party partner ecosystems and aligns to industry frameworks and best practices, including NIST and CISA, offering coverage from prevention to extended detection and response across all pillars of zero trust.
Trend Vision One is an innovative solution that empowers organizations to identify their vulnerabilities, monitor potential threats, and evaluate risks in real-time, enabling them to make informed decisions regarding access control. With its open platform approach, Trend enables seamless integration with third-party partner ecosystems, including IAM, Vulnerability Management, Firewall, BAS, and SIEM/SOAR vendors, providing a comprehensive and unified source of truth for risk assessment within your current security framework. Additionally, Trend Vision One is interoperable with SWG, CASB, and ZTNA and includes Attack Surface Management and XDR, all within a single console.
Conclusion
CISOs today understand that the journey towards achieving Zero Trust is a gradual process that requires careful planning, step-by-step implementation, and a shift in mindset towards proactive security and cyber risk management. By understanding the core principles of Zero Trust and utilizing the guidelines provided by NIST and CISA to operationalize Zero Trust with Trend Vision One, you can ensure that your organization’s cybersecurity measures are strong and can adapt to the constantly changing threat landscape.
To read more thought leadership and research about Zero Trust, click here.
By: William Malik – Trendmicro August 14, 2023 Read time: 4 min (1014 words)
Rethinking learning metrics and fostering critical thinking in the era of generative AI and LLMs
I recently participated in a conversation about artificial intelligence, specifically ChatGPT and its kin, with a group of educators in South Africa. They were concerned that the software would help students cheat.
We discussed two possible alternatives to ChatGPT: First, teachers could require that students submit handwritten homework. This would force students to at least read the material once before submitting it; Second, teachers could grade the paper submissions no higher than 89 percent (or a “B”), but that to get an “A,” the student would have to stand in front of the class and verbally discuss the material, their research, their conclusion, and answer any questions the teacher or other classmates might ask. (With that verbal defense of the ideas, the teacher might even waive the requirement for paper submission at all!)
The fundamental problem is that the grading system depends on homework. If education aims to teach an individual both a) a body of knowledge and b) the techniques of reasoning with that knowledge, then the metrics proving that achievement is misaligned.
One of the most quoted management scientists is Fredrick W. Taylor. He is most known for saying, “If you can’t measure it, you can’t manage it.” Interestingly, he never said that – which is fortunate because it is entirely wrong. People always manage things without metrics – from driving a car to raising children. He said: “If you measure it, you’ll manage it” – and he intended that as a warning. Whenever you adopt a metric, you will adjust your assessment of the underlying process in terms of your chosen metric. His warning is to be very careful about which metrics you choose.
Sometime in the past forty years, we decided that the purpose of education is to do well on tests. Unfortunately, that is also wrong. The purpose of education is to teach people to gather evidence and to think clearly about it. Students should learn how to judge various forms of evidence. They should understand rhetorical techniques (in the classical sense – how to render ideas clearly). They should be aware of common errors in thinking – the cognitive pitfalls we all fall into when rushed or distracted and logical fallacies which rob our arguments of their validity.
Large Language Models (LLMs) aggregate vast troves of text. Those data sources are not curated, so LLMs reflect the biases, logical limitations, and cognitive distortions in so much of what’s online. We are all familiar with early chatbots that were easily corrupted – the Microsoft chatbot Tay was perverted into being a racist resonator. (See “Twitter taught Microsoft’s AI Chatbot to be a Racist A**hole in Less than a Day” from The Verge, March 24, 2016, at https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbot-racist accessed Aug 2023.)
LLMs do not think. They scan as much material as possible, then build a set of probabilities about which word is most likely to follow another word. If the word “pterodactyl” occurs in a text, then the next most likely word might be “soaring,” and “flying” might be in second place. If ChatGPT gets the word “pterodactyl” as input, it will put “soaring” next to it. This may look plausible to a person reading the output, but it cannot be correct. Correctness implies some kind of comprehension and judgment. ChatGPT does neither. It merely arranges words based on their statistical likelihood in the LLM’s database. We are now learning that LLMs that ingest computer-generated content become even more skewed – augmenting the likelihood of one word following another by rescanning the previous output. Over time, LLMs fed AI-generated content will drift farther and farther from actual human writing. The oft-mentioned hallucinations that LLMs generate will become more common as the distillation and amplification of the more likely subset of words leads to a contracted pool of possible machine-generated responses. Eventually – if we are not able to prevent LLMs from ingesting already-processed content – the output of ChatGPT will become more and more constrained, which, taken to the extreme, will yield one plot, one answer, one painting, and one outcome regardless of the specific input. Long before then, people will have abandoned LLM-based efforts for any activity that requires creativity.
Where can LLMs help? By sorting through bounded sets of information. That means an LLM trained on protein sequences could rapidly develop a most likely model for a protein that could attack a particular disease or interrupt an allergic reaction. In that case, the issue isn’t seeking creativity but rapidly scanning a set of nearly identical data overreactions to find the few that stand out enough to make a difference. A human doing this kind of work would quickly grow bored and likely make errors. LLMs can help science move quickly through vast quantities of data in closed domains. But when looking at an unbounded domain (art, poetry, fiction, movies, music, and the like), LLMs can only build average content, filling in the space between works. Artists seek to reach beyond the space their prior work defined.
The core problem with LLMs may be unsolvable. At this point, various organizations are exploring ways to tag AI-generated content (written and graphic) so humans can spend a moment assessing the accuracy and validity of the material. Of course, message digests can be corrupted and watermarks forged. A bad actor might maliciously tag authentic content as AI-generated. Recent developments include malicious ChatGPT variants designed to create BEC and phishing email content,
Students will always look for a shortcut, and that habit is difficult to overcome. In business, it will also be tempting for bureaucrats to use tools to simplify their tasks. How will your firm incorporate LLMs safely into your business processes? Organizations should consider how they will audit their internal procedures to ensure that LLM outputs are incorporated appropriately into communications. Imagine the potential for harm if some publicly traded company was found to have used an LLM to develop its annual financial report!
What do you think? Let me know in the comments below, or contact me @wjmalik@noc.social
By: Kazuhisa Tagaya – Trendmicro August 14, 2023 Read time: 2 min (638 words)
The latest study said that OT security is less mature in several capabilities than IT security, but most organizations are improving it.
e asked participants whether OT security for cybersecurity capabilities is less mature or more mature than IT in their organizations with reference to the NIST CSF.
As an average of all items, 39.5% answered that OT has a lower level of maturity. (18% answered OT security is more mature, and 36.4% at the same level)
Categorizing security capabilities into the five cores of the NIST CSF and aggregating them for each core, the most was that Detect is lower maturity in OT security than in IT. (42%)
Figure1: What security capabilities in OT are lower than IT (NIST CSF 5 Core)
Furthermore, looking at the specific security capabilities, the score of “Cyber event detection” is the most(45.7%).
Figure2: What security capabilities in OT are lower than IT (detail)
The OT environment has more diverse legacy assets, and protocol stacks dedicated to ICS/OT, making it difficult to implement sensors to detect malicious behavior or apply the patches on the assets. The inability to implement uniform measures in the same way as IT security is an obstacle to increasing the maturity level.
Detection in OT: Endpoint and Network
The survey asked respondents about their Endpoint Detection and Response (EDR) and Network Security Monitoring (NSM) implementations to measure their visibility in their OT environments. They answered whether EDR (including antivirus) was implemented in the following three places.
Server assets running commercial OS (Windows, Linux, Unix): 41%
Engineering (engineering workstations, instrumentation laptops, calibration and test equipment) assets running commercial OS (Windows, Unix, Linux): 34%
In addition, 76% of organizations that have already deployed EDR said they plan to expand their deployment within 24 months.
Figure3: EDR deployment
We also asked whether NSM (including IDS) was implemented at the following levels referring to the Purdue model.
Purdue Level 4 (Enterprise): 30%
Purdue Level 3.5 (DMZ): 36%
Purdue Level 3 (Site or SCADA-wide): 38%
Purdue Level 2 (Control): 20%
Purdue Levels 1/0 (Sensors and Actuators): 8%
Like EDR, 70% of organizations that have already implemented NSM said they have plans to expand implementation within 24 months.
Figure4: NSM deployment
In this survey, EDR implementation rates tended to vary depending on the respondent’s industry and size of organization. The implementation rate of NSM was relatively high in DMZ and Level 3, and the implementation rate decreased according to the lower layers. But I think it is not appropriate to conclude the decisive trend from the average value in the questions, because there are variations in the places where they are implemented EDR and NSM depending on the organization. The implementation rate shown here is just a rough standard. Where and how much to invest depends on the environment and decision-making of the organization. Asset owners can use the result as a reference to see where to implement EDR and NSM and evaluate their implementation plans.
By: Trend Micro August 15, 2023 Read time: 4 min (1157 words)
The unveiling of the first-ever Open Worldwide Application Security Project (OWASP) risk list for large language model AI chatbots was yet another sign of generative AI’s rush into the mainstream—and a crucial step toward protecting enterprises from AI-related threats.
For more than 20 years, the Open Worldwide Application Security Project (OWASP) top 10 risk list has been a go-to reference in the fight to make software more secure. So it’s no surprise developers and cybersecurity professionals paid close attention earlier this spring when OWASP published an all-new list focused on large language model AI vulnerabilities.
OWASP’s move is yet more proof of how quickly AI chatbots have swept into the mainstream. Nearly half (48%) of corporate respondents to one survey said that by February 2023 they had already replaced workers with ChatGPT—just three months after its public launch. With many observers expressing concern that AI adoption has rushed ahead without understanding of the risks involved, the OWASP top 10 AI risk list is both timely and essential.
Large language model vulnerabilities at a glance
OWASP has released two draft versions of its AI vulnerability list so far: one in May 2023 and a July 1 update with refined classifications and definitions, examples, scenarios, and links to additional references. The most recent is labeled ‘version 0.5’, and a formal version 1 is reported to be in the works.
We did some analysis and found the vulnerabilities identified by OWASP fall broadly into three categories:
Access risks associated with exploited privileges and unauthorized actions.
Data risks such as data manipulation or loss of services.
Reputational and business risks resulting from bad AI outputs or actions.
In this blog, we take a closer look at the specific risks in each case and offer some suggestions about how to handle them.
1. Access risks
Of the 10 vulnerabilities listed by OWASP, four are specific to access and misuse of privileges: insecure plugins, insecure output handling, permissions issues, and excessive agency.
According to OWASP, any large language model that uses insecure plugins to receive “free-form text” inputs could be exposed to malicious requests, resulting in unwanted behaviors or the execution of unauthorized remote code. On the flipside, plugins or applications that handle large language model outputs insecurely—without evaluating them—could be susceptible to cross-site and server-side request forgeries, unauthorized privilege escalations, hijack attacks, and more.
Similarly, when authorizations aren’t tracked between plugins, permissions issues can arise that open the way for indirect prompt injections or malicious plugin usage.
Finally, because AI chatbots are ‘actors’ able to make and implement decisions, it matters how much free reign (i.e., agency) they’re given. As OWASP explains, “When LLMs interface with other systems, unrestricted agency may lead to undesirable operations and actions.” Examples include personal mail reader assistants being exploited to propagate spam or customer service AI chatbots manipulated into issuing undeserved refunds.
In all of these cases, the large language model becomes a conduit for bad actors to infiltrate systems.
2. Data risks
Poisoned training data, supply chain vulnerabilities, prompt injection vulnerabilities and denials of serviceare all data-specific AI risks.
Data can be poisoned deliberately by bad actors who want to harm an organization. It can also be distorted inadvertently when an AI system learns from unreliable or unvetted sources. Both types of poisoning can occur within an active AI chatbot application or emerge from the large language model supply chain, where reliance on pre-trained models, crowdsourced data, and insecure plugin extensions may produce biased data outputs, security breaches, or system failures.
With prompt injections, ill-meaning inputs may cause a large language model AI chatbot to expose data that should be kept private or perform other actions that lead to data compromises.
AI denial of service attacks are similar to classic DOS attacks. They may aim to overwhelm a large language model and deprive users of access to data and apps, or—because many AI chatbots rely on pay-as-you-go IT infrastructure—force the system to consume excessive resources and rack up massive costs.
3. Reputational and business risks
The final OWASP vulnerability (according to our buckets) is already reaping consequences around the world today:overreliance on AI. There’s no shortage of stories about large language models generating false or inappropriate outputs from fabricated citations and legal precedents to racist and sexist language.
OWASP points out that depending on AI chatbots without proper oversight can make organizations vulnerable to publishing misinformation or offensive content that results in reputational damage or even legal action. Given all these various risks, the question becomes, “What can we do about it?” Fortunately, there are some protective steps organizations can take.
What enterprises can do about large language model vulnerabilities
From our perspective at Trend Micro, defending against AI access risks requires a zero-trust security stance with disciplined separation of systems (sandboxing). Even though generative AI has the ability to challenge zero-trust defenses in ways that other IT systems don’t—because it can mimic trusted entities—a zero-trust posture still adds checks and balances that make it easier to identify and contain unwanted activity. OWASP also advises that large language models “should not self-police” and calls for controls to be embedded in application programming interfaces (APIs).
Sandboxing is also key to protecting data privacy and integrity: keeping confidential information fully separated from shareable data and making it inaccessible to AI chatbots and other public-facing systems. (See our recent blog on AI cybersecurity policies for more.)
Good separation of data prevents large language models from including private or personally identifiable information in public outputs, and from being publicly prompted to interact with secure applications such as payment systems in inappropriate ways.
On the reputational front, the simplest remedies are to not rely solely on AI-generated content or code, and to never publish or use AI outputs without first verifying they are true, accurate, and reliable.
Many of these defensive measures can—and should—be embedded in corporate policies. Once an appropriate policy foundation is in place, security technologies such as endpoint detection and response (EDR), extended detection and response (XDR), and security information and event management (SIEM) can be used for enforcement and to monitor for potentially harmful activity.
Large language model AI chatbots are here to stay
OWASP’s initial work cataloguing AI risks proves that concerns about the rush to embrace AI are well justified. At the same time, AI clearly isn’t going anywhere, so understanding the risks and taking responsible steps to mitigate them is critically important.
Setting up the right policies to manage AI use and implementing those policies with the help of cybersecurity solutions is a good first step. So is staying informed. The way we see it at Trend Micro, OWASP’s top 10 AI risk list is bound to become as much of an annual must-read as its original application security list has been since 2003.
Next steps
For more Trend Micro thought leadership on AI chatbot security, check out these resources:
By: Trend Micro August 18, 2023 Read time: 3 min (931 words)
Private 5G networks offer businesses enhanced security, reliability, and scalability. Learn more about why private 5G could be the future of secure networking.
Private 5G networks offer businesses enhanced security, reliability, and scalability. Learn more about why private 5G could be the future of secure networking.
As ransomware attacks continue to grow in number and sophistication, threat actors can quickly impact business operations if organizations are not well prepared. In a recent investigation by Microsoft Incident Response (previously known as Microsoft Detection and Response Team – DART) of an intrusion, we found that the threat actor progressed through the full attack chain, from initial access to impact, in less than five days, causing significant business disruption for the victim organization.
Our investigation found that within those five days, the threat actor employed a range of tools and techniques, culminating in the deployment of BlackByte 2.0 ransomware, to achieve their objectives. These techniques included:
Exploitation of unpatched internet-exposed Microsoft Exchange Servers
Web shell deployment facilitating remote access
Use of living-off-the-land tools for persistence and reconnaissance
Deployment of Cobalt Strike beacons for command and control (C2)
Process hollowing and the use of vulnerable drivers for defense evasion
Deployment of custom-developed backdoors to facilitate persistence
Deployment of a custom-developed data collection and exfiltration tool
Figure 1. BlackByte 2.0 ransomware attack chain
In this blog, we share details of our investigation into the end-to-end attack chain, exposing security weaknesses that the threat actor exploited to advance their attack. As we learned from Microsoft’s tracking of ransomware attacks and the cybercriminal economy that enables them, disrupting common attack patterns could stop many of the attacker activities that precede ransomware deployment. This case highlights that common security hygiene practices go a long way in preventing, identifying, and responding to malicious activity as early as possible to mitigate the impact of ransomware attacks. We encourage organizations to follow the outlined mitigation steps, including ensuring that internet-facing assets are up to date and configured securely. We also share indicators of compromise, detection details, and hunting guidance to help organizations identify and respond to these attacks in their environments.
Forensic analysis
Initial access and privilege escalation
To obtain initial access into the victim’s environment, the threat actor was observed exploiting the ProxyShell vulnerabilities CVE-2021-34473, CVE-2021-34523, and CVE-2021-31207 on unpatched Microsoft Exchange Servers. The exploitation of these vulnerabilities allowed the threat actor to:
Attain system-level privileges on the compromised Exchange host
Enumerate LegacyDN of users by sending Autodiscover requests, including SIDs of users
Construct a valid authentication token and use it against the Exchange PowerShell backend
Impersonate domain admin users and create a web shell by using the New-MailboxExportRequest cmdlet
Create web shells to obtain remote control on affected servers
The threat actor was observed operating from the following IP to exploit ProxyShell and access the web shell:
185.225.73[.]244
Persistence
Backdoor
After gaining access to a device, the threat actor created the following registry run keys to run a payload each time a user signs in:
The file api-msvc.dll (SHA-256: 4a066569113a569a6feb8f44257ac8764ee8f2011765009fdfd82fe3f4b92d3e) was determined to be a backdoor capable of collecting system information, such as the installed antivirus products, device name, and IP address. This information is then sent via HTTP POST request to the following C2 channel:
hxxps://myvisit[.]alteksecurity[.]org/t
The organization was not using Microsoft Defender Antivirus, which detects this malware as Trojan:Win32/Kovter!MSR, as the primary antivirus solution, and the backdoor was allowed to run.
An additional file, api-system.png, was identified to have similarities to api-msvc.dll. This file behaved like a DLL, had the same default export function, and also leveraged run keys for persistence.
Cobalt Strike Beacon
The threat actor leveraged Cobalt Strike to achieve persistence. The file sys.exe (SHA-256: 5f37b85687780c089607670040dbb3da2749b91b8adc0aa411fd6280b5fa7103), detected by Microsoft Defender Antivirus as Trojan:Win64/CobaltStrike!MSR, was determined to be a Cobalt Strike Beacon and was downloaded directly from the file sharing service temp[.]sh:
hxxps://temp[.]sh/szAyn/sys.exe
This beacon was configured to communicate with the following C2 channel:
109.206.243[.]59:443
AnyDesk
Threat actors leverage legitimate remote access tools during intrusions to blend into a victim network. In this case, the threat actor utilized the remote administration tool AnyDesk, to maintain persistence and move laterally within the network. AnyDesk was installed as a service and was run from the following paths:
C:\systemtest\anydesk\AnyDesk.exe
C:\Program Files (x86)\AnyDesk\AnyDesk.exe
C:\Scripts\AnyDesk.exe
Successful connections were observed in the AnyDesk log file ad_svc.trace involving anonymizer service IP addresses linked to TOR and MULLVAD VPN, a common technique that threat actors employ to obscure their source IP ranges.
Reconnaissance
We found the presence and execution of the network discovery tool NetScan being used by the threat actor to perform network enumeration using the following file names:
Additionally, execution of AdFind (SHA-256: f157090fd3ccd4220298c06ce8734361b724d80459592b10ac632acc624f455e), an Active Directory reconnaissance tool, was observed in the environment.
Credential access
Evidence of likely usage of the credential theft tool Mimikatzwas also uncovered through the presence of a related log file mimikatz.log. Microsoft IR assesses that Mimikatz was likely used to attain credentials for privileged accounts.
Lateral movement
Using compromised domain admin credentials, the threat actor used Remote Desktop Protocol (RDP) and PowerShell remoting to obtain access to other servers in the environment, including domain controllers.
Data staging and exfiltration
In one server where Microsoft Defender Antivirus was installed, a suspicious file named explorer.exe was identified, detected as Trojan:Win64/WinGoObfusc.LK!MT, and quarantined. However, because tamper protection wasn’t enabled on this server, the threat actor was able to disable the Microsoft Defender Antivirus service, enabling the threat actor to run the file using the following command:
explorer.exe P@$$w0rd
After reverse engineering explorer.exe, we determined it to be ExByte, a GoLang-based tool developed and commonly used in BlackByte ransomware attacks for collection and exfiltration of files from victim networks. This tool is capable of enumerating files of interest across the network and, upon execution, creates a log file containing a list of files and associated metadata. Multiple log files were uncovered during the investigation in the path:
C:\Exchange\MSExchLog.log
Analysis of the binary revealed a list of file extensions that are targeted for enumeration.
Figure 2. Binary analysis showing file extensions enumerated by explorer.exe
Forensic analysis identified a file named data.txt that was created and later deleted after ExByte execution. This file contained obfuscated credentials that ExByte leveraged to authenticate to the popular file sharing platform Mega NZ using the platform’s API at:
hxxps://g.api.mega.co[.]nz
Figure 3. Binary analysis showing explorer.exe functionality for connecting to file sharing service MEGA NZ
We also determined that this version of Exbyte was crafted specifically for the victim, as it contained a hardcoded device name belonging to the victim and an internal IP address.
ExByte execution flow
Upon execution, ExByte decodes several strings and checks if the process is running with privileged access by reading \\.\PHYSICALDRIVE0:
If this check fails, ShellExecuteW is invoked with the IpOperation parameter RunAs, which runs explorer.exe with elevated privileges.
After this access check, explorer.exe attempts to read the data.txt file in the current location:
If the text file doesn’t exist, it invokes a command for self-deletion and exits from memory:
If data.txt exists, explorer.exe reads the file, passes the buffer to Base64 decode function, and then decrypts the data using the key provided in the command line. The decrypted data is then parsed as JSON below and fed for login function:
{“a”:”us0”,“user”:”<CONTENT FROM data.txt>”}
Finally, it forms a URL for sign-in to the API of the service MEGA NZ:
hxxps://g.api.mega.co[.]nz/cs?id=1674017543
Data encryption and destruction
On devices where files were successfully encrypted, we identified suspicious executables, detected by Microsoft Defender Antivirus as Trojan:Win64/BlackByte!MSR, with the following names:
wEFT.exe
schillerized.exe
The files were analyzed and determined to be BlackByte 2.0 binaries responsible for encryption across the environment. The binaries require an 8-digit key number to encrypt files.
Two modes of execution were identified:
When the -s parameter is provided, the ransomware self-deletes and encrypts the machine it was executed on.
When the -a parameter is provided, the ransomware conducts enumeration and uses an Ultimate Packer Executable (UPX) packed version of PsExec to deploy across the network. Several domain admin credentials were hardcoded in the binary, facilitating the deployment of the binary across the network.
Depending on the switch (-s or -a), execution may create the following files:
C:\SystemData\M8yl89s7.exe (UPX-packed PsExec with a random name; SHA-256: ba3ec3f445683d0d0407157fda0c26fd669c0b8cc03f21770285a20b3133098f)
C:\SystemData\rENEgOtiAtES (A vulnerable (CVE-2019-16098) driver RtCore64.sys used to evade detection by installed antivirus software; SHA-256: 01aa278b07b58dc46c84bd0b1b5c8e9ee4e62ea0bf7a695862444af32e87f1fd)
C:\SystemData\iHu6c4.ico (Random name – BlackBytes icon)
Some capabilities identified for the BlackByte 2.0 ransomware were:
Antivirus bypass
The file rENEgOtiAtES created matches RTCore64.sys, a vulnerable driver (CVE-2049-16098) that allows any authenticated user to read or write to arbitrary memory
The BlackByte binary then creates and starts a service named RABAsSaa calling rENEgOtiAtES, and exploits this service to evade detection by installed antivirus software
Process hollowing
Invokes svchost.exe, injects to it to complete device encryption, and self-deletes by executing the following command:
Ability to terminate running services and processes
Ability to enumerate and mount volumes and network shares for encryption
Perform anti-forensics technique timestomping (sets the file time of encrypted and ReadMe file to 2000-01-01 00:00:00)
Ability to perform anti-debugging techniques
Recommendations
To guard against BlackByte ransomware attacks, Microsoft recommends the following:
Ensure that you have a patch management process in place and that patching for internet-exposed devices is prioritized; Understand and assess your cyber exposure with advanced vulnerability and configuration assessment tools like Microsoft Defender Vulnerability Management
Implement an endpoint detection and response (EDR) solution like Microsoft Defender for Endpoint to gain visibility into malicious activity in real time across your network
Ensure antivirus protections are updated regularly by turning on cloud-based protection and that your antivirus solution is configured to block threats
Enable tamper protection to prevent components of Microsoft Defender Antivirus from being disabled
Block inbound traffic from IPs specified in the indicators of compromise section of this report
Block inbound traffic from TOR exit nodes
Block inbound access from unauthorized public VPN services
Restrict administrative privileges to prevent authorized system changes
Conclusion
BlackByte ransomware attacks target organizations that have infrastructure with unpatched vulnerabilities. As outlined in the Microsoft Digital Defense Report, common security hygiene practices, including keeping systems up to date, could protect against 98% of attacks.
As new tools are being developed by threat actors, a modern threat protection solution like Microsoft 365 Defender is necessary to prevent and detect the multiple techniques used in the attack chain, especially where the threat actor attempts to evade or disable specific defense mechanisms. Hunting for malicious behavior should be performed regularly in order to detect potential attacks that could evade detections, as a complementary activity for continuous monitoring from security tools alerts and incidents.
To understand how Microsoft can help you secure your network and respond to network compromise, visit https://aka.ms/MicrosoftIR.
Microsoft 365 Defender detections
Microsoft Defender Antivirus
Microsoft Defender Antivirus detects this threat as the following malware:
Trojan:Win32/Kovter!MSR
Trojan:Win64/WinGoObfusc.LK!MT
Trojan:Win64/BlackByte!MSR
HackTool:Win32/AdFind!MSR
Trojan:Win64/CobaltStrike!MSR
Microsoft Defender for Endpoint
The following alerts might indicate threat activity related to this threat. Note, however, that these alerts can be also triggered by unrelated threat activity.
‘CVE-2021-31207’ exploit malware was detected
An active ‘NetShDisableFireWall’ malware in a command line was prevented from executing.
Suspicious registry modification.
‘Rtcore64’ hacktool was detected
Possible ongoing hands-on-keyboard activity (Cobalt Strike)
A file or network connection related to a ransomware-linked emerging threat activity group detected
Suspicious sequence of exploration activities
A process was injected with potentially malicious code
Suspicious behavior by cmd.exe was observed
‘Blackbyte’ ransomware was detected
Microsoft Defender Vulnerability Management
Microsoft Defender Vulnerability Management surfaces devices that may be affected by the following vulnerabilities used in this threat:
CVE-2021-34473
CVE-2021-34523
CVE-2021-31207
CVE-2019-16098
Hunting queries
Microsoft 365 Defender
Microsoft 365 Defender customers can run the following query to find related activity in their networks:
ProxyShell web shell creation events
DeviceProcessEvents| where ProcessCommandLine has_any ("ExcludeDumpster","New-ExchangeCertificate") and ProcessCommandLine has_any ("-RequestFile","-FilePath")
Suspicious vssadmin events
DeviceProcessEvents| where ProcessCommandLine has_any ("vssadmin","vssadmin.exe") and ProcessCommandLine has "Resize ShadowStorage" and ProcessCommandLine has_any ("MaxSize=401MB"," MaxSize=UNBOUNDED")
Detection for persistence creation using Registry Run keys
DeviceRegistryEvents | where ActionType == "RegistryValueSet" | where (RegistryKey has @"Microsoft\Windows\CurrentVersion\RunOnce" and RegistryValueName == "MsEdgeMsE") or (RegistryKey has @"Microsoft\Windows\CurrentVersion\RunOnceEx" and RegistryValueName == "MsEdgeMsE")or (RegistryKey has @"Microsoft\Windows\CurrentVersion\Run" and RegistryValueName == "MsEdgeMsE")| where RegistryValueData startswith @"rundll32"| where RegistryValueData endswith @".dll,Default"| project Timestamp,DeviceId,DeviceName,ActionType,RegistryKey,RegistryValueName,RegistryValueData
Microsoft Sentinel
Microsoft Sentinel customers can use the TI Mapping analytics (a series of analytics all prefixed with ‘TI map’) to automatically match the malicious domain indicators mentioned in this blog post with data in their workspace. If the TI Map analytics are not currently deployed, customers can install the Threat Intelligence solution from the Microsoft Sentinel Content Hub to have the analytics rule deployed in their Sentinel workspace. More details on the Content Hub can be found here: https://learn.microsoft.com/azure/sentinel/sentinel-solutions-deploy
Microsoft Sentinel also has a range of detection and threat hunting content that customers can use to detect the post exploitation activity detailed in this blog in addition to Microsoft 365 Defender detections list above.
The table below shows IOCs observed during our investigation. We encourage our customers to investigate these indicators in their environments and implement detections and protections to identify past related activity and prevent future attacks against their systems.
AdFind.exe (Active Directory information gathering tool)
hxxps://myvisit[.]alteksecurity[.]org/t
URL
C2 for backdoor api-msvc.dll
hxxps://temp[.]sh/szAyn/sys.exe
URL
Download URL for sys.exe
109.206.243[.]59
IP Address
C2 for Cobalt Strike Beacon sys.exe
185.225.73[.]244
IP Address
Originating IP address for ProxyShell exploitation and web shell interaction
NOTE: These indicators should not be considered exhaustive for this observed activity.
Appendix
File extensions targeted by BlackByte binary for encryption:
.4dd
.4dl
.accdb
.accdc
.accde
.accdr
.accdt
.accft
.adb
.ade
.adf
.adp
.arc
.ora
.alf
.ask
.btr
.bdf
.cat
.cdb
.ckp
.cma
.cpd
.dacpac
.dad
.dadiagrams
.daschema
.db
.db-shm
.db-wal
.db3
.dbc
.dbf
.dbs
.dbt
.dbv
. dbx
. dcb
. dct
. dcx
. ddl
. dlis
. dp1
. dqy
. dsk
. dsn
. dtsx
. dxl
. eco
. ecx
. edb
. epim
. exb
. fcd
. fdb
. fic
. fmp
. fmp12
. fmpsl
. fol
.fp3
. fp4
. fp5
. fp7
. fpt
. frm
. gdb
. grdb
. gwi
. hdb
. his
. ib
. idb
. ihx
. itdb
. itw
. jet
. jtx
. kdb
. kexi
. kexic
. kexis
. lgc
. lwx
. maf
. maq
. mar
. masmav
. mdb
. mpd
. mrg
. mud
. mwb
. myd
. ndf
. nnt
. nrmlib
. ns2
. ns3
. ns4
. nsf
. nv
. nv2
. nwdb
. nyf
. odb
. ogy
. orx
. owc
. p96
. p97
. pan
. pdb
. pdm
. pnz
. qry
. qvd
. rbf
. rctd
. rod
. rodx
. rpd
. rsd
. sas7bdat
. sbf
. scx
. sdb
. sdc
. sdf
. sis
. spg
. sql
. sqlite
. sqlite3
. sqlitedb
. te
. temx
. tmd
. tps
. trc
. trm
. udb
. udl
. usr
. v12
. vis
. vpd
. vvv
. wdb
. wmdb
. wrk
. xdb
. xld
. xmlff
. abcddb
. abs
. abx
. accdw
. and
. db2
. fm5
. hjt
. icg
. icr
. kdb
. lut
. maw
. mdn
. mdt
Shared folders targeted for encryption (Example: \\[IP address]\Downloads):
Users
Backup
Veeam
homes
home
media
common
Storage Server
Public
Web
Images
Downloads
BackupData
ActiveBackupForBusiness
Backups
NAS-DC
DCBACKUP
DirectorFiles
share
File extensions ignored:
.ini
.url
.msilog
.log
.ldf
.lock
.theme
.msi
.sys
.wpx
.cpl
.adv
.msc
.scr
.key
.ico
.dll
.hta
.deskthemepack
.nomedia
.msu
.rtp
.msp
.idx
.ani
.386
.diagcfg
.bin
.mod
.ics
.com
.hlp
.spl
.nls
.cab
.exe
.diagpkg
.icl
.ocx
.rom
.prf
.thempack
.msstyles
.icns
.mpa
.drv
.cur
.diagcab
.cmd
.shs
Folders ignored:
windows
boot
program files (x86)
windows.old
programdata
intel
bitdefender
trend micro
windowsapps
appdata
application data
system volume information
perflogs
msocache
Files ignored:
bootnxt
ntldr
bootmgr
thumbs.db
ntuser.dat
bootsect.bak
autoexec.bat
iconcache.db
bootfont.bin
Processes terminated:
teracopy
teamviewer
nsservice
nsctrl
uranium
processhacker
procmon
pestudio
procmon64
x32dbg
x64dbg
cff explorer
procexp
pslist
tcpview
tcpvcon
dbgview
rammap
rammap64
vmmap
ollydbg
autoruns
autorunssc
filemon
regmon
idaq
idaq64
immunitydebugger
wireshark
dumpcap
hookexplorer
importrec
petools
lordpe
sysinspector
proc_analyzer
sysanalyzer
sniff_hit
windbg
joeboxcontrol
joeboxserver
resourcehacker
fiddler
httpdebugger
dumpit
rammap
rammap64
vmmap
agntsvc
cntaosmgr
dbeng50
dbsnmp
encsvc
infopath
isqlplussvc
mbamtray
msaccess
msftesql
mspub
mydesktopqos
mydesktopservice
mysqld
mysqld-nt
mysqld-opt
Ntrtscan
ocautoupds
ocomm
ocssd
onenote
oracle
outlook
PccNTMon
powerpnt
sqbcoreservice
sql
sqlagent
sqlbrowser
sqlservr
sqlwriter
steam
synctime
tbirdconfig
thebat
thebat64
thunderbird
tmlisten
visio
winword
wordpad
xfssvccon
zoolz
Services terminated:
CybereasonRansomFree
vnetd
bpcd
SamSs
TeraCopyService
msftesql
nsService
klvssbridge64
vapiendpoint
ShMonitor
Smcinst
SmcService
SntpService
svcGenericHost
Swi_
TmCCSF
tmlisten
TrueKey
TrueKeyScheduler
TrueKeyServiceHelper
WRSVC
McTaskManager
OracleClientCache80
mfefire
wbengine
mfemms
RESvc
mfevtp
sacsvr
SAVAdminService
SepMasterService
PDVFSService
ESHASRV
SDRSVC
FA_Scheduler
KAVFS
KAVFS_KAVFSGT
kavfsslp
klnagent
macmnsvc
masvc
MBAMService
MBEndpointAgent
McShield
audioendpointbuilder
Antivirus
AVP
DCAgent
bedbg
EhttpSrv
MMS
ekrn
EPSecurityService
EPUpdateService
ntrtscan
EsgShKernel
msexchangeadtopology
AcrSch2Svc
MSOLAP$TPSAMA
Intel(R) PROSet Monitoring
msexchangeimap4
ARSM
unistoresvc_1af40a
ReportServer$TPS
MSOLAP$SYSTEM_BGC
W3Svc
MSExchangeSRS
ReportServer$TPSAMA
Zoolz 2 Service
MSOLAP$TPS
aphidmonitorservice
SstpSvc
MSExchangeMTA
ReportServer$SYSTEM_BGC
Symantec System Recovery
UI0Detect
MSExchangeSA
MSExchangeIS
ReportServer
MsDtsServer110
POP3Svc
MSExchangeMGMT
SMTPSvc
MsDtsServer
IisAdmin
MSExchangeES
EraserSvc11710
Enterprise Client Service
MsDtsServer100
NetMsmqActivator
stc_raw_agent
VSNAPVSS
PDVFSService
AcrSch2Svc
Acronis
CASAD2DWebSvc
CAARCUpdateSvc
McAfee
avpsus
DLPAgentService
mfewc
BMR Boot Service
DefWatch
ccEvtMgr
ccSetMgr
SavRoam
RTVsc screenconnect
ransom
sqltelemetry
msexch
vnc
teamviewer
msolap
veeam
backup
sql
memtas
vss
sophos
svc$
mepocs
wuauserv
Drivers that Blackbyte can bypass:
360avflt.sys
360box.sys
360fsflt.sys
360qpesv.sys
5nine.cbt.sys
a2acc.sys
a2acc64.sys
a2ertpx64.sys
a2ertpx86.sys
a2gffi64.sys
a2gffx64.sys
a2gffx86.sys
aaf.sys
aalprotect.sys
abrpmon.sys
accessvalidator.sys
acdriver.sys
acdrv.sys
adaptivaclientcache32.sys
adaptivaclientcache64.sys
adcvcsnt.sys
adspiderdoc.sys
aefilter.sys
agentrtm64.sys
agfsmon.sys
agseclock.sys
agsyslock.sys
ahkamflt.sys
ahksvpro.sys
ahkusbfw.sys
ahnrghlh.sys
aictracedrv_am.sys
airship-filter.sys
ajfsprot.sys
alcapture.sys
alfaff.sys
altcbt.sys
amfd.sys
amfsm.sys
amm6460.sys
amm8660.sys
amsfilter.sys
amznmon.sys
antileakfilter.sys
antispyfilter.sys
anvfsm.sys
apexsqlfilterdriver.sys
appcheckd.sys
appguard.sys
appvmon.sys
arfmonnt.sys
arta.sys
arwflt.sys
asgard.sys
ashavscan.sys
asiofms.sys
aswfsblk.sys
aswmonflt.sys
aswsnx.sys
aswsp.sys
aszfltnt.sys
atamptnt.sys
atc.sys
atdragent.sys
atdragent64.sys
aternityregistryhook.sys
atflt.sys
atrsdfw.sys
auditflt.sys
aupdrv.sys
avapsfd.sys
avc3.sys
avckf.sys
avfsmn.sys
avgmfi64.sys
avgmfrs.sys
avgmfx64.sys
avgmfx86.sys
avgntflt.sys
avgtpx64.sys
avgtpx86.sys
avipbb.sys
avkmgr.sys
avmf.sys
awarecore.sys
axfltdrv.sys
axfsysmon.sys
ayfilter.sys
b9kernel.sys
backupreader.sys
bamfltr.sys
bapfecpt.sys
bbfilter.sys
bd0003.sys
bddevflt.sys
bdfiledefend.sys
bdfilespy.sys
bdfm.sys
bdfsfltr.sys
bdprivmon.sys
bdrdfolder.sys
bdsdkit.sys
bdsfilter.sys
bdsflt.sys
bdsvm.sys
bdsysmon.sys
bedaisy.sys
bemk.sys
bfaccess.sys
bfilter.sys
bfmon.sys
bhdrvx64.sys
bhdrvx86.sys
bhkavka.sys
bhkavki.sys
bkavautoflt.sys
bkavsdflt.sys
blackbirdfsa.sys
blackcat.sys
bmfsdrv.sys
bmregdrv.sys
boscmflt.sys
bosfsfltr.sys
bouncer.sys
boxifier.sys
brcow_x_x_x_x.sys
brfilter.sys
brnfilelock.sys
brnseclock.sys
browsermon.sys
bsrfsflt.sys
bssaudit.sys
bsyaed.sys
bsyar.sys
bsydf.sys
bsyirmf.sys
bsyrtm.sys
bsysp.sys
bsywl.sys
bwfsdrv.sys
bzsenspdrv.sys
bzsenth.sys
bzsenyaradrv.sys
caadflt.sys
caavfltr.sys
cancelsafe.sys
carbonblackk.sys
catflt.sys
catmf.sys
cbelam.sys
cbfilter20.sys
cbfltfs4.sys
cbfsfilter2017.sys
cbfsfilter2020.sys
cbsampledrv.sys
cdo.sys
cdrrsflt.sys
cdsgfsfilter.sys
centrifyfsf.sys
cfrmd.sys
cfsfdrv
cgwmf.sys
change.sys
changelog.sys
chemometecfilter.sys
ciscoampcefwdriver.sys
ciscoampheurdriver.sys
ciscosam.sys
clumiochangeblockmf.sys
cmdccav.sys
cmdcwagt.sys
cmdguard.sys
cmdmnefs.sys
cmflt.sys
code42filter.sys
codex.sys
conduantfsfltr.sys
containermonitor.sys
cpavfilter.sys
cpavkernel.sys
cpepmon.sys
crexecprev.sys
crncache32.sys
crncache64.sys
crnsysm.sys
cruncopy.sys
csaam.sys
csaav.sys
csacentr.sys
csaenh.sys
csagent.sys
csareg.sys
csascr.sys
csbfilter.sys
csdevicecontrol.sys
csfirmwareanalysis.sys
csflt.sys
csmon.sys
cssdlp.sys
ctamflt.sys
ctifile.sys
ctinet.sys
ctrpamon.sys
ctx.sys
cvcbt.sys
cvofflineflt32.sys
cvofflineflt64.sys
cvsflt.sys
cwdriver.sys
cwmem2k64.sys
cybkerneltracker.sys
cylancedrv64.sys
cyoptics.sys
cyprotectdrv32.sys
cyprotectdrv64.sys
cytmon.sys
cyverak.sys
cyvrfsfd.sys
cyvrlpc.sys
cyvrmtgn.sys
datanow_driver.sys
dattofsf.sys
da_ctl.sys
dcfafilter.sys
dcfsgrd.sys
dcsnaprestore.sys
deepinsfs.sys
delete_flt.sys
devmonminifilter.sys
dfmfilter.sys
dgedriver.sys
dgfilter.sys
dgsafe.sys
dhwatchdog.sys
diflt.sys
diskactmon.sys
dkdrv.sys
dkrtwrt.sys
dktlfsmf.sys
dnafsmonitor.sys
docvmonk.sys
docvmonk64.sys
dpmfilter.sys
drbdlock.sys
drivesentryfilterdriver2lite.sys
drsfile.sys
drvhookcsmf.sys
drvhookcsmf_amd64.sys
drwebfwflt.sys
drwebfwft.sys
dsark.sys
dsdriver.sys
dsfemon.sys
dsflt.sys
dsfltfs.sys
dskmn.sys
dtdsel.sys
dtpl.sys
dwprot.sys
dwshield.sys
dwshield64.sys
eamonm.sys
easeflt.sys
easyanticheat.sys
eaw.sys
ecatdriver.sys
edevmon.sys
ednemfsfilter.sys
edrdrv.sys
edrsensor.sys
edsigk.sys
eectrl.sys
eetd32.sys
eetd64.sys
eeyehv.sys
eeyehv64.sys
egambit.sys
egfilterk.sys
egminflt.sys
egnfsflt.sys
ehdrv.sys
elock2fsctldriver.sys
emxdrv2.sys
enigmafilemondriver.sys
enmon.sys
epdrv.sys
epfw.sys
epfwwfp.sys
epicfilter.sys
epklib.sys
epp64.sys
epregflt.sys
eps.sys
epsmn.sys
equ8_helper.sys
eraser.sys
esensor.sys
esprobe.sys
estprmon.sys
estprp.sys
estregmon.sys
estregp.sys
estrkmon.sys
estrkr.sys
eventmon.sys
evmf.sys
evscase.sys
excfs.sys
exprevdriver.sys
failattach.sys
failmount.sys
fam.sys
fangcloud_autolock_driver.sys
fapmonitor.sys
farflt.sys
farwflt.sys
fasdriver
fcnotify.sys
fcontrol.sys
fdrtrace.sys
fekern.sys
fencry.sys
ffcfilt.sys
ffdriver.sys
fildds.sys
filefilter.sys
fileflt.sys
fileguard.sys
filehubagent.sys
filemon.sys
filemonitor.sys
filenamevalidator.sys
filescan.sys
filesharemon.sys
filesightmf.sys
filesystemcbt.sys
filetrace.sys
file_monitor.sys
file_protector.sys
file_tracker.sys
filrdriver.sys
fim.sys
fiometer.sys
fiopolicyfilter.sys
fjgsdis2.sys
fjseparettifilterredirect.sys
flashaccelfs.sys
flightrecorder.sys
fltrs329.sys
flyfs.sys
fmdrive.sys
fmkkc.sys
fmm.sys
fortiaptfilter.sys
fortimon2.sys
fortirmon.sys
fortishield.sys
fpav_rtp.sys
fpepflt.sys
fsafilter.sys
fsatp.sys
fsfilter.sys
fsgk.sys
fshs.sys
fsmon.sys
fsmonitor.sys
fsnk.sys
fsrfilter.sys
fstrace.sys
fsulgk.sys
fsw31rj1.sys
gagsecurity.sys
gbpkm.sys
gcffilter.sys
gddcv.sys
gefcmp.sys
gemma.sys
geprotection.sys
ggc.sys
gibepcore.sys
gkff.sys
gkff64.sys
gkpfcb.sys
gkpfcb64.sys
gofsmf.sys
gpminifilter.sys
groundling32.sys
groundling64.sys
gtkdrv.sys
gumhfilter.sys
gzflt.sys
hafsnk.sys
hbflt.sys
hbfsfltr.sys
hcp_kernel_acq.sys
hdcorrelatefdrv.sys
hdfilemon.sys
hdransomoffdrv.sys
hdrfs.sys
heimdall.sys
hexisfsmonitor.sys
hfileflt.sys
hiofs.sys
hmpalert.sys
hookcentre.sys
hooksys.sys
hpreg.sys
hsmltmon.sys
hsmltwhl.sys
hssfwhl.sys
hvlminifilter.sys
ibr2fsk.sys
iccfileioad.sys
iccfilteraudit.sys
iccfiltersc.sys
icfclientflt.sys
icrlmonitor.sys
iderafilterdriver.sys
ielcp.sys
ieslp.sys
ifs64.sys
ignis.sys
iguard.sys
iiscache.sys
ikfilesec.sys
im.sys
imffilter.sys
imfilter.sys
imgguard.sys
immflex.sys
immunetprotect.sys
immunetselfprotect.sys
inisbdrv64.sys
ino_fltr.sys
intelcas.sys
intmfs.sys
inuse.sys
invprotectdrv.sys
invprotectdrv64.sys
ionmonwdrv.sys
iothorfs.sys
ipcomfltr.sys
ipfilter.sys
iprotect.sys
iridiumswitch.sys
irongatefd.sys
isafekrnl.sys
isafekrnlmon.sys
isafermon
isecureflt.sys
isedrv.sys
isfpdrv.sys
isirmfmon.sys
isregflt.sys
isregflt64.sys
issfltr.sys
issregistry.sys
it2drv.sys
it2reg.sys
ivappmon.sys
iwdmfs.sys
iwhlp.sys
iwhlp2.sys
iwhlpxp.sys
jdppsf.sys
jdppwf.sys
jkppob.sys
jkppok.sys
jkpppf.sys
jkppxk.sys
k7sentry.sys
kavnsi.sys
kawachfsminifilter.sys
kc3.sys
kconv.sys
kernelagent32.sys
kewf.sys
kfac.sys
kfileflt.sys
kisknl.sys
klam.sys
klbg.sys
klboot.sys
kldback.sys
kldlinf.sys
kldtool.sys
klfdefsf.sys
klflt.sys
klgse.sys
klhk.sys
klif.sys
klifaa.sys
klifks.sys
klifsm.sys
klrsps.sys
klsnsr.sys
klupd_klif_arkmon.sys
kmkuflt.sys
kmnwch.sys
kmxagent.sys
kmxfile.sys
kmxsbx.sys
ksfsflt.sys
ktfsfilter.sys
ktsyncfsflt.sys
kubwksp.sys
lafs.sys
lbd.sys
lbprotect.sys
lcgadmon.sys
lcgfile.sys
lcgfilemon.sys
lcmadmon.sys
lcmfile.sys
lcmfilemon.sys
lcmprintmon.sys
ldsecdrv.sys
libwamf.sys
livedrivefilter.sys
llfilter.sys
lmdriver.sys
lnvscenter.sys
locksmith.sys
lragentmf.sys
lrtp.sys
magicbackupmonitor.sys
magicprotect.sys
majoradvapi.sys
marspy.sys
maxcryptmon.sys
maxproc64.sys
maxprotector.sys
mbae64.sys
mbam.sys
mbamchameleon.sys
mbamshuriken.sys
mbamswissarmy.sys
mbamwatchdog.sys
mblmon.sys
mcfilemon32.sys
mcfilemon64.sys
mcstrg.sys
mearwfltdriver.sys
message.sys
mfdriver.sys
mfeaack.sys
mfeaskm.sys
mfeavfk.sys
mfeclnrk.sys
mfeelamk.sys
mfefirek.sys
mfehidk.sys
mfencbdc.sys
mfencfilter.sys
mfencoas.sys
mfencrk.sys
mfeplk.sys
mfewfpk.sys
miniicpt.sys
minispy.sys
minitrc.sys
mlsaff.sys
mmpsy32.sys
mmpsy64.sys
monsterk.sys
mozycorpfilter.sys
mozyenterprisefilter.sys
mozyentfilter.sys
mozyhomefilter.sys
mozynextfilter.sys
mozyoemfilter.sys
mozyprofilter.sys
mpfilter.sys
mpkernel.sys
mpksldrv.sys
mpxmon.sys
mracdrv.sys
mrxgoogle.sys
mscan-rt.sys
msiodrv4.sys
msixpackagingtoolmonitor.sys
msnfsflt.sys
mspy.sys
mssecflt.sys
mtsvcdf.sys
mumdi.sys
mwac.sys
mwatcher.sys
mwfsmfltr.sys
mydlpmf.sys
namechanger.sys
nanoavmf.sys
naswsp.sys
ndgdmk.sys
neokerbyfilter
netaccctrl.sys
netaccctrl64.sys
netguard.sys
netpeeker.sys
ngscan.sys
nlcbhelpi64.sys
nlcbhelpx64.sys
nlcbhelpx86.sys
nlxff.sys
nmlhssrv01.sys
nmpfilter.sys
nntinfo.sys
novashield.sys
nowonmf.sys
npetw.sys
nprosec.sys
npxgd.sys
npxgd64.sys
nravwka.sys
nrcomgrdka.sys
nrcomgrdki.sys
nregsec.sys
nrpmonka.sys
nrpmonki.sys
nsminflt.sys
nsminflt64.sys
ntest.sys
ntfsf.sys
ntguard.sys
ntps_fa.sys
nullfilter.sys
nvcmflt.sys
nvmon.sys
nwedriver.sys
nxfsmon.sys
nxrmflt.sys
oadevice.sys
oavfm.sys
oczminifilter.sys
odfsfilter.sys
odfsfimfilter.sys
odfstokenfilter.sys
offsm.sys
omfltlh.sys
osiris.sys
ospfile_mini.sys
ospmon.sys
parity.sys
passthrough.sys
path8flt.sys
pavdrv.sys
pcpifd.sys
pctcore.sys
pctcore64.sys
pdgenfam.sys
pecfilter.sys
perfectworldanticheatsys.sys
pervac.sys
pfkrnl.sys
pfracdrv.sys
pgpfs.sys
pgpwdefs.sys
phantomd.sys
phdcbtdrv.sys
pkgfilter.sys
pkticpt.sys
plgfltr.sys
plpoffdrv.sys
pointguardvista64f.sys
pointguardvistaf.sys
pointguardvistar32.sys
pointguardvistar64.sys
procmon11.sys
proggerdriver.sys
psacfileaccessfilter.sys
pscff.sys
psgdflt.sys
psgfoctrl.sys
psinfile.sys
psinproc.sys
psisolator.sys
pwipf6.sys
pwprotect.sys
pzdrvxp.sys
qdocumentref.sys
qfapflt.sys
qfilter.sys
qfimdvr.sys
qfmon.sys
qminspec.sys
qmon.sys
qqprotect.sys
qqprotectx64.sys
qqsysmon.sys
qqsysmonx64.sys
qutmdrv.sys
ranpodfs.sys
ransomdefensexxx.sys
ransomdetect.sys
reaqtor.sys
redlight.sys
regguard.sys
reghook.sys
regmonex.sys
repdrv.sys
repmon.sys
revefltmgr.sys
reveprocprotection.sys
revonetdriver.sys
rflog.sys
rgnt.sys
rmdiskmon.sys
rmphvmonitor.sys
rpwatcher.sys
rrmon32.sys
rrmon64.sys
rsfdrv.sys
rsflt.sys
rspcrtw.sys
rsrtw.sys
rswctrl.sys
rswmon.sys
rtologon.sys
rtw.sys
ruaff.sys
rubrikfileaudit.sys
ruidiskfs.sys
ruieye.sys
ruifileaccess.sys
ruimachine.sys
ruiminispy.sys
rvsavd.sys
rvsmon.sys
rw7fsflt.sys
rwchangedrv.sys
ryfilter.sys
ryguard.sys
safe-agent.sys
safsfilter.sys
sagntflt.sys
sahara.sys
sakfile.sys
sakmfile.sys
samflt.sys
samsungrapidfsfltr.sys
sanddriver.sys
santa.sys
sascan.sys
savant.sys
savonaccess.sys
scaegis.sys
scauthfsflt.sys
scauthiodrv.sys
scensemon.sys
scfltr.sys
scifsflt.sys
sciptflt.sys
sconnect.sys
scred.sys
sdactmon.sys
sddrvldr.sys
sdvfilter.sys
se46filter.sys
secdodriver.sys
secone_filemon10.sys
secone_proc10.sys
secone_reg10.sys
secone_usb.sys
secrmm.sys
secufile.sys
secure_os.sys
secure_os_mf.sys
securofsd_x64.sys
sefo.sys
segf.sys
segiraflt.sys
segmd.sys
segmp.sys
sentinelmonitor.sys
serdr.sys
serfs.sys
sfac.sys
sfavflt.sys
sfdfilter.sys
sfpmonitor.sys
sgresflt.sys
shdlpmedia.sys
shdlpsf.sys
sheedantivirusfilterdriver.sys
sheedselfprotection.sys
shldflt.sys
si32_file.sys
si64_file.sys
sieflt.sys
simrep.sys
sisipsfilefilter
sk.sys
skyamdrv.sys
skyrgdrv.sys
skywpdrv.sys
slb_guard.sys
sld.sys
smbresilfilter.sys
smdrvnt.sys
sndacs.sys
snexequota.sys
snilog.sys
snimg.sys
snscore.sys
snsrflt.sys
sodatpfl.sys
softfilterxxx.sys
soidriver.sys
solitkm.sys
sonar.sys
sophosdt2.sys
sophosed.sys
sophosntplwf.sys
sophossupport.sys
spbbcdrv.sys
spellmon.sys
spider3g.sys
spiderg3.sys
spiminifilter.sys
spotlight.sys
sprtdrv.sys
sqlsafefilterdriver.sys
srminifilterdrv.sys
srtsp.sys
srtsp64.sys
srtspit.sys
ssfmonm.sys
ssrfsf.sys
ssvhook.sys
stcvsm.sys
stegoprotect.sys
stest.sys
stflt.sys
stkrnl64.sys
storagedrv.sys
strapvista.sys
strapvista64.sys
svcbt.sys
swcommfltr.sys
swfsfltr.sys
swfsfltrv2.sys
swin.sys
symafr.sys
symefa.sys
symefa64.sys
symefasi.sys
symevent.sys
symevent64x86.sys
symevnt.sys
symevnt32.sys
symhsm.sys
symrg.sys
sysdiag.sys
sysmon.sys
sysmondrv.sys
sysplant.sys
szardrv.sys
szdfmdrv.sys
szdfmdrv_usb.sys
szedrdrv.sys
szpcmdrv.sys
taniumrecorderdrv.sys
taobserveflt.sys
tbfsfilt.sys
tbmninifilter.sys
tbrdrv.sys
tdevflt.sys
tedrdrv.sys
tenrsafe2.sys
tesmon.sys
tesxnginx.sys
tesxporter.sys
tffregnt.sys
tfsflt.sys
tgfsmf.sys
thetta.sys
thfilter.sys
threatstackfim.sys
tkdac2k.sys
tkdacxp.sys
tkdacxp64.sys
tkfsavxp.sys
tkfsavxp64.sys
tkfsft.sys
tkfsft64.sys
tkpcftcb.sys
tkpcftcb64.sys
tkpl2k.sys
tkpl2k64.sys
tksp2k.sys
tkspxp.sys
tkspxp64.sys
tmactmon.sys
tmcomm.sys
tmesflt.sys
tmevtmgr.sys
tmeyes.sys
tmfsdrv2.sys
tmkmsnsr.sys
tmnciesc.sys
tmpreflt.sys
tmumh.sys
tmums.sys
tmusa.sys
tmxpflt.sys
topdogfsfilt.sys
trace.sys
trfsfilter.sys
tritiumfltr.sys
trpmnflt.sys
trufos.sys
trustededgeffd.sys
tsifilemon.sys
tss.sys
tstfilter.sys
tstfsredir.sys
tstregredir.sys
tsyscare.sys
tvdriver.sys
tvfiltr.sys
tvmfltr.sys
tvptfile.sys
tvspfltr.sys
twbdcfilter.sys
txfilefilter.sys
txregmon.sys
uamflt.sys
ucafltdriver.sys
ufdfilter.sys
uncheater.sys
upguardrealtime.sys
usbl_ifsfltr.sys
usbpdh.sys
usbtest.sys
uvmcifsf.sys
uwfreg.sys
uwfs.sys
v3flt2k.sys
v3flu2k.sys
v3ift2k.sys
v3iftmnt.sys
v3mifint.sys
varpffmon.sys
vast.sys
vcdriv.sys
vchle.sys
vcmfilter.sys
vcreg.sys
veeamfct.sys
vfdrv.sys
vfilefilter.sys
vfpd.sys
vfsenc.sys
vhddelta.sys
vhdtrack.sys
vidderfs.sys
vintmfs.sys
virtfile.sys
virtualagent.sys
vk_fsf.sys
vlflt.sys
vmwvvpfsd.sys
vollock.sys
vpdrvnt.sys
vradfil2.sys
vraptdef.sys
vraptflt.sys
vrarnflt.sys
vrbbdflt.sys
vrexpdrv.sys
vrfsftm.sys
vrfsftmx.sys
vrnsfilter.sys
vrsdam.sys
vrsdcore.sys
vrsdetri.sys
vrsdetrix.sys
vrsdfmx.sys
vrvbrfsfilter.sys
vsepflt.sys
vsscanner.sys
vtsysflt.sys
vxfsrep.sys
wats_se.sys
wbfilter.sys
wcsdriver.sys
wdcfilter.sys
wdfilter.sys
wdocsafe.sys
wfp_mrt.sys
wgfile.sys
whiteshield.sys
windbdrv.sys
windd.sys
winfladrv.sys
winflahdrv.sys
winfldrv.sys
winfpdrv.sys
winload.sys
winteonminifilter.sys
wiper.sys
wlminisecmod.sys
wntgpdrv.sys
wraekernel.sys
wrcore.sys
wrcore.x64.sys
wrdwizfileprot.sys
wrdwizregprot.sys
wrdwizscanner.sys
wrdwizsecure64.sys
wrkrn.sys
wrpfv.sys
wsafefilter.sys
wscm.sys
xcpl.sys
xendowflt.sys
xfsgk.sys
xhunter1.sys
xhunter64.sys
xiaobaifs.sys
xiaobaifsr.sys
xkfsfd.sys
xoiv8x64.sys
xomfcbt8x64.sys
yahoostorage.sys
yfsd.sys
yfsd2.sys
yfsdr.sys
yfsrd.sys
zampit_ml.sys
zesfsmf.sys
zqfilter.sys
zsfprt.sys
zwasatom.sys
zwpxesvr.sys
zxfsfilt.sys
zyfm.sys
zzpensys.sys
Further reading
For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog: https://aka.ms/threatintelblog.
To get notified about new publications and to join discussions on social media, follow us on Twitter at https://twitter.com/MsftSecIntel.
On July 11, 2023, Microsoft published two blogs detailing a malicious campaign by a threat actor tracked as Storm-0558 that targeted customer email that we’ve detected and mitigated: Microsoft Security Response Center and Microsoft on the Issues. As we continue our investigation into this incident and deploy defense in depth measures to harden all systems involved, we’re providing this deeper analysis of the observed actor techniques for obtaining unauthorized access to email data, tools, and unique infrastructure characteristics.
As described in more detail in our July 11 blogs, Storm-0558 is a China-based threat actor with espionage objectives. Beginning May 15, 2023, Storm-0558 used forged authentication tokens to access user email from approximately 25 organizations, including government agencies and related consumer accounts in the public cloud. No other environment was impacted. Microsoft has successfully blocked this campaign from Storm-0558. As with any observed nation-state actor activity, Microsoft has directly notified targeted or compromised customers, providing them with important information needed to secure their environments.
Since identification of this malicious campaign on June 16, 2023, Microsoft has identified the root cause, established durable tracking of the campaign, disrupted malicious activities, hardened the environment, notified every impacted customer, and coordinated with multiple government entities. We continue to investigate and monitor the situation and will take additional steps to protect customers.
Actor overview
Microsoft Threat Intelligence assesses with moderate confidence that Storm-0558 is a China-based threat actor with activities and methods consistent with espionage objectives. While we have discovered some minimal overlaps with other Chinese groups such as Violet Typhoon (ZIRCONIUM, APT31), we maintain high confidence that Storm-0558 operates as its own distinct group.
Figure 1 shows Storm-0558 working patterns from April to July 2023; the actor’s core working hours are consistent with working hours in China, Monday through Friday from 12:00 AM UTC (8:00 AM China Standard time) through 09:00 AM UTC (5:00 PM China Standard Time).
Figure 1. Heatmap of observed Stom-0558 activity by day of week and hour (UTC).
In past activity observed by Microsoft, Storm-0558 has primarily targeted US and European diplomatic, economic, and legislative governing bodies, and individuals connected to Taiwan and Uyghur geopolitical interests.
Historically, this threat actor has displayed an interest in targeting media companies, think tanks, and telecommunications equipment and service providers. The objective of most Storm-0558 campaigns is to obtain unauthorized access to email accounts belonging to employees of targeted organizations. Storm-0558 pursues this objective through credential harvesting, phishing campaigns, and OAuth token attacks. This threat actor has displayed an interest in OAuth applications, token theft, and token replay against Microsoft accounts since at least August 2021. Storm-0558 operates with a high degree of technical tradecraft and operational security. The actors are keenly aware of the target’s environment, logging policies, authentication requirements, policies, and procedures. Storm-0558’s tooling and reconnaissance activity suggests the actor is technically adept, well resourced, and has an in-depth understanding of many authentication techniques and applications.
In the past, Microsoft has observed Storm-0558 obtain credentials for initial access through phishing campaigns. The actor has also exploited vulnerabilities in public-facing applications to gain initial access to victim networks. These exploits typically result in web shells, including China Chopper, being deployed on compromised servers. One of the most prevalent malware families used by Storm-0558 is a shared tool tracked by Microsoft as Cigril. This family exists in several variants and is launched using dynamic-link library (DLL) search order hijacking.
After gaining access to a compromised system, Storm-0558 accesses credentials from a variety of sources, including the LSASS process memory and Security Account Manager (SAM) registry hive. Microsoft assesses that once Storm-0558 has access to the desired user credentials, the actor signs into the compromised user’s cloud email account with the valid account credentials. The actor then collects information from the email account over the web service.
Initial discovery and analysis of current activity
On June 16, 2023, Microsoft was notified by a customer of anomalous Exchange Online data access. Microsoft analysis attributed the activity to Storm-0558 based on established prior TTPs. We determined that Storm-0558 was accessing the customer’s Exchange Online data using Outlook Web Access (OWA). Microsoft’s investigative workflow initially assumed the actor was stealing correctly issued Azure Active Directory (Azure AD) tokens, most probably using malware on infected customer devices. Microsoft analysts later determined that the actor’s access was utilizing Exchange Online authentication artifacts, which are typically derived from Azure AD authentication tokens (Azure AD tokens). Further in-depth analysis over the next several days led Microsoft analysts to assess that the internal Exchange Online authentication artifacts did not correspond to Azure AD tokens in Microsoft logs.
Microsoft analysts began investigating the possibility that the actor was forging authentication tokens using an acquired Azure AD enterprise signing key. In-depth analysis of the Exchange Online activity discovered that in fact the actor was forging Azure AD tokens using an acquired Microsoft account (MSA) consumer signing key. This was made possible by a validation error in Microsoft code. The use of an incorrect key to sign the requests allowed our investigation teams to see all actor access requests which followed this pattern across both our enterprise and consumer systems. Use of the incorrect key to sign this scope of assertions was an obvious indicator of the actor activity as no Microsoft system signs tokens in this way. Use of acquired signing material to forge authentication tokens to access customer Exchange Online data differs from previously observed Storm-0558 activity. Microsoft’s investigations have not detected any other use of this pattern by other actors and Microsoft has taken steps to block related abuse.
Actor techniques
Token forgery
Authentication tokens are used to validate the identity of entities requesting access to resources – in this case, email. These tokens are issued to the requesting entity (such as a user’s browser) by identity providers like Azure AD. To prove authenticity, the identity provider signs the token using a private signing key. The relying party validates the token presented by the requesting entity by using a public validation key. Any request whose signature is correctly validated by the published public validation key will be trusted by the relying party. An actor that can acquire a private signing key can then create falsified tokens with valid signatures that will be accepted by relying parties. This is called token forgery.
Storm-0558 acquired an inactive MSA consumer signing key and used it to forge authentication tokens for Azure AD enterprise and MSA consumer to access OWA and Outlook.com. All MSA keys active prior to the incident – including the actor-acquired MSA signing key – have been invalidated. Azure AD keys were not impacted. The method by which the actor acquired the key is a matter of ongoing investigation. Though the key was intended only for MSA accounts, a validation issue allowed this key to be trusted for signing Azure AD tokens. This issue has been corrected.
As part of defense in depth, we continuously update our systems. We have substantially hardened key issuance systems since the acquired MSA key was initially issued. This includes increased isolation of the systems, refined monitoring of system activity, and moving to the hardened key store used for our enterprise systems. We have revoked all previously active keys and issued new keys using these updated systems. Our active investigation indicates these hardening and isolation improvements disrupt the mechanisms we believe the actor could have used to acquire MSA signing keys. No key-related actor activity has been observed since Microsoft invalidated the actor-acquired MSA signing key. Further, we have seen Storm-0558 transition to other techniques, which indicates that the actor is not able to utilize or access any signing keys. We continue to explore other ways the key may have been acquired and add additional defense in depth measures.
Identity techniques for access
Once authenticated through a legitimate client flow leveraging the forged token, the threat actor accessed the OWA API to retrieve a token for Exchange Online from the GetAccessTokenForResource API used by OWA. The actor was able to obtain new access tokens by presenting one previously issued from this API due to a design flaw. This flaw in the GetAccessTokenForResourceAPI has since been fixed to only accept tokens issued from Azure AD or MSA respectively. The actor used these tokens to retrieve mail messages from the OWA API.
Actor tooling
Microsoft Threat Intelligence routinely identifies threat actor capabilities and leverages file intelligence to facilitate our protection of Microsoft customers. During this investigation, we identified several distinct Storm-0558 capabilities that facilitate the threat actor’s intrusion techniques. The capabilities described in this section are not expected to be present in the victim environment.
Storm-0558 uses a collection of PowerShell and Python scripts to perform REST API calls against the OWA Exchange Store service. For example, Storm-0558 has the capability to use minted access tokens to extract email data such as:
Download emails
Download attachments
Locate and download conversations
Get email folder information
The generated web requests can be routed through a Tor proxy or several hardcoded SOCKS5 proxy servers. The threat actor was observed using several User-Agents when issuing web requests, for example:
Client=REST;Client=RESTSystem;;
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/106.0.0.0 Safari/537.36 Edg/106.0.1370.52
The scripts contain highly sensitive hardcoded information such as bearer access tokens and email data, which the threat actor uses to perform the OWA API calls. The threat actor has the capability to refresh the access token for use in subsequent OWA commands.
Figure 2. Python code snippet of the token refresh functionality used by the threat actor.Figure 3. PowerShell code snippet of OWA REST API call to GetConversationItems.
Actor infrastructure
During significant portions of Storm-0558’s malicious activities, the threat actor leveraged dedicated infrastructure running the SoftEther proxy software. Proxy infrastructure complicates detection and attribution of Storm-0558 activities. During our response, Microsoft Threat Intelligence identified a unique method of profiling this proxy infrastructure and correlated with behavioral characteristics of the actor intrusion techniques. Our profile was based on the following facets:
Hosts operating as part of this network present a JARM fingerprint consistent with SoftEther VPN: 06d06d07d06d06d06c42d42d000000cdb95e27fd8f9fee4a2bec829b889b8b.
Presented x509 certificate has expiration date of December 31, 2037.
Subject information within the x509 certificate does not contain “softether”.
Over the course of the campaign, the IPs listed in the table below were used during the corresponding timeframes.
IP address
First seen
Last seen
Description
51.89.156[.]153
3/9/2023
7/10/2023
SoftEther proxy
176.31.90[.]129
3/28/2023
6/29/2023
SoftEther proxy
137.74.181[.]100
3/31/2023
7/11/2023
SoftEther proxy
193.36.119[.]45
4/19/2023
7/7/2023
SoftEther proxy
185.158.248[.]159
4/24/2023
7/6/2023
SoftEther proxy
131.153.78[.]188
5/6/2023
6/29/2023
SoftEther proxy
37.143.130[.]146
5/12/2023
5/19/2023
SoftEther proxy
146.70.157[.]45
5/12/2023
6/8/2023
SoftEther proxy
185.195.200[.]39
5/15/2023
6/29/2023
SoftEther proxy
185.38.142[.]229
5/15/2023
7/12/2023
SoftEther proxy
146.70.121[.]44
5/17/2023
6/29/2023
SoftEther proxy
31.42.177[.]181
5/22/2023
5/23/2023
SoftEther proxy
185.51.134[.]52
6/7/2023
7/11/2023
SoftEther proxy
173.44.226[.]70
6/9/2023
7/11/2023
SoftEther proxy
45.14.227[.]233
6/12/2023
6/26/2023
SoftEther proxy
185.236.231[.]109
6/12/2023
7/3/2023
SoftEther proxy
178.73.220[.]149
6/16/2023
7/12/2023
SoftEther proxy
45.14.227[.]212
6/19/2023
6/29/2023
SoftEther proxy
91.222.173[.]225
6/20/2023
7/1/2023
SoftEther proxy
146.70.35[.]168
6/22/2023
6/29/2023
SoftEther proxy
146.70.157[.]213
6/26/2023
6/30/2023
SoftEther proxy
31.42.177[.]201
6/27/2023
6/29/2023
SoftEther proxy
5.252.176[.]8
7/1/2023
7/1/2023
SoftEther proxy
80.85.158[.]215
7/1/2023
7/9/2023
SoftEther proxy
193.149.129[.]88
7/2/2023
7/12/2023
SoftEther proxy
5.252.178[.]68
7/3/2023
7/11/2023
SoftEther proxy
116.202.251[.]8
7/4/2023
7/7/2023
SoftEther proxy
185.158.248[.]93
6/25/2023
06/26/2023
SoftEther proxy
20.108.240[.]252
6/25/2023
7/5/2023
SoftEther proxy
146.70.135[.]182
5/18/2023
6/22/2023
SoftEther proxy
As early as May 15, 2023, Storm-0558 shifted to using a separate series of dedicated infrastructure servers specifically for token replay and interaction with Microsoft services. It is likely that the dedicated infrastructure and supporting services configured on this infrastructure offered a more efficient manner of facilitating the actor’s activities. The dedicated infrastructure would host an actor-developed web panel that presented an authentication page at URI /#/login. The observed sign-in pages had one of two SHA-1 hashes: 80d315c21fc13365bba5b4d56357136e84ecb2d4 and 931e27b6f1a99edb96860f840eb7ef201f6c68ec.
Figure 4. Token web panel sign-in page with SHA-1 hashes.
As part of the intelligence-driven response to this campaign, and in support of tracking, analyzing, and disrupting actor activity, analytics were developed to proactively track the dedicated infrastructure. Through this tracking, we identified the following dedicated infrastructure.
IP address
First seen
Last seen
Description
195.26.87[.]219
5/15/2023
6/25/2023
Token web panel
185.236.228[.]183
5/24/2023
6/11/2023
Token web panel
85.239.63[.]160
6/7/2023
6/11/2023
Token web panel
193.105.134[.]58
6/24/2023
6/25/2023
Token web panel
146.0.74[.]16
6/28/2023
7/4/2023
Token web panel
91.231.186[.]226
6/29/2023
7/4/2023
Token web panel
91.222.174[.]41
6/29/2023
7/3/2023
Token web panel
185.38.142[.]249
6/29/2023
7/2/2023
Token web panel
The last observed dedicated token replay infrastructure associated with this activity was stood down on July 4, 2023, roughly one day following the coordinated mitigation conducted by Microsoft.
Post-compromise activity
Our telemetry and investigations indicate that post-compromise activity was limited to email access and exfiltration for targeted users.
Mitigation and hardening
No customer action is required to mitigate the token forgery technique or validation error in OWA or Outlook.com. Microsoft has mitigated this issue on customers’ behalf as follows:
On June 26, OWA stopped accepting tokens issued from GetAccessTokensForResource for renewal, which mitigated the token renewal being abused.
On June 27, Microsoft blocked the usage of tokens signed with the acquired MSA key in OWA preventing further threat actor enterprise mail activity.
On June 29, Microsoft completed replacement of the key to prevent the threat actor from using it to forge tokens. Microsoft revoked all MSA signing which were valid at the time of the incident, including the actor-acquired MSA key. The new MSA signing keys are issued in substantially updated systems which benefit from hardening not present at issuance of the actor-acquired MSA key:
Microsoft has increased the isolation of these systems from corporate environments, applications, and users.Microsoft has refined monitoring of all systems related to key activity, and increased automated alerting related to this monitoring.
Microsoft has moved the MSA signing keys to the key store used for our enterprise systems.
On July 3, Microsoft blocked usage of the key for all impacted consumer customers to prevent use of previously-issued tokens.
Ongoing monitoring indicates that all actor activity related to this incident has been blocked. Microsoft will continue to monitor Storm-0558 activity and implement protections for our customers.
Recommendations
Microsoft has mitigated this activity on our customers’ behalf for Microsoft services. No customer action is required to prevent threat actors from using the techniques described above to access Exchange Online and Outlook.com.
Indicators of compromise
Indicator
Type
First seen
Last seen
Description
d4b4cccda9228624656bff33d8110955779632aa
Thumbprint
Thumbprint of acquired signing key
195.26.87[.]219
IPv4
5/15/2023
6/25/2023
Token web panel
185.236.228[.]183
IPv4
5/24/2023
6/11/2023
Token web panel
85.239.63[.]160
IPv4
6/7/2023
6/11/2023
Token web panel
193.105.134[.]58
IPv4
6/24/2023
6/25/2023
Token web panel
146.0.74[.]16
IPv4
6/28/2023
7/4/2023
Token web panel
91.231.186[.]226
IPv4
6/29/2023
7/4/2023
Token web panel
91.222.174[.]41
IPv4
6/29/2023
7/3/2023
Token web panel
185.38.142[.]249
IPv4
6/29/2023
7/2/2023
Token web panel
51.89.156[.]153
IPv4
3/9/2023
7/10/2023
SoftEther proxy
176.31.90[.]129
IPv4
3/28/2023
6/29/2023
SoftEther proxy
137.74.181[.]100
IPv4
3/31/2023
7/11/2023
SoftEther proxy
193.36.119[.]45
IPv4
4/19/2023
7/7/2023
SoftEther proxy
185.158.248[.]159
IPv4
4/24/2023
7/6/2023
SoftEther proxy
131.153.78[.]188
IPv4
5/6/2023
6/29/2023
SoftEther proxy
37.143.130[.]146
IPv4
5/12/2023
5/19/2023
SoftEther proxy
146.70.157[.]45
IPv4
5/12/2023
6/8/2023
SoftEther proxy
185.195.200[.]39
IPv4
5/15/2023
6/29/2023
SoftEther proxy
185.38.142[.]229
IPv4
5/15/2023
7/12/2023
SoftEther proxy
146.70.121[.]44
IPv4
5/17/2023
6/29/2023
SoftEther proxy
31.42.177[.]181
IPv4
5/22/2023
5/23/2023
SoftEther proxy
185.51.134[.]52
IPv4
6/7/2023
7/11/2023
SoftEther proxy
173.44.226[.]70
IPv4
6/9/2023
7/11/2023
SoftEther proxy
45.14.227[.]233
IPv4
6/12/2023
6/26/2023
SoftEther proxy
185.236.231[.]109
IPv4
6/12/2023
7/3/2023
SoftEther proxy
178.73.220[.]149
IPv4
6/16/2023
7/12/2023
SoftEther proxy
45.14.227[.]212
IPv4
6/19/2023
6/29/2023
SoftEther proxy
91.222.173[.]225
IPv4
6/20/2023
7/1/2023
SoftEther proxy
146.70.35[.]168
IPv4
6/22/2023
6/29/2023
SoftEther proxy
146.70.157[.]213
IPv4
6/26/2023
6/30/2023
SoftEther proxy
31.42.177[.]201
IPv4
6/27/2023
6/29/2023
SoftEther proxy
5.252.176[.]8
IPv4
7/1/2023
7/1/2023
SoftEther proxy
80.85.158[.]215
IPv4
7/1/2023
7/9/2023
SoftEther proxy
193.149.129[.]88
IPv4
7/2/2023
7/12/2023
SoftEther proxy
5.252.178[.]68
IPv4
7/3/2023
7/11/2023
SoftEther proxy
116.202.251[.]8
IPv4
7/4/2023
7/7/2023
SoftEther proxy
Further reading
For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog: https://aka.ms/threatintelblog.
To get notified about new publications and to join discussions on social media, follow us on Twitter at https://twitter.com/MsftSecIntel.
Small businesses are often targeted by cybercriminals due to their lack of resources and security measures. Protecting your business from cyber threats is crucial to avoid data breaches and financial losses.
Why is cyber security so important for small businesses?
Small businesses are particularly in danger of cyberattacks, which can result in financial loss, data breaches, and damage to IT equipment. To protect your business, it’s important to implement strong cybersecurity measures.
Here are some tips to help you get started:
One important aspect of data protection and cybersecurity for small businesses is controlling access to customer lists. It’s important to limit access to this sensitive information to only those employees who need it to perform their job duties. Additionally, implementing strong password policies and regularly updating software and security measures can help prevent unauthorized access and protect against cyber attacks. Regular employee training on cybersecurity best practices can also help ensure that everyone in the organization is aware of potential threats and knows how to respond in the event of a breach.
When it comes to protecting customer credit card information in small businesses, there are a few key tips to keep in mind. First and foremost, it’s important to use secure payment processing systems that encrypt sensitive data. Additionally, it’s crucial to regularly update software and security measures to stay ahead of potential threats. Employee training and education on cybersecurity best practices can also go a long way in preventing data breaches. Finally, having a plan in place for responding to a breach can help minimize the damage and protect both your business and your customers.
Small businesses are often exposed to cyber attacks, making data protection and cybersecurity crucial. One area of particular concern is your company’s banking details. To protect this sensitive information, consider implementing strong passwords, two-factor authentication, and regular monitoring of your accounts. Additionally, educate your employees on safe online practices and limit access to financial information to only those who need it. Regularly backing up your data and investing in cybersecurity software can also help prevent data breaches.
Small businesses are often at high risk of cyber attacks due to their limited resources and lack of expertise in cybersecurity. To protect sensitive data, it is important to implement strong passwords, regularly update software and antivirus programs, and limit access to confidential information.
It is also important to have a plan in place in case of a security breach, including steps to contain the breach and notify affected parties. By taking these steps, small businesses can better protect themselves from cyber threats and ensure the safety of their data.
Tips for protecting your small business from cyber threats and data breaches are crucial in today’s digital age. One of the most important steps is to educate your employees on cybersecurity best practices, such as using strong passwords and avoiding suspicious emails or links.
It’s also important to regularly update your software and systems to ensure they are secure and protected against the latest threats. Additionally, implementing multi-factor authentication and encrypting sensitive data can add an extra layer of protection. Finally, having a plan in place for responding to a cyber-attack or data breach can help minimize the damage and get your business back on track as quickly as possible.
Small businesses are attackable to cyber-attacks and data breaches, which can have devastating consequences. To protect your business, it’s important to implement strong cybersecurity measures. This includes using strong passwords, regularly updating software and systems, and training employees on how to identify and avoid phishing scams.
It’s also important to have a data backup plan in place and to regularly test your security measures to ensure they are effective. By taking these steps, you can help protect your business from cyber threats and safeguard your valuable data.
To protect against cyber threats, it’s important to implement strong data protection and cybersecurity measures. This can include regularly updating software and passwords, using firewalls and antivirus software, and providing employee training on safe online practices. Additionally, it’s important to have a plan in place for responding to a cyber attack, including backing up data and having a designated point person for handling the situation.
In today’s digital age, small businesses must prioritize data protection and cybersecurity to safeguard their operations and reputation. With the rise of remote work and cloud-based technology, businesses are more vulnerable to cyber attacks than ever before. To mitigate these risks, it’s crucial to implement strong security measures for online meetings, advertising, transactions, and communication with customers and suppliers. By prioritizing cybersecurity, small businesses can protect their data and prevent unauthorized access or breaches.
Here are 8 essential tips for data protection and cybersecurity in small businesses.
1. Train Your Employees on Cybersecurity Best Practices
Your employees are the first line of defense against cyber threats. It’s important to train them on cybersecurity best practices to ensure they understand the risks and how to prevent them. This includes creating strong passwords, avoiding suspicious emails and links, and regularly updating software and security systems. Consider providing regular training sessions and resources to keep your employees informed and prepared.
2. Use Strong Passwords and Two-Factor Authentication
One of the most basic yet effective ways to protect your business from cyber threats is to use strong passwords and two-factor authentication. Encourage your employees to use complex passwords that include a mix of letters, numbers, and symbols, and to avoid using the same password for multiple accounts. Two-factor authentication adds an extra layer of security by requiring a second form of verification, such as a code sent to a mobile device, before granting access to an account. This can help prevent unauthorized access even if a password is compromised.
3. Keep Your Software and Systems Up to Date
One of the easiest ways for cybercriminals to gain access to your business’s data is through outdated software and systems. Hackers are constantly looking for vulnerabilities in software and operating systems, and if they find one, they can exploit it to gain access to your data. To prevent this, make sure all software and systems are kept up-to-date with the latest security patches and updates. This includes not only your computers and servers but also any mobile devices and other connected devices used in your business. Set up automatic updates whenever possible to ensure that you don’t miss any critical security updates.
4. Use Antivirus and Anti-Malware Software
Antivirus and anti-malware software are essential tools for protecting your small business from cyber threats. These programs can detect and remove malicious software, such as viruses, spyware, and ransomware before they can cause damage to your systems or steal your data. Make sure to install reputable antivirus and anti-malware software on all devices used in your business, including computers, servers, and mobile devices. Keep the software up-to-date and run regular scans to ensure that your systems are free from malware.
5. Backup Your Data Regularly
One of the most important steps you can take to protect your small business from data loss is to back up your data regularly. This means creating copies of your important files and storing them in a secure location, such as an external hard drive or cloud storage service. In the event of a cyber-attack or other disaster, having a backup of your data can help you quickly recover and minimize the impact on your business. Make sure to test your backups regularly to ensure that they are working properly and that you can restore your data if needed.
6. Carry out a risk assessment
Small businesses are especially in peril of cyber attacks, making it crucial to prioritize data protection and cybersecurity. One important step is to assess potential risks that could compromise your company’s networks, systems, and information. By identifying and analyzing possible threats, you can develop a plan to address security gaps and protect your business from harm.
For Small businesses making data protection and cybersecurity is a crucial part. To start, conduct a thorough risk assessment to identify where and how your data is stored, who has access to it, and potential threats. If you use cloud storage, consult with your provider to assess risks. Determine the potential impact of breaches and establish risk levels for different events. By taking these steps, you can better protect your business from cyber threats
7. Limit access to sensitive data
One effective strategy is to limit access to critical data to only those who need it. This reduces the risk of a data breach and makes it harder for malicious insiders to gain unauthorized access. To ensure accountability and clarity, create a plan that outlines who has access to what information and what their roles and responsibilities are. By taking these steps, you can help safeguard your business against cyber threats.
8. Use a firewall
For Small businesses, it’s important to protect the system from cyber attacks by making data protection and reducing cybersecurity risk. One effective measure is implementing a firewall, which not only protects hardware but also software. By blocking or deterring viruses from entering the network, a firewall provides an added layer of security. It’s important to note that a firewall differs from an antivirus, which targets software affected by a virus that has already infiltrated the system.
Small businesses can take steps to protect their data and ensure cybersecurity. One important step is to install a firewall and keep it updated with the latest software or firmware. Regularly checking for updates can help prevent potential security breaches.
Conclusion
Small businesses are particularly vulnerable to cyber attacks, so it’s important to take steps to protect your data. One key tip is to be cautious when granting access to your systems, especially to partners or suppliers. Before granting access, make sure they have similar cybersecurity practices in place. Don’t hesitate to ask for proof or to conduct a security audit to ensure your data is safe.
By: Ieriz Nicolle Gonzalez, Katherine Casona, Sarah Pearl Camiling July 07, 2023
We analyze the technical details of a new ransomware family named Big Head. In this entry, we discuss the Big Head ransomware’s similarities and distinct markers that add more technical details to initial reports on the ransomware.
Reports of a newransomware family and its variant named Big Head emerged in May, with at least two variants of this family being documented. Upon closer examination, we discovered that both strains shared a common contact email in their ransom notes, leading us to suspect that the two different variants originated from the same malware developer. Looking into these variants further, we uncovered a significant number of versions of this malware. In this entry, we go deeper into the routines of these variants, their similarities and differences, and the potential impact of these infections when abused for attacks.
Analysis
In this section, we go expound on the three samples of Big Head we found, as well as their distinct functions and routines. While we continue to investigate and track this threat, we also highly suspect that all three samples of the Big Head ransomware are distributed via malvertisement as fake Windows updates and fake Word installers.
First sample
Figure 1. The infection routine of the first Big Head ransomware sample
The first sample of Big Head ransomware (SHA256: 6d27c1b457a34ce9edfb4060d9e04eb44d021a7b03223ee72ca569c8c4215438, detected by Trend Micro as Ransom.MSIL.EGOGEN.THEBBBC) featured a .NET compiled binary file. This binary checks the mutex name 8bikfjjD4JpkkAqrz using CreateMutex and terminates itself if the mutex name is found.
Figure 2. Calling CreateMutex functionFigure 3. MTX value “8bikfjjD4JpkkAqrz”
The sample also has a list of configurations containing details related to the installation process. It specifies various actions such as creating a registry key, checking the existence of a file and overwriting it if necessary, setting system file attributes, and creating an autorun registry entry. These configuration settings are separated by the pipe symbol “|” and are accompanied by corresponding strings that define the specific behavior associated with each action.
Figure 4. List of configurations
The format that the malware adheres to in terms of its behavior upon installation is as follows:
Additionally, we noted the presence of three resources that contained data resembling executable files with the “*.exe” extension:
1.exe drops a copy of itself for propagation. This is a piece of ransomware that checks for the extension “.r3d” before encrypting and appending the “.poop” extension.
Archive.exe drops a file named teleratserver.exe, a Telegram bot responsible for establishing communication with the threat actor’s chatbot ID.
Xarch.exe drops a file named BXIuSsB.exe, a piece of ransomware that encrypts files and encodes file names to Base64. It also displays a fake Windows update to deceive the victim into thinking that the malicious activity is a legitimate process.
These binaries are encrypted, rendering their contents inaccessible without the appropriate decryption mechanism.
Figure 5. Three resources found in the main sampleFigure 6. The encrypted content of one of the files located within the resource section (“1.exe”)
To extract the three binaries from the resources, the malware employs AES decryption with the electronic codebook (ECB) mode. This decryption process requires an initialization vector (IV) for proper decryption.
It is also noteworthy that the decryption key used is derived from the MD5 hash of the mutex 8bikfjjD4JpkkAqrz. This mutex is a hard-coded string value wherein its MD5 hash is used to decrypt the three binaries 1.exe, archive.exe, and Xarch.exe. It is important to note that the MTX value and the encrypted resources are different per sample.
We manually decrypted the content within each binary by exclusively utilizing the MD5 hash of the mutant name. Once this step was completed, we proceeded with the AES decryption to decrypt the encrypted resource file.
Figure 7. Code for decrypting the three binaries (top) and the decrypted binary file that came from the parent file (bottom)
The following table shows the details of the binaries dropped by the decrypted malware using the MTX value 8bikfjjD4JpkkAqrz. These three binaries exhibit similarities with the parent sample in terms of code structure and binary extraction:
File name
Bytes
Dropped file
1.exe
233488
1.exe
archive.exe
12843536
teleratserver.exe
Xarch.exe
65552
BXIuSsB.exe
Figure 8. 1.exe (left), teleratserver.exe (middle), and BXIuSsB.exe (right)
Binaries
This section details the binaries dropped, as identified from the previous table, and the first binary, 1.exe, was dropped by the parent sample.
1. Binary: 1.exe Bytes: 222224 MTX value that was used to decrypt this file: 2AESRvXK5jbtN9Rvh
Initially, the file will hide the console window by using WinAPI ShowWindow with SW_HIDE (0). The malware will create an autorun registry key, which allows it to execute automatically upon system startup. Additionally, it will make a copy of itself, which it will save as discord.exe in the <%localappdata%> folder in the local machine.
Figure 9. ShowWindow API code hides the window of the current process (top) and the creation of the registry key and drops a copy of itself as “discord.exe” (bottom)
The Big Head ransomware checks for the victim’s ID in %appdata%\ID. If the ID exists, the ransomware verifies the ID and reads the content. Otherwise, it creates a randomly generated 40-character string and writes it to the file %appdata%\ID as a type of infection marker to identify its victims.
Figure 10. Randomly generating the 40-character string ID (top) and file named ID saved in the “<%appdata%>” folder (bottom)
The observed behavior indicates that files with the extension “.r3d” are specifically targeted for encryption using AES, with the key derived from the SHA256 hash of “123” in cipher block chaining (CBC) mode. As a result, the encrypted files end up having the “.poop” extension appended to them.
Figure 11. The malware checks for the extension that contains “.r3d” before encrypting and appending the ”.poop” extension (top) and the file encryption process when the file extension “.r3d” exists (bottom).
In this file, we also observed how the ransomware deletes its shadow copies. The command used to delete shadow copies and backups, which is also used to disable the recovery option is as follows:
It drops the ransom note on the desktop, subdirectories, and the %appdata% folder. The Big Head ransomware also changes the wallpaper of the victim’s machine.
Figure 12. Ransom note of the “1.exe” binaryFigure 13. The wallpaper that appears on the victim’s machine
Lastly, it will execute the command to open a browser and access the malware developer’s Telegram account at hxxps[:]//t[.]me/[REDACTED]_69. Our analysis showed no particular action or communication being exchanged with this account in addition to the redirection.
2. Binary: teleratserver.exe Bytes: 12832480 MTX value that was used to decrypt this file: OJ4nwj2KO3bCeJoJ1
Teleratserver is a 64-bit Python-compiled binary that acts as a communication channel between the threat actor and the victim via Telegram. It accepts the commands “start”, “help”, “screenshot”, and “message”.
Figure 14. Decompiled Python script from the binary
3. Binary: BXIuSsB.exe Bytes: 54288 MTX value that was used to decrypt this file: gdmJp5RKIvzZTepRJ
The malware displays a fake Windows Update UI to deceive the victim into thinking that the malicious activity is a legitimate software update process, with the percentage of progress in increments of 100 seconds.
Figure 15. The code responsible for fake update (left) and the fake update shown to the user (right)
The malware terminates itself if the user’s system language matches the Russian, Belarusian, Ukrainian, Kazakh, Kyrgyz, Armenian, Georgian, Tatar, and Uzbek country codes. The malware also disables the Task Manager to prevent users from terminating or investigating its process.
Figure 16. The “KillCtrlAltDelete” command responsible for disabling the Task Manager
The malware drops a copy of itself in the hidden folder <%temp%\Adobe> that it created, then creates an entry in the RunOnce registry key, ensuring that it will only run once at the next system startup.
Figure 17. Creation of AutoRun registry
The malware also randomly generates a 32-character key that will later be used to encrypt files. This key will then be encrypted using RSA-2048 with a hard-coded public key.
The ransomware then drops the ransom note that includes the encrypted key.
Figure 18. The ransom note
The malware avoids the directories that contain the following substrings:
WINDOWS or Windows
RECYCLER or Recycler
Program Files
Program Files (x86)
Recycle.Bin or RECYCLE.BIN
TEMP or Temp
APPDATA or AppData
ProgramData
Microsoft
Burn
By excluding these directories from its malicious activities, the malware reduces the likelihood of being detected by security solutions installed in the system and increases its chances of remaining undetected and operational for a longer duration. The following are the extensions that the Big Head ransomware encrypts:
The malware renames the encrypted files using Base64. We observed the malware using the LockFile function which encrypts files by renaming them and adding a marker. This marker serves as an indicator to determine whether a file has been encrypted. Through further examination, we saw the function checking for the marker inside the encrypted file. When decrypted, the marker can be matched at the end of the encrypted file.
Figure 19. The LockFile functionFigure 20. Checking for the marker “###” (top) and finding the marker at the end of the encrypted file (bottom)
The malware targets the following languages and region or local settings of the current user’s operating system as listed in the following:
The ransomware checks for strings like VBOX, Virtual, or VMware in the disk enumeration registry to determine whether the system is operating within a virtual environment. It also scans for processes that contain the following substring: VBox, prl_(parallel’s desktop), srvc.exe, vmtoolsd.
Figure 21. Checking for virtual machine identifiers (top) and processes (bottom)
The malware identifies specific process names associated with virtualization software to determine if the system is running in a virtualized environment, allowing it to adjust its actions accordingly for better success or evasion. It can also proceed to delete recovery backup available by using the following command line:
After deleting the backup, regardless of the number available, it will proceed to delete itself using the SelfDelete() function. This function initiates the execution of the batch file, which will delete the malware executable and the batch file itself.
Figure 22. SelfDelete function
Second sample
The second sample of the Big Head ransomware we observed (SHA256: 2a36d1be9330a77f0bc0f7fdc0e903ddd99fcee0b9c93cb69d2f0773f0afd254, detected by Trend as Ransom.MSIL.EGOGEN.THEABBC) exhibits both ransomware and stealer behaviors.
Figure 23. The infection routine of the second sample of the Big Head ransomware
The main file drops and executes the following files:
The malware employs the AES algorithm to encrypt files and adds the suffix “.poop69news@[REDACTED]” to the encrypted files. It specifically targets files with the following extensions:
The file azz1.exe, which is also involved in other ransomware activities, establishes a registry entry at <HKCU\Software\Microsoft\Windows\CurrentVersion\Run>. This entry ensures the persistence of a copy of itself. It also drops a file containing the victim’s ID and a ransom note:
Figure 24. The ransom note for the second sample of the Big Head ransomware
Like the first sample, the second sample also changes the victim’s desktop wallpaper. Afterward, it will open the URL hxxps[:]//github[.]com/[REDACTED]_69 using the system’s default web browser. As of this writing, the URL is no longer available.
Other variants of this ransomware used the dropper azz1.exe as well, although the specific file might differ in each binary. Meanwhile, Server.exe, which we have identified as the WorldWind stealer, collects the following data:
Browsing history of all available browsers
List of directories
Replica of drivers
List of running processes
Product key
Networks
Screenshot of the screen after running the file
Third sample
The third sample (SHA256: 25294727f7fa59c49ef0181c2c8929474ae38a47b350f7417513f1bacf8939ff, detected by Trend as Ransom.MSIL.EGOGEN.YXDEL) includes a file infector we identified as Neshta in its chain.
Figure 25. The infection routine of the third sample of the Big Head ransomware
Neshta is a virus designed to infect and insert its malicious code into executable files. This malware also has a characteristic behavior of dropping a file called directx.sys, which contains the full path name of the infected file that was last executed. This behavior is not commonly observed in most types of malware, as they typically do not store such specific information in their dropped files.
Incorporating Neshta into the ransomware deployment can also serve as a camouflage technique for the final Big Head ransomware payload. This technique can make the piece of malware appear as a different type of threat, such as a virus, which can divert the prioritization of security solutions that primarily focus on detecting ransomware.
Notably, the ransom note and wallpaper associated with this binary are different from the ones previously mentioned.
Figure 26. Wallpaper (top) and ransom note (bottom) used in the victim’s machine post infection
The Big Head ransomware exhibits unique behaviors during the encryption process, such as displaying the Windows update screen as it encrypts files to deceive users and effectively locking them out of their machines, renaming the encrypted files using Base64 encoding to provide an extra layer of obfuscation, and as a whole making it more challenging for users to identify the original file names and types of encrypted files. We also noted the following significant distinctions among the three versions of the Big Head ransomware:
The first sample incorporates a backdoor in its infection chain.
The second sample employs a trojan spy and/or info stealer.
The third sample utilizes a file infector.
Threat actor
The ransom note clearly indicates that the malware developer utilizes both email and Telegram for communication with their victims. Upon further investigation with the given Telegram username, we were directed to a YouTube account.
The account on the platform is relatively new, having joined on April 19, 2023, With a total of 12 published videos as of this writing. This YouTube channel showcases demonstrations of the piece of malware the cybercriminals have. We also noted that in a pinned comment on each of their videos, they explicitly state their username on Telegram.
Figure 27. A new YouTube account with a number of videos featuring pieces of malware (top) and a Telegram username pinned in the comments section for all videos (bottom)
While we suspect that this actor engages in transactions on Telegram, it is worth noting that the YouTube name “aplikasi premium cuma cuma” is a phrase in Bahasa that translates to “premium application for free.” While it is possible, we can only speculate on any connection between the ransomware and the countries that use the said language.
Insights
Aside from the specific email address to tie all the samples of the Big Head ransomware together, the ransom notes from the samples have the same bitcoin wallet and drops the same files. Looking at the samples altogether, we can see that all the routines have the same structure in the infection process that it follows once the ransomware infects a system.
The malware developers mention in the comment section of their YouTube videos that they have a “new” Telegram account, indicative of an old one previously used. We also checked their Bitcoin wallet history and found transactions made in 2022. While we’re unaware of what those transactions are, the history implies that these cybercriminals are not new at this type of threats and attacks, although they might not be sophisticated actors as a whole.
The discovery of the Big Head ransomware as a developing piece of malware prior to the occurrence of any actual attacks or infections can be seen as a huge advantage for security researchers and analysts. Analysis and reporting of the variants provide an opportunity to analyze the codes, behaviors, and potential vulnerabilities. This information can then be used to develop countermeasures, patch vulnerabilities, and enhance security systems to mitigate future risks.
Moreover, advertising on YouTube without any evidence of “successful penetrations or infections” might seem premature promotional activities from a non-technical perspective. From a technical point of view, these malware developers left recognizable strings, used predictable encryption methods, or implementing weak or easily detectable evasion techniques, among other “mistakes.”
However, security teams should remain prepared given the malware’s diverse functionalities, encompassing stealers, infectors, and ransomware samples. This multifaceted nature gives the malware the potential to cause significant harm once fully operational, making it more challenging to defend systems against, as each attack vector requires separate attention.
Internet connections are most often marketed and sold on the basis of “speed”, with providers touting the number of megabits or gigabits per second that their various service tiers are supposed to provide. This marketing has largely been successful, as most subscribers believe that “more is better”. Furthermore, many national broadband plans in countries around the world include specific target connection speeds. However, even with a high speed connection, gamers may encounter sluggish performance, while video conference participants may experience frozen video or audio dropouts. Speeds alone don’t tell the whole story when it comes to Internet connection quality.
Additional factors like latency, jitter, and packet loss can significantly impact end user experience, potentially leading to situations where higher speed connections actually deliver a worse user experience than lower speed connections. Connection performance and quality can also vary based on usage – measured average speed will differ from peak available capacity, and latency varies under loaded and idle conditions.
The new Cloudflare Radar Internet Quality page
A little more than three years ago, as residential Internet connections were strained because of the shift towards working and learning from home due to the COVID-19 pandemic, Cloudflare announced the speed.cloudflare.com speed test tool, which enabled users to test the performance and quality of their Internet connection. Within the tool, users can download the results of their individual test as a CSV, or share the results on social media. However, there was no aggregated insight into Cloudflare speed test results at a network or country level to provide a perspective on connectivity characteristics across a larger population.
Today, we are launching these long-missing aggregated connection performance and quality insights on Cloudflare Radar. The new Internet Quality page provides both country and network (autonomous system) level insight into Internet connection performance (bandwidth) and quality (latency, jitter) over time. (Your Internet service provider is likely an autonomous system with its own autonomous system number (ASN), and many large companies, online platforms, and educational institutions also have their own autonomous systems and associated ASNs.) The insights we are providing are presented across two sections: the Internet Quality Index (IQI), which estimates average Internet quality based on aggregated measurements against a set of Cloudflare & third-party targets, and Connection Quality, which presents peak/best case connection characteristics based on speed.cloudflare.com test results aggregated over the previous 90 days. (Details on our approach to the analysis of this data are presented below.)
Users may note that individual speed test results, as well as the aggregate speed test results presented on the Internet Quality page will likely differ from those presented by other speed test tools. This can be due to a number of factors including differences in test endpoint locations (considering both geographic and network distance), test content selection, the impact of “rate boosting” by some ISPs, and testing over a single connection vs. multiple parallel connections. Infrequent testing (on any speed test tool) by users seeking to confirm perceived poor performance or validate purchased speeds will also contribute to the differences seen in the results published by the various speed test platforms.
And as we announced in April, Cloudflare has partnered with Measurement Lab (M-Lab) to create a publicly-available, queryable repository for speed test results. M-Lab is a non-profit third-party organization dedicated to providing a representative picture of Internet quality around the world. M-Lab produces and hosts the Network Diagnostic Tool, which is a very popular network quality test that records millions of samples a day. Given their mission to provide a publicly viewable, representative picture of Internet quality, we chose to partner with them to provide an accurate view of your Internet experience and the experience of others around the world using openly available data.
Connection speed & quality data is important
While most advertisements for fixed broadband and mobile connectivity tend to focus on download speeds (and peak speeds at that), there’s more to an Internet connection, and the user’s experience with that Internet connection, than that single metric. In addition to download speeds, users should also understand the upload speeds that their connection is capable of, as well as the quality of the connection, as expressed through metrics known as latency and jitter. Getting insight into all of these metrics provides a more well-rounded view of a given Internet connection, or in aggregate, the state of Internet connectivity across a geography or network.
The concept of download speeds are fairly well understood as a measure of performance. However, it is important to note that the average download speeds experienced by a user during common Web browsing activities, which often involves the parallel retrieval of multiple smaller files from multiple hosts, can differ significantly from peak download speeds, where the user is downloading a single large file (such as a video or software update), which allows the connection to reach maximum performance. The bandwidth (speed) available for upload is sometimes mentioned in ISP advertisements, but doesn’t receive much attention. (And depending on the type of Internet connection, there’s often a significant difference between the available upload and download speeds.) However, the importance of upload came to the forefront in 2020 as video conferencing tools saw a surge in usage as both work meetings and school classes shifted to the Internet during the COVID-19 pandemic. To share your audio and video with other participants, you need sufficient upload bandwidth, and this issue was often compounded by multiple people sharing a single residential Internet connection.
Latency is the time it takes data to move through the Internet, and is measured in the number of milliseconds that it takes a packet of data to go from a client (such as your computer or mobile device) to a server, and then back to the client. In contrast to speed metrics, lower latency is preferable. This is especially true for use cases like online gaming where latency can make a difference between a character’s life and death in the game, as well as video conferencing, where higher latency can cause choppy audio and video experiences, but it also impacts web page performance. The latency metric can be further broken down into loaded and idle latency. The former measures latency on a loaded connection, where bandwidth is actively being consumed, while the latter measures latency on an “idle” connection, when there is no other network traffic present. (These specific loaded and idle definitions are from the device’s perspective, and more specifically, from the speed test application’s perspective. Unless the speed test is being performed directly from a router, the device/application doesn’t have insight into traffic on the rest of the network.) Jitter is the average variation found in consecutive latency measurements, and can be measured on both idle and loaded connections. A lower number means that the latency measurements are more consistent. As with latency, Internet connections should have minimal jitter, which helps provide more consistent performance.
Our approach to data analysis
The Internet Quality Index (IQI) and Connection Quality sections get their data from two different sources, providing two different (albeit related) perspectives. Under the hood they share some common principles, though.
IQI builds upon the mechanism we already use to regularly benchmark ourselves against other industry players. It is based on end user measurements against a set of Cloudflare and third-party targets, meant to represent a pattern that has become very common in the modern Internet, where most content is served from distribution networks with points of presence spread throughout the world. For this reason, and by design, IQI will show worse results for regions and Internet providers that rely on international (rather than peering) links for most content.
IQI is also designed to reflect the traffic load most commonly associated with web browsing, rather than more intensive use. This, and the chosen set of measurement targets, effectively biases the numbers towards what end users experience in practice (where latency plays an important role in how fast things can go).
For each metric covered by IQI, and for each ASN, we calculate the 25th percentile, median, and 75th percentile at 15 minute intervals. At the country level and above, the three calculated numbers for each ASN visible from that region are independently aggregated. This aggregation takes the estimated user population of each ASN into account, biasing the numbers away from networks that source a lot of automated traffic but have few end users.
The Connection Quality section gets its data from the Cloudflare Speed Test tool, which exercises a user’s connection in order to see how well it is able to perform. It measures against the closest Cloudflare location, providing a good balance of realistic results and network proximity to the end user. We have a presence in 285 cities around the world, allowing us to be pretty close to most users.
Similar to the IQI, we calculate the 25th percentile, median, and 75th percentile for each ASN. But here these three numbers are immediately combined using an operation called the trimean — a single number meant to balance the best connection quality that most users have, with the best quality available from that ASN (users may not subscribe to the best available plan for a number of reasons).
Because users may choose to run a speed test for different motives at different times, and also because we take privacy very seriously and don’t record any personally identifiable information along with test results, we aggregate at 90-day intervals to capture as much variability as we can.
At the country level and above, the calculated trimean for each ASN in that region is aggregated. This, again, takes the estimated user population of each ASN into account, biasing the numbers away from networks that have few end users but which may still have technicians using the Cloudflare Speed Test to assess the performance of their network.
Navigating the Internet Quality page
The new Internet Quality page includes three views: Global, country-level, and autonomous system (AS). In line with the other pages on Cloudflare Radar, the country-level and AS pages show the same data sets, differing only in their level of aggregation. Below, we highlight the various components of the Internet Quality page.
Global
The top section of the global (worldwide) view includes time series graphs of the Internet Quality Index metrics aggregated at a continent level. The time frame shown in the graphs is governed by the selection made in the time frame drop down at the upper right of the page, and at launch, data for only the last three months is available. For users interested in examining a specific continent, clicking on the other continent names in the legend removes them from the graph. Although continent-level aggregation is still rather coarse, it still provides some insight into regional Internet quality around the world.
Further down the page, the Connection Quality section presents a choropleth map, with countries shaded according to the values of the speed, latency, or jitter metric selected from the drop-down menu. Hovering over a country displays a label with the country’s name and metric value, and clicking on the country takes you to the country’s Internet Quality page. Note that in contrast to the IQI section, the Connection Quality section always displays data aggregated over the previous 90 days.
Country-level
Within the country-level page (using Canada as an example in the figures below), the country’s IQI metrics over the selected time frame are displayed. These time series graphs show the median bandwidth, latency, and DNS response time within a shaded band bounded at the 25th and 75th percentile and represent the average expected user experience across the country, as discussed in the Our approach to data analysis section above.
Below that is the Connection Quality section, which provides a summary view of the country’s measured upload and download speeds, as well as latency and jitter, over the previous 90 days. The colored wedges in the Performance Summary graph are intended to illustrate aggregate connection quality at a glance, with an “ideal” connection having larger upload and download wedges and smaller latency and jitter wedges. Hovering over the wedges displays the metric’s value, which is also shown in the table to the right of the graph.
Below that, the Bandwidth and Latency/Jitter histograms illustrate the bucketed distribution of upload and download speeds, and latency and jitter measurements. In some cases, the speed histograms may show a noticeable bar at 1 Gbps, or 1000 ms (1 second) on the latency/jitter histograms. The presence of such a bar indicates that there is a set of measurements with values greater than the 1 Gbps/1000 ms maximum histogram values.
Autonomous system level
Within the upper-right section of the country-level page, a list of the top five autonomous systems within the country is shown. Clicking on an ASN takes you to the Performance page for that autonomous system. For others not displayed in the top five list, you can use the search bar at the top of the page to search by autonomous system name or number. The graphs shown within the AS level view are identical to those shown at a country level, but obviously at a different level of aggregation. You can find the ASN that you are connected to from the My Connection page on Cloudflare Radar.
Exploring connection performance & quality data
Digging into the IQI and Connection Quality visualizations can surface some interesting observations, including characterizing Internet connections, and the impact of Internet disruptions, including shutdowns and network issues. We explore some examples below.
Characterizing Internet connections
Verizon FiOS is a residential fiber-based Internet service available to customers in the United States. Fiber-based Internet services (as opposed to cable-based, DSL, dial-up, or satellite) will generally offer symmetric upload and download speeds, and the FiOS plans page shows this to be the case, offering 300 Mbps (upload & download), 500 Mbps (upload & download), and “1 Gig” (Verizon claims average wired speeds between 750-940 Mbps download / 750-880 Mbps upload) plans. Verizon carries FiOS traffic on AS701 (labeled UUNET due to a historical acquisition), and in looking at the bandwidth histogram for AS701, several things stand out. The first is a rough symmetry in upload and download speeds. (A cable-based Internet service provider, in contrast, would generally show a wide spread of download speeds, but have upload speeds clustered at the lower end of the range.) Another is the peaks around 300 Mbps and 750 Mbps, suggesting that the 300 Mbps and “1 Gig” plans may be more popular than the 500 Mbps plan. It is also clear that there are a significant number of test results with speeds below 300 Mbps. This is due to several factors: one is that Verizon also carries lower speed non-FiOS traffic on AS701, while another is that erratic nature of in-home WiFi often means that the speeds achieved on a test will be lower than the purchased service level.
Traffic shifts drive latency shifts
On May 9, 2023, the government of Pakistan ordered the shutdown of mobile network services in the wake of protests following the arrest of former Prime Minister Imran Khan. Our blog post covering this shutdown looked at the impact from a traffic perspective. Within the post, we noted that autonomous systems associated with fixed broadband networks saw significant increases in traffic when the mobile networks were shut down – that is, some users shifted to using fixed networks (home broadband) when mobile networks were unavailable.
Examining IQI data after the blog post was published, we found that the impact of this traffic shift was also visible in our latency data. As can be seen in the shaded area of the graph below, the shutdown of the mobile networks resulted in the median latency dropping about 25% as usage shifted from higher latency mobile networks to lower latency fixed broadband networks. An increase in latency is visible in the graph when mobile connectivity was restored on May 12.
Bandwidth shifts as a potential early warning sign
On April 4, UK mobile operator Virgin Media suffered several brief outages. In examining the IQI bandwidth graph for AS5089, the ASN used by Virgin Media (formerly branded as NTL), indications of a potential problem are visible several days before the outages occurred, as median bandwidth dropped by about a third, from around 35 Mbps to around 23 Mbps. The outages are visible in the circled area in the graph below. Published reports indicate that the problems lasted into April 5, in line with the lower median bandwidth measured through mid-day.
Submarine cable issues cause slower browsing
On June 5, Philippine Internet provider PLDTTweeted an advisory that noted “One of our submarine cable partners confirms a loss in some of its internet bandwidth capacity, and thus causing slower Internet browsing.” IQI latency and bandwidth graphs for AS9299, a primary ASN used by PLDT, shows clear shifts starting around 06:45 UTC (14:45 local time). Median bandwidth dropped by half, from 17 Mbps to 8 Mbps, while median latency increased by 75% from 37 ms to around 65 ms. 75th percentile latency also saw a significant increase, nearly tripling from 63 ms to 180 ms coincident with the reported submarine cable issue.
Conclusion
Making network performance and quality insights available on Cloudflare Radar supports Cloudflare’s mission to help build a better Internet. However, we’re not done yet – we have more enhancements planned. These include making data available at a more granular geographical level (such as state and possibly city), incorporating AIM scores to help assess Internet quality for specific types of use cases, and embedding the Cloudflare speed test directly on Radar using the open source JavaScript module.
In the meantime, we invite you to use speed.cloudflare.com to test the performance and quality of your Internet connection, share any country or AS-level insights you discover on social media (tag @CloudflareRadar on Twitter or @radar@cloudflare.social on Mastodon), and explore the underlying data through the M-Lab repository or the Radar API.
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