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Autonomous Action Prevents Cyber-Threats' Malicious Behavior

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02
Dec 2019
02
Dec 2019
Darktrace Antigena allows your security team to take the time they need to investigate malicious behavior. Learn how this can benefit your cyber security!
“The next phase in our journey toward autonomous security is Autonomous Response decision-making.”

Lawrence Pingree, Research Vice President, Gartner

We’ve talked extensively on this blog about Autonomous Response: the AI-powered technology that, according to Gartner, represents a paradigm shift in cyber defense. As the first such Autonomous Response tool, Darktrace Antigena has already thwarted countless cyber-attacks, from a spear phishing campaign against a major city to an IoT smart locker attack targeting a popular amusement park. Antigena’s surgical intervention afforded their security teams the time they needed to investigate — stopping the clock in seconds by containing just the malicious behavior.

For all its benefits, however, Autonomous Response does have one drawback: it can make for slightly anticlimactic blog posts. In place of captivating, step-by-step descriptions of malware spreading throughout the enterprise and inflicting irrevocable damage, Antigena case studies end a mere moment after they start, with the “patient zero” employee completely unaware of the compromise that could have been.

In this particular case, however, Antigena was deployed in Human Confirmation Mode — a starter mode wherein the AI’s actions must first be approved by the security team. Absent such approval, the result was both an in-depth look at a sophisticated ransomware attack, as well as a remarkable illustration of how Antigena reacted in real time to every stage of that attack’s lifecycle:

Initial download

Patient zero here was a device that Darktrace detected downloading an executable file from a server with which no other devices on the network had ever communicated. Downloads like this one regularly bypass conventional endpoint tools, since they cannot be programmed in advance to catch the full range of unpredictable future threats. By contrast, because Darktrace AI learned the typical behavior of the company’s unique users and devices while ‘on the job’, it easily determined the download to be anomalous.

Figure 1: Darktrace alerts on the 100% rare connection and subsequent download — as it occurs.

Had Antigena been in Active Mode at the time, this would have marked the end of the blog post. By blocking all connections to the associated IP and port, Antigena would have instantly stopped the download — without otherwise impacting the device at all.

Figure 2: Antigena, in Human Confirmation Mode, recommends that it block the suspicious activity.

Command and control

Following the download, Darktrace observed the device making an HTTP GET request to the same rare endpoint. The continuation of this suspicious activity precipitated an escalation in Antigena’s recommended response, which would now have blocked all outgoing traffic from the breached device to prevent any infection from spreading.

Darktrace then detected the device making yet more unusual external connections to endpoints that, in many cases, had self-signed SSL certificates. Such self-signed certificates do not require verification by a trusted authority and are therefore frequently utilized by cyber-criminals. As a consequence, the outgoing connections from our infected device are likely the installed malware communicating with its command and control infrastructure, as Darktrace flagged below:

Figure 3: Darktrace alerts on the suspicious SSL certificates.

Figure 4: Antigena recommends taking action to block the connections in question.

Internal reconnaissance

Beyond the unusual external activity observed from the breached device, it also began to deviate significantly from its typical pattern of internal behavior. Indeed, Darktrace detected the device making over 160,000 failed internal connections on two key ports: Remote Desktop Protocol port 3389 and SMB port 445. This activity — known as network scanning — provides crucial reconnaissance, giving the attacker insight into the network structure, the services available on each device, and any potential vulnerabilities. Ports 3389 and 445 are especially common targets.

Figure 5: Darktrace tracks this ransomware attack at every step, though the security team does not mount a response in time.

The unusual external connections to self-signed SSL certificates, combined with the highly anomalous internal connectivity from the device, would have caused Antigena to escalate further. Alas, the attack proceeds.

Darktrace detected no further anomalous activity from patient zero for the next four days — perhaps a mechanism to remain under the radar. Yet this period of dormancy concluded when, once again, the device connected to a rare domain with a self-signed SSL certificate, likely reaching out to its command and control infrastructure for additional instructions.

Lateral movement

A day later — in a sign that suggests the prior scanning was somewhat fruitful — the infected device performed a large amount of unusual SMB activity consistent with the malware attempting to move laterally across the network. Darktrace picked up on the breached device sending unusual outgoing SMB writes to the remote administration tool PsExec to a total of 38 destination devices, 28 of which it compromised with a malicious file.

Darktrace recognized this activity as highly anomalous for the particular device, as it doesn’t usually communicate with these destination devices in this manner. Antigena would therefore would have surgically blocked the remote administration behavior by first containing the patient zero device to its normal ‘pattern of life’, and then by escalating to blocking all outgoing connections from the device if lateral movement had continued. Antigena’s escalation can be seen below: the first action is taken at 08:03, the second, more severe action at 08:43.

Figure 6: Darktrace repeatedly alerts on the unusual SMB traffic with high confidence — thanks to its evolving understanding of the device’s typical ‘pattern of life’.
Figure 7: Antigena again recommends immediate intervention, this time to impede lateral movement.

Encryption

Darktrace observed the first sign of the ransomware’s ultimate objective — encrypting files — on a different device, which also performed a large volume of unusual SMB activity. After accessing a multitude of SMB shares that it hadn’t accessed previously, it systematically appended those files with the .locked extension. When all was said and done, this encryption activity was seen from no less than 40 internal devices.

In Active Mode, Antigena Ransomware Block would have fully quarantined the devices — a culmination of increasingly severe Antigena actions from the initial infection of patient zero, to the command and control communication, to the internal reconnaissance, to the lateral movement, and finally to the file encryption.

Figure 8: Antigena Ransomware Block was fully armed and prepared to fight back against the infection.

The case for boring blog posts

No other approach to cyber security is able to track ransomware so comprehensively throughout its lifecycle, as programming legacy tools to flag all remote administration behavior, for instance, would inundate security teams with thousands of false positive alerts. Thus, only Darktrace’s understanding ‘self’ for each infected device can shed light on such activities — in the rare cases when they are anomalous.

Figure 9: An overview of Darktrace’s myriad warnings throughout the five-day attack with each colored dot representing a high-confidence alert.

However, intriguing though it may be to track this lifecycle to conclusion, the technology to write far less intriguing blog posts already exists and is already proven. Autonomous Response will render this kind of threat story a relic of the past, and for organizations with sensitive data and critical intellectual property to safeguard, the days of boring security blogs cannot come soon enough.

INSIDE THE SOC
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.

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Lost in Translation: Darktrace Blocks Non-English Phishing Campaign Concealing Hidden Payloads

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15
May 2024

Email – the vector of choice for threat actors

In times of unprecedented globalization and internationalization, the enormous number of emails sent and received by organizations every day has opened the door for threat actors looking to gain unauthorized access to target networks.

Now, increasingly global organizations not only need to safeguard their email environments against phishing campaigns targeting their employees in their own language, but they also need to be able to detect malicious emails sent in foreign languages too [1].

Why are non-English language phishing emails more popular?

Many traditional email security vendors rely on pre-trained English language models which, while function adequately against malicious emails composed in English, would struggle in the face of emails composed in other languages. It should, therefore, come as no surprise that this limitation is becoming increasingly taken advantage of by attackers.  

Darktrace/Email™, on the other hand, focuses on behavioral analysis and its Self-Learning AI understands what is considered ‘normal’ for every user within an organization’s email environment, bypassing any limitations that would come from relying on language-trained models [1].

In March 2024, Darktrace observed anomalous emails on a customer’s network that were sent from email addresses belonging to an international fast-food chain. Despite this seeming legitimacy, Darktrace promptly identified them as phishing emails that contained malicious payloads, preventing a potentially disruptive network compromise.

Attack Overview and Darktrace Coverage

On March 3, 2024, Darktrace observed one of the customer’s employees receiving an email which would turn out to be the first of more than 50 malicious emails sent by attackers over the course of three days.

The Sender

Darktrace/Email immediately understood that the sender never had any previous correspondence with the organization or its employees, and therefore treated the emails with caution from the onset. Not only was Darktrace able to detect this new sender, but it also identified that the emails had been sent from a domain located in China and contained an attachment with a Chinese file name.

The phishing emails detected by Darktrace sent from a domain in China and containing an attachment with a Chinese file name.
Figure 1: The phishing emails detected by Darktrace sent from a domain in China and containing an attachment with a Chinese file name.

Darktrace further detected that the phishing emails had been sent in a synchronized fashion between March 3 and March 5. Eight unique senders were observed sending a total of 55 emails to 55 separate recipients within the customer’s email environment. The format of the addresses used to send these suspicious emails was “12345@fastflavor-shack[.]cn”*. The domain “fastflavor-shack[.]cn” is the legitimate domain of the Chinese division of an international fast-food company, and the numerical username contained five numbers, with the final three digits changing which likely represented different stores.

*(To maintain anonymity, the pseudonym “Fast Flavor Shack” and its fictitious domain, “fastflavor-shack[.]cn”, have been used in this blog to represent the actual fast-food company and the domains identified by Darktrace throughout this incident.)

The use of legitimate domains for malicious activities become commonplace in recent years, with attackers attempting to leverage the trust endpoint users have for reputable organizations or services, in order to achieve their nefarious goals. One similar example was observed when Darktrace detected an attacker attempting to carry out a phishing attack using the cloud storage service Dropbox.

As these emails were sent from a legitimate domain associated with a trusted organization and seemed to be coming from the correct connection source, they were verified by Sender Policy Framework (SPF) and were able to evade the customer’s native email security measures. Darktrace/Email; however, recognized that these emails were actually sent from a user located in Singapore, not China.

Darktrace/Email identified that the email had been sent by a user who had logged in from Singapore, despite the connection source being in China.
Figure 2: Darktrace/Email identified that the email had been sent by a user who had logged in from Singapore, despite the connection source being in China.

The Emails

Darktrace/Email autonomously analyzed the suspicious emails and identified that they were likely phishing emails containing a malicious multistage payload.

Darktrace/Email identifying the presence of a malicious phishing link and a multistage payload.
Figure 3: Darktrace/Email identifying the presence of a malicious phishing link and a multistage payload.

There has been a significant increase in multistage payload attacks in recent years, whereby a malicious email attempts to elicit recipients to follow a series of steps, such as clicking a link or scanning a QR code, before delivering a malicious payload or attempting to harvest credentials [2].

In this case, the malicious actor had embedded a suspicious link into a QR code inside a Microsoft Word document which was then attached to the email in order to direct targets to a malicious domain. While this attempt to utilize a malicious QR code may have bypassed traditional email security tools that do not scan for QR codes, Darktrace was able to identify the presence of the QR code and scan its destination, revealing it to be a suspicious domain that had never previously been seen on the network, “sssafjeuihiolsw[.]bond”.

Suspicious link embedded in QR Code, which was detected and extracted by Darktrace.
Figure 4: Suspicious link embedded in QR Code, which was detected and extracted by Darktrace.

At the time of the attack, there was no open-source intelligence (OSINT) on the domain in question as it had only been registered earlier the same day. This is significant as newly registered domains are typically much more likely to bypass gateways until traditional security tools have enough intelligence to determine that these domains are malicious, by which point a malicious actor may likely have already gained access to internal systems [4]. Despite this, Darktrace’s Self-Learning AI enabled it to recognize the activity surrounding these unusual emails as suspicious and indicative of a malicious phishing campaign, without needing to rely on existing threat intelligence.

The most commonly used sender name line for the observed phishing emails was “财务部”, meaning “finance department”, and Darktrace observed subject lines including “The document has been delivered”, “Income Tax Return Notice” and “The file has been released”, all written in Chinese.  The emails also contained an attachment named “通知文件.docx” (“Notification document”), further indicating that they had been crafted to pass for emails related to financial transaction documents.

 Darktrace/Email took autonomous mitigative action against the suspicious emails by holding the message from recipient inboxes.
Figure 5: Darktrace/Email took autonomous mitigative action against the suspicious emails by holding the message from recipient inboxes.

Conclusion

Although this phishing attack was ultimately thwarted by Darktrace/Email, it serves to demonstrate the potential risks of relying on solely language-trained models to detect suspicious email activity. Darktrace’s behavioral and contextual learning-based detection ensures that any deviations in expected email activity, be that a new sender, unusual locations or unexpected attachments or link, are promptly identified and actioned to disrupt the attacks at the earliest opportunity.

In this example, attackers attempted to use non-English language phishing emails containing a multistage payload hidden behind a QR code. As traditional email security measures typically rely on pre-trained language models or the signature-based detection of blacklisted senders or known malicious endpoints, this multistage approach would likely bypass native protection.  

Darktrace/Email, meanwhile, is able to autonomously scan attachments and detect QR codes within them, whilst also identifying the embedded links. This ensured that the customer’s email environment was protected against this phishing threat, preventing potential financial and reputation damage.

Credit to: Rajendra Rushanth, Cyber Analyst, Steven Haworth, Head of Threat Modelling, Email

Appendices  

List of Indicators of Compromise (IoCs)  

IoC – Type – Description

sssafjeuihiolsw[.]bond – Domain Name – Suspicious Link Domain

通知文件.docx – File - Payload  

References

[1] https://darktrace.com/blog/stopping-phishing-attacks-in-enter-language  

[2] https://darktrace.com/blog/attacks-are-getting-personal

[3] https://darktrace.com/blog/phishing-with-qr-codes-how-darktrace-detected-and-blocked-the-bait

[4] https://darktrace.com/blog/the-domain-game-how-email-attackers-are-buying-their-way-into-inboxes

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Rajendra Rushanth
Cyber Analyst

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The State of AI in Cybersecurity: The Impact of AI on Cybersecurity Solutions

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13
May 2024

About the 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.

This blog continues the conversation from “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners” which was an overview of the entire report. This blog will focus on one aspect of the overarching report, the impact of AI on cybersecurity solutions.

To access the full report, click here.

The effects of AI on cybersecurity solutions

Overwhelming alert volumes, high false positive rates, and endlessly innovative threat actors keep security teams scrambling. Defenders have been forced to take a reactive approach, struggling to keep pace with an ever-evolving threat landscape. It is hard to find time to address long-term objectives or revamp operational processes when you are always engaged in hand-to-hand combat.                  

The impact of AI on the threat landscape will soon make yesterday’s approaches untenable. Cybersecurity vendors are racing to capitalize on buyer interest in AI by supplying solutions that promise to meet the need. But not all AI is created equal, and not all these solutions live up to the widespread hype.  

Do security professionals believe AI will impact their security operations?

Yes! 95% of cybersecurity professionals agree that AI-powered solutions will level up their organization’s defenses.                                                                

Not only is there strong agreement about the ability of AI-powered cybersecurity solutions to improve the speed and efficiency of prevention, detection, response, and recovery, but that agreement is nearly universal, with more than 95% alignment.

This AI-powered future is about much more than generative AI. While generative AI can help accelerate the data retrieval process within threat detection, create quick incident summaries, automate low-level tasks in security operations, and simulate phishing emails and other attack tactics, most of these use cases were ranked lower in their impact to security operations by survey participants.

There are many other types of AI, which can be applied to many other use cases:

Supervised machine learning: Applied more often than any other type of AI in cybersecurity. Trained on attack patterns and historical threat intelligence to recognize known attacks.

Natural language processing (NLP): Applies computational techniques to process and understand human language. It can be used in threat intelligence, incident investigation, and summarization.

Large language models (LLMs): Used in generative AI tools, this type of AI applies deep learning models trained on massively large data sets to understand, summarize, and generate new content. The integrity of the output depends upon the quality of the data on which the AI was trained.

Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies. With the correct models, this AI can use anomaly-based detections to identify all kinds of cyber-attacks, including entirely unknown and novel ones.

What are the areas of cybersecurity AI will impact the most?

Improving threat detection is the #1 area within cybersecurity where AI is expected to have an impact.                                                                                  

The most frequent response to this question, improving threat detection capabilities in general, was top ranked by slightly more than half (57%) of respondents. This suggests security professionals hope that AI will rapidly analyze enormous numbers of validated threats within huge volumes of fast-flowing events and signals. And that it will ultimately prove a boon to front-line security analysts. They are not wrong.

Identifying exploitable vulnerabilities (mentioned by 50% of respondents) is also important. Strengthening vulnerability management by applying AI to continuously monitor the exposed attack surface for risks and high-impact vulnerabilities can give defenders an edge. If it prevents threats from ever reaching the network, AI will have a major downstream impact on incident prevalence and breach risk.

Where will defensive AI have the greatest impact on cybersecurity?

Cloud security (61%), data security (50%), and network security (46%) are the domains where defensive AI is expected to have the greatest impact.        

Respondents selected broader domains over specific technologies. In particular, they chose the areas experiencing a renaissance. Cloud is the future for most organizations,
and the effects of cloud adoption on data and networks are intertwined. All three domains are increasingly central to business operations, impacting everything everywhere.

Responses were remarkably consistent across demographics, geographies, and organization sizes, suggesting that nearly all survey participants are thinking about this similarly—that AI will likely have far-reaching applications across the broadest fields, as well as fewer, more specific applications within narrower categories.

Going forward, it will be paramount for organizations to augment their cloud and SaaS security with AI-powered anomaly detection, as threat actors sharpen their focus on these targets.

How will security teams stop AI-powered threats?            

Most security stakeholders (71%) are confident that AI-powered security solutions are better able to block AI-powered threats than traditional tools.

There is strong agreement that AI-powered solutions will be better at stopping AI-powered threats (71% of respondents are confident in this), and there’s also agreement (66%) that AI-powered solutions will be able to do so automatically. This implies significant faith in the ability of AI to detect threats both precisely and accurately, and also orchestrate the correct response actions.

There is also a high degree of confidence in the ability of security teams to implement and operate AI-powered solutions, with only 30% of respondents expressing doubt. This bodes well for the acceptance of AI-powered solutions, with stakeholders saying they’re prepared for the shift.

On the one hand, it is positive that cybersecurity stakeholders are beginning to understand the terms of this contest—that is, that only AI can be used to fight AI. On the other hand, there are persistent misunderstandings about what AI is, what it can do, and why choosing the right type of AI is so important. Only when those popular misconceptions have become far less widespread can our industry advance its effectiveness.  

To access the full report, click here.

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