Catching Mimikatz Behavior with Anomaly Detection Defense
14
Feb 2019
14
Feb 2019
Detect and prevent Mimikatz cyber attacks with AI anomaly detection. Read more to learn how cyber defenses can stop Mimikatz and its variants in their tracks.
Originally created by famed French programmer Benjamin Delpy to highlight security flaws in Windows authentication mechanisms, today Mimikatz is a staple post-exploitation module in the arsenal of cyber-criminals, since it facilitates lateral movement across a victim’s network. Mimikatz was a primary feature of the global ransomware attacks NotPetya and BadRabbit, in addition to the alleged Russian hacking of the German parliament in 2015 and 2017.
Among the primary vulnerabilities that Mimikatz exploits is Windows’ Local Security Authority Subsystem Service (LSASS), which is designed to obviate the need for users to reauthenticate every time they seek to access internal resources. Despite its clear utility, LSASS works by keeping a cache of every credential used since the last boot, presenting an obvious security risk in the event the cache is compromised. Broadly speaking, Mimikatz plunders this resource and allows users to access cleartext passwords as well as NTLM hashes. With this data in hand, threat actors are able to conduct the following attacks:
Kerberos Golden Ticket: Provides administrative credentials for the whole domain.
Pass-the-Ticket: Enables a user to pass a Kerberos ticket to a second device and login using this ticket.
Kerberos Silver Ticket: Provides a TGS ticket to log into any network service.
Pass-the-Hash: Allows a user to pass a hash string in order to login.
Dumping LSASS memory is just one method that Mimikatz and its many updated versions employ to harvest credentials. Indeed, once malware such as NotPetya has established itself on single device, the Mimikatz module can exploit a variety of security flaws to obtain the password information for any other users or computers that have logged onto that machine: a key step for both lateral movement and privilege escalation. Like many successful hacker tools, Mimikatz has inspired the creation of other programs with similar aims, which are largely intended to circumvent antivirus controls.
Using AI to end the cat-and-mouse game
At the most basic level, security teams can reduce vulnerability to Mimikatz and to lateral movement more generally by ensuring that each user has the minimum amount of privileges needed for his or her role. But while this measure is certainly prudent, it will not always be effective — especially when dealing with sophisticated threats. Another strategy is to implement endpoint security tools and anti-virus software, which rely on rules and signatures to detect known Mimikatz variants. However, as Mimikatz and its copycats continue to evolve, these traditional tools are locked in a ceaseless cat-and-mouse game, unable to spot unknown variants of Mimikatz that are designed to circumvent them.
As a fundamentally different approach to security, artificially intelligent systems like Darktrace avoid this cat-and-mouse game by learning what constitutes normal behavior for the users and devices they safeguard while “on the job,” rather than using fixed rules and signatures. This approach alerts defenders to any anomalous activity, regardless of whether such activity constitutes a known or unknown threat. Typically, lateral movement involving Mimikatz will involve a spike in unusual SMB activity, as attackers seek to write the tool to target devices. The following recent attack on a Darktrace customer highlights how AI can allow security teams to quickly detect and respond to such lateral movement involving Mimikatz:
Darktrace alerts to a non-domain joined Linux device appearing on the customer’s network and engaging in extensive SMB bruteforce activity against key servers.
Figure 1: Darktrace detects the spike in SMB activity.
Darktrace flags the device successfully logging into a server using administrative credentials via SMBv1, opening the \sect pipe and writing the suspicious and likely malicious file Syssvc.exe to the server’s \temp folder. Immediately after this, Darktrace alerts to the device writing mimikatz.exe to the same folder.
Figure 2: Darktrace flags the device using Mimikatz.
Following this step, multiple .tmp and .txt files appear on the target device, indicating that Mimikatz was proceeding to access passwords and hashes.
The novelty of this AI-powered approach is that it does not rely on a string search for the term “mimikatz.exe”; rather, it highlights the unusual behavior that commonly surrounds Mimikatz’ activity. As shown in the screenshot above, this behavior constitutes “New activity”: Darktrace has not seen this type of SMB activity between the source and the destination before. Moreover, SMBv1 is used — highly unusual for the environment. And finally, it is unusual for devices in the target environment to make SMB network drive writes to Temp folders. All these subtle deviations from the customer’s ‘pattern of life,’ taken together, caused Darktrace to alert on the Mimikatz behavior.
Since its introduction to the cyber-threat landscape, Mimikatz has become a highly effective means for cyber-attackers to move laterally inside corporate and government networks. But by empowering security teams to respond before attackers can plunder a network’s entire cache of passwords, AI cyber defenses are thwarting Mimikatz and its copycats alike.
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Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
AUTHOR
ABOUT ThE AUTHOR
Max Heinemeyer
Chief Product Officer
Max is a cyber security expert with over a decade of experience in the field, specializing in a wide range of areas such as Penetration Testing, Red-Teaming, SIEM and SOC consulting and hunting Advanced Persistent Threat (APT) groups. At Darktrace, Max is closely involved with Darktrace’s strategic customers & prospects. He works with the R&D team at Darktrace, shaping research into new AI innovations and their various defensive and offensive applications. Max’s insights are regularly featured in international media outlets such as the BBC, Forbes and WIRED. Max holds an MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.
The State of AI in Cybersecurity: Understanding AI Technologies
24
Jul 2024
About the State of AI Cybersecurity Report
Darktrace surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.
How familiar are security professionals with supervised machine learning
Just 31% of security professionals report that they are “very familiar” with supervised machine learning.
Many participants admitted unfamiliarity with various AI types. Less than one-third felt "very familiar" with the technologies surveyed: only 31% with supervised machine learning and 28% with natural language processing (NLP).
Most participants were "somewhat" familiar, ranging from 46% for supervised machine learning to 36% for generative adversarial networks (GANs). Executives and those in larger organizations reported the highest familiarity.
Combining "very" and "somewhat" familiar responses, 77% had familiarity with supervised machine learning, 74% generative AI, and 73% NLP. With generative AI getting so much media attention, and NLP being the broader area of AI that encompasses generative AI, these results may indicate that stakeholders are understanding the topic on the basis of buzz, not hands-on work with the technologies.
If defenders hope to get ahead of attackers, they will need to go beyond supervised learning algorithms trained on known attack patterns and generative AI. Instead, they’ll need to adopt a comprehensive toolkit comprised of multiple, varied AI approaches—including unsupervised algorithms that continuously learn from an organization’s specific data rather than relying on big data generalizations.
Different types of AI
Different types of AI have different strengths and use cases in cyber security. It’s important to choose the right technique for what you’re trying to achieve.
Supervised machine learning: Applied more often than any other type of AI in cyber security. Trained on human attack patterns and historical threat intelligence.
Large language models (LLMs): Applies deep learning models trained on extremely large data sets to understand, summarize, and generate new content. Used in generative AI tools.
Natural language processing (NLP): Applies computational techniques to process and understand human language.
Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies.
What impact will generative AI have on the cybersecurity field?
More than half of security professionals (57%) believe that generative AI will have a bigger impact on their field over the next few years than other types of AI.
Figure 1: Chart from Darktrace's State of AI in Cybersecurity Report
Security stakeholders are highly aware of generative AI and LLMs, viewing them as pivotal to the field's future. Generative AI excels at abstracting information, automating tasks, and facilitating human-computer interaction. However, LLMs can "hallucinate" due to training data errors and are vulnerable to prompt injection attacks. Despite improvements in securing LLMs, the best cyber defenses use a mix of AI types for enhanced accuracy and capability.
AI education is crucial as industry expectations for generative AI grow. Leaders and practitioners need to understand where and how to use AI while managing risks. As they learn more, there will be a shift from generative AI to broader AI applications.
Do security professionals fully understand the different types of AI in security products?
Only 26% of security professionals report a full understanding of the different types of AI in use within security products.
Confusion is prevalent in today’s marketplace. Our survey found that only 26% of respondents fully understand the AI types in their security stack, while 31% are unsure or confused by vendor claims. Nearly 65% believe generative AI is mainly used in cybersecurity, though it’s only useful for identifying phishing emails. This highlights a gap between user expectations and vendor delivery, with too much focus on generative AI.
Key findings include:
Executives and managers report higher understanding than practitioners.
Larger organizations have better understanding due to greater specialization.
As AI evolves, vendors are rapidly introducing new solutions faster than practitioners can learn to use them. There's a strong need for greater vendor transparency and more education for users to maximize the technology's value.
To help ease confusion around AI technologies in cybersecurity, Darktrace has released the CISO’s Guide to Cyber AI. A comprehensive white paper that categorizes the different applications of AI in cybersecurity. Download the White Paper here.
Do security professionals believe generative AI alone is enough to stop zero-day threats?
No! 86% of survey participants believe generative AI alone is NOT enough to stop zero-day threats
This consensus spans all geographies, organization sizes, and roles, though executives are slightly less likely to agree. Asia-Pacific participants agree more, while U.S. participants agree less.
Despite expecting generative AI to have the most impact, respondents recognize its limited security use cases and its need to work alongside other AI types. This highlights the necessity for vendor transparency and varied AI approaches for effective security across threat prevention, detection, and response.
Stakeholders must understand how AI solutions work to ensure they offer advanced, rather than outdated, threat detection methods. The survey shows awareness that old methods are insufficient.
Jupyter Ascending: Darktrace’s Investigation of the Adaptive Jupyter Information Stealer
18
Jul 2024
What is Malware as a Service (MaaS)?
Malware as a Service (MaaS) is a model where cybercriminals develop and sell or lease malware to other attackers.
This approach allows individuals or groups with limited technical skills to launch sophisticated cyberattacks by purchasing or renting malware tools and services. MaaS is often provided through online marketplaces on the dark web, where sellers offer various types of malware, including ransomware, spyware, and trojans, along with support services such as updates and customer support.
The Growing MaaS Marketplace
The Malware-as-a-Service (MaaS) marketplace is rapidly expanding, with new strains of malware being regularly introduced and attracting waves of new and previous attackers. The low barrier for entry, combined with the subscription-like accessibility and lucrative business model, has made MaaS a prevalent tool for cybercriminals. As a result, MaaS has become a significant concern for organizations and their security teams, necessitating heightened vigilance and advanced defense strategies.
Examples of Malware as a Service
Ransomware as a Service (RaaS): Providers offer ransomware kits that allow users to launch ransomware attacks and share the ransom payments with the service provider.
Phishing as a Service: Services that provide phishing kits, including templates and email lists, to facilitate phishing campaigns.
Botnet as a Service: Renting out botnets to perform distributed denial-of-service (DDoS) attacks or other malicious activities.
Information Stealer: Information stealers are a type of malware specifically designed to collect sensitive data from infected systems, such as login credentials, credit card numbers, personal identification information, and other valuable data.
How does information stealer malware work?
Information stealers are an often-discussed type MaaS tool used to harvest personal and proprietary information such as administrative credentials, banking information, and cryptocurrency wallet details. This information is then exfiltrated from target networks via command-and-control (C2) communication, allowing threat actors to monetize the data. Information stealers have also increasingly been used as an initial access vector for high impact breaches including ransomware attacks, employing both double and triple extortion tactics.
After investigating several prominent information stealers in recent years, the Darktrace Threat Research team launched an investigation into indicators of compromise (IoCs) associated with another variant in late 2023, namely the Jupyter information stealer.
What is Jupyter information stealer and how does it work?
The Jupyter information stealer (also known as Yellow Cockatoo, SolarMarker, and Polazert) was first observed in the wild in late 2020. Multiple variants have since become part of the wider threat landscape, however, towards the end of 2023 a new variant was observed. This latest variant achieved greater stealth and updated its delivery method, targeting browser extensions such as Edge, Firefox, and Chrome via search engine optimization (SEO) poisoning and malvertising. This then redirects users to download malicious files that typically impersonate legitimate software, and finally initiates the infection and the attack chain for Jupyter [3][4]. In recently noted cases, users download malicious executables for Jupyter via installer packages created using InnoSetup – an open-source compiler used to create installation packages in the Windows OS.
The latest release of Jupyter reportedly takes advantage of signed digital certificates to add credibility to downloaded executables, further supplementing its already existing tactics, techniques and procedures (TTPs) for detection evasion and sophistication [4]. Jupyter does this while still maintaining features observed in other iterations, such as dropping files into the %TEMP% folder of a system and using PowerShell to decrypt and load content into memory [4]. Another reported feature includes backdoor functionality such as:
C2 infrastructure
Ability to download and execute malware
Execution of PowerShell scripts and commands
Injecting shellcode into legitimate windows applications
Darktrace Coverage of Jupyter information stealer
In September 2023, Darktrace’s Threat Research team first investigated Jupyter and discovered multiple IoCs and TTPs associated with the info-stealer across the customer base. Across most investigated networks during this time, Darktrace observed the following activity:
HTTP POST requests over destination port 80 to rare external IP addresses (some of these connections were also made via port 8089 and 8090 with no prior hostname lookup).
HTTP POST requests specifically to the root directory of a rare external endpoint.
Data streams being sent to unusual external endpoints
Anomalous PowerShell execution was observed on numerous affected networks.
Taking a further look at the activity patterns detected, Darktrace identified a series of HTTP POST requests within one customer’s environment on December 7, 2023. The HTTP POST requests were made to the root directory of an external IP address, namely 146.70.71[.]135, which had never previously been observed on the network. This IP address was later reported to be malicious and associated with Jupyter (SolarMarker) by open-source intelligence (OSINT) [5].
Figure 1: Device Event Log indicating several connections from the source device to the rare external IP address 146.70.71[.]135 over port 80.
This activity triggered the Darktrace / NETWORK model, ‘Anomalous Connection / Posting HTTP to IP Without Hostname’. This model alerts for devices that have been seen posting data out of the network to rare external endpoints without a hostname. Further investigation into the offending device revealed a significant increase in external data transfers around the time Darktrace alerted the activity.
Figure 2: This External Data Transfer graph demonstrates a spike in external data transfer from the internal device indicated at the top of the graph on December 7, 2023, with a time lapse shown of one week prior.
Packet capture (PCAP) analysis of this activity also demonstrates possible external data transfer, with the device observed making a POST request to the root directory of the malicious endpoint, 146.70.71[.]135.
Figure 3: PCAP of a HTTP POST request showing streams of data being sent to the endpoint, 146.70.71[.]135.
In other cases investigated by the Darktrace Threat Research team, connections to the rare external endpoint 67.43.235[.]218 were detected on port 8089 and 8090. This endpoint was also linked to Jupyter information stealer by OSINT sources [6].
Darktrace recognized that such suspicious connections represented unusual activity and raised several model alerts on multiple customer environments, including ‘Compromise / Large Number of Suspicious Successful Connections’ and ‘Anomalous Connection / Multiple Connections to New External TCP Port’.
In one instance, a device that was observed performing many suspicious connections to 67.43.235[.]218 was later observed making suspicious HTTP POST connections to other malicious IP addresses. This included 2.58.14[.]246, 91.206.178[.]109, and 78.135.73[.]176, all of which had been linked to Jupyter information stealer by OSINT sources [7] [8] [9].
Darktrace further observed activity likely indicative of data streams being exfiltrated to Jupyter information stealer C2 endpoints.
Figure 4: Graph displaying the significant increase in the number of HTTP POST requests with No Get made by an affected device, likely indicative of Jupyter information stealer C2 activity.
In several cases, Darktrace was able to leverage customer integrations with other security vendors to add additional context to its own model alerts. For example, numerous customers who had integrated Darktrace with Microsoft Defender received security integration alerts that enriched Darktrace’s model alerts with additional intelligence, linking suspicious activity to Jupyter information stealer actors.
Figure 5: The security integration model alerts ‘Security Integration / Low Severity Integration Detection’ and (right image) ‘Security Integration / High Severity Integration Detection’, linking suspicious activity observed by Darktrace with Jupyter information stealer (SolarMarker).
Conclusion
The MaaS ecosystems continue to dominate the current threat landscape and the increasing sophistication of MaaS variants, featuring advanced defense evasion techniques, poses significant risks once deployed on target networks.
Leveraging anomaly-based detections is crucial for staying ahead of evolving MaaS threats like Jupyter information stealer. By adopting AI-driven security tools like Darktrace / NETWORK, organizations can more quickly identify and effectively detect and respond to potential threats as soon as they emerge. This is especially crucial given the rise of stealthy information stealing malware strains like Jupyter which cannot only harvest and steal sensitive data, but also serve as a gateway to potentially disruptive ransomware attacks.
Credit to Nahisha Nobregas (Senior Cyber Analyst), Vivek Rajan (Cyber Analyst)
![Device Event Log indicating several connections from the source device to the rare external IP address 146.70.71[.]135 over port 80.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/669973708ae3bd007b761051_Screenshot%202024-07-18%20at%2012.55.55%20PM.png)
![PCAP of a HTTP POST request showing streams of data being sent to the endpoint, 146.70.71[.]135.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/669973c9c7e48724389cdff4_Screenshot%202024-07-18%20at%2012.57.50%20PM.png)
