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April 5, 2022

How Darktrace Antigena Thwarted Cobalt Strike Attack

Learn how Darktrace's Antigena technology intercepted and delayed a Cobalt Strike intrusion. Discover more cybersecurity news and analyses on Darktrace's blog.
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.
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Dylan Evans
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05
Apr 2022

In December 2021 several CVEs[1] were issued for the Log4j vulnerabilities that sent security teams into a global panic. Threat actors are now continuously scanning external infrastructure for evidence of the vulnerability to deploy crypto-mining malware.[2] However, through December ‘21 – February ‘22, it was ransomware groups that seized the initiative.

Compromise

In January 2022, a Darktrace customer left an external-facing VMware server unpatched allowing Cobalt Strike to be successfully installed. Several IoCs indicate that Cuba Ransomware operators were behind the attack. Thanks to the Darktrace SOC service, the customer was notified of the active threat on their network, and Antigena’s Autonomous Response was able to keep the attackers at bay before encryption events took place.

Initially the VMware server breached two models relating to an anomalous script download and a new user agent both connecting via HTTP. As referenced in an earlier Darktrace blog, both of these models had been seen in previous Log4j exploits. As with all Darktrace models however, the model deck is not designed to detect only one exploit, infection variant, or APT.

Figure 1: Darktrace models breaching due to the malicious script download

Analyst investigation

A PCAP of the downloaded script showed that it contained heavily obfuscated JavaScript. After an OSINT investigation a similar script was uncovered which likely breached the same Yara rules.

Figure 2: PCAP of the Initial HTTP GET request for the Windows Script component

Figure 3: PCAP of the initial HTTP response containing obfuscated JavaScript

Figure 4: A similar script that has been observed installing additional payloads after an initial infection[3]

While not an exact match, this de-obfuscated code shared similarities to those seen when downloading other banking trojans.

Having identified on the Darktrace UI that this was a VMware server, the analyst isolated the incoming external connections to the server shortly prior to the HTTP GET requests and was able to find an IP address associated with Log4j exploit attempts.

Figure 5: Advanced Search logs showing incoming SSL connections from an IP address linked to Log4j exploits

Through Advanced Search the analyst identified spikes shortly prior and immediately after the download. This suggested the files were downloaded and executed by exploiting the Log4j vulnerability.

Antigena response

Figure 6: AI Analyst reveals both the script downloads and the unusual user agent associated with the connections

Figure 7: Antigena blocked all further connections to these endpoints following the downloads

Cobalt Strike

Cobalt Strike is a popular tool for threat actors as it can be used to perform a swathe of MITRE ATT&CK techniques. In this case the threat actor attempted command and control tactics to pivot through the network, however, Antigena responded promptly when the malware attempted to communicate with external infrastructure.

On Wednesday January 26, the DNS beacon attempted to connect to malicious infrastructure. Antigena responded, and a Darktrace SOC analyst issued an alert.

Figure 8: A Darktrace model detected the suspicious DNS requests and Antigena issued a response

The attacker changed their strategy by switching to a different server “bluetechsupply[.]com” and started issuing commands over TLS. Again, Darktrace detected these connections and AI Analyst reported on the incident (Figure 9, below). OSINT sources subsequently indicated that this destination is affiliated with Cobalt Strike and was only registered 14 days prior to this incident.

Figure 9: AI Analyst summary of the suspicious beaconing activity

Simultaneous to these connections, the device scanned multiple internal devices via an ICMP scan and then scanned the domain controller over key TCP ports including 139 and 445 (SMB). This was followed by an attempt to write an executable file to the domain controller. While Antigena intervened in the file write, another Darktrace SOC analyst was issuing an alert due to the escalation in activity.

Figure 10: AI Analyst summary of the .dll file that Antigena intercepted to the Windows/temp directory of the domain controller

Following the latest round of Antigena blocks, the threat actor attempted to change methods again. The VMware server utilised the Remote Access Tool/Trojan NetSupport Manager in an attempt to install further malware.

Figure 11: Darktrace reveals the attacker changing tactics

Despite this escalation, Darktrace yet again blocked the connection.

Perhaps due to an inability to connect to C2 infrastructure, the attack stopped in its tracks for around 12 hours. Thanks to Antigena and the Darktrace SOC team, the security team had been afforded time to remediate and recover from the active threat in their network. Interestingly, Darktrace detected a final attempt at pivoting from the machine, with an unusual PowerShell Win-RM connection to an internal machine. The modern Win-RM protocol typically utilises port 5985 for HTTP connections however pre-Windows 7 machines may use Windows 7 indicating this server was running an old OS.

Figure 12: Darktrace detects unusual PowerShell usage

Cuba Ransomware

While no active encryption appears to have taken place for this customer, a range of IoCs were identified which indicated that the threat actor was the group being tracked as UNC2596, the operators of Cuba Ransomware.[4]

These IoCs include: one of the initially dropped files (komar2.ps1,[5] revealed by AI Analyst in Figure 6), use of the NetSupport RAT,[6] and Cobalt Strike beaconing.[7] These were implemented to maintain persistence and move laterally across the network.

Cuba Ransomware operators prefer to exfiltrate data to their beacon infrastructure rather than using cloud storage providers, however no evidence of upload activity was observed on the customer’s network.

Concluding thoughts

Unpatched, external-facing VMware servers vulnerable to the Log4j exploit are actively being targeted by threat actors with the aim of ransomware detonation. Without using rules or signatures, Darktrace was able to detect all stages of the compromise. While Antigena delayed the attack, forcing the threat actor to change C2 servers constantly, the Darktrace analyst team relayed their findings to the security team who were able to remediate the compromised machines and prevent a final ransomware payload from detonating.

For Darktrace customers who want to find out more about Cobalt Strike, refer here for an exclusive supplement to this blog.

Appendix

Darktrace model detections

Initial Compromise:

  • Device / New User Agent To Internal Server
  • Anomalous Server Activity / New User Agent from Internet Facing System
  • Experimental / Large Number of Suspicious Successful Connections

Breaches from Critical Devices / DC:

  • Device / Large Number of Model Breaches
  • Antigena / Network / External Threat / Antigena File then New Outbound Block
  • Device / SMB Lateral Movement
  • Experimental / Unusual SMB Script Write V2
  • Compliance / High Priority Compliance Model Breach
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Experimental / Possible Cobalt Strike Server IP V2

Lateral Movement:

  • Antigena / Network / Insider Threat / Antigena Internal Anomalous File Activity
  • Compliance / SMB Drive Write
  • Anomalous File / Internal / Executable Uploaded to DC
  • Experimental / Large Number of Suspicious Failed Connections
  • Compromise / Suspicious Beaconing Behaviour
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block
  • Anomalous Connection / High Volume of Connections to Rare Domain
  • Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Network Scan Activity:

  • Device / Suspicious SMB Scanning Activity
  • Experimental / Network Scan V2
  • Device / ICMP Address Scan
  • Experimental / Possible SMB Scanning Activity
  • Experimental / Possible SMB Scanning Activity V2
  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Device / Network Scan
  • Compromise / DNS / Possible DNS Beacon
  • Device / Internet Facing Device with High Priority Alert
  • Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

DNS / Cobalt Strike Activity:

  • Experimental / Possible Cobalt Strike Server IP
  • Experimental / Possible Cobalt Strike Server IP V2
  • Antigena / Network / External Threat / Antigena File then New Outbound Block
  • Antigena / Network / External Threat / Antigena Suspicious File Block
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / Script from Rare External Location

MITRE ATT&CK techniques observed

IoCs

Thanks to Brianna Leddy, Sam Lister and Marco Alanis for their contributions.

Footnotes

1.

https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-44228
https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-44530
https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-45046
https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-4104

2. https://www.toolbox.com/it-security/threat-reports/news/log4j-vulnerabilities-exploitation-attempts

3. https://twitter.com/ItsReallyNick/status/899845845906071553

4. https://www.mandiant.com/resources/unc2596-cuba-ransomware

5. https://www.ic3.gov/Media/News/2021/211203-2.pdf

6. https://threatpost.com/microsoft-exchange-exploited-cuba-ransomware/178665/

7. https://www.bleepingcomputer.com/news/security/microsoft-exchange-servers-hacked-to-deploy-cuba-ransomware/

8. https://gist.github.com/blotus/f87ed46718bfdc634c9081110d243166

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.
Written by
Dylan Evans

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May 12, 2026

Resilience at the Speed of AI: Defending the Modern Campus with Darktrace

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Why higher education is a different cybersecurity battlefield

After four decades in IT, now serving as both CIO and CISO, I’ve learned one simple truth: cybersecurity is never “done.” It’s a constant game of cat and mouse. Criminals evolve. Technologies advance. Regulations expand. But in higher education, the challenge is uniquely complex.

Unlike a bank or a military installation, we can’t lock down networks to a narrow set of approved applications. Higher education environments are open by design. Students collaborate globally, faculty conduct cutting-edge research, and administrators manage critical operations, all of which require seamless access to the internet, global networks, cloud platforms, and connected systems.

Combine that openness with expanding regulatory mandates and tight budgets, and the balancing act becomes clear.

Threat actors don’t operate under the same constraints. Often well-funded and sponsored by nation-states with significant resources, they’re increasingly organized, strategic, and innovative.

That sophistication shows up in the tactics we face every day, from social engineering and ransomware to AI-driven impersonation attacks. We’re dealing with massive volumes of data, countless signals, and a very small window between detection and damage.

No human team, no matter how talented or how numerous, can manually sift through that noise at the speed required.

Discovering a force multiplier

Nothing in cybersecurity is 100% foolproof. I never “set it and forget it.” But for institutions balancing rising threats and finite resources, the Darktrace ActiveAI Security Platform™ offers something incredibly valuable: peace of mind through speed and scale.

It closes the gap between detection and response in a way humans can’t possibly match. At the speed of light, it can quarantine, investigate, and contain anomalous activity.

I’ve purchased and deployed Darktrace three separate times at three different institutions because I’ve seen firsthand what it can do and what it enables teams like mine to achieve.

I first encountered Darktrace while serving as CIO for a large multi-campus college system. What caught my attention was Darktrace's Self-Learning AI, and its ability to learn what "normal" looked like across our network. Instead of relying solely on static signatures or rigid rules, Darktrace built a behavioral baseline unique to our environment and alerted us in real time when something simply didn’t look right.

In higher education, where strict lockdowns aren’t realistic, that behavioral model made all the difference. We deployed it across five campuses, and the impact was immediate. Operating 24/7, Darktrace surfaced threats in ways our team couldn’t replicate manually.

Over time, the Darktrace platform evolved alongside the changing threat landscape, expanding into intrusion prevention, cloud visibility, and email security. At subsequent institutions, including Washington College, Darktrace was one of my first strategic investments.

Revealing the hidden threat other tools missed

One of the most surprising investigations of my career involved a data leak. Leadership suspected sensitive information from high-level meetings was being exposed, but our traditional tools couldn’t provide any answers.

Using Darktrace’s deep network visibility, down to packet-level data, we traced unusual connections to our CCTV camera system, which had been configured with a manufacturer’s default password. A small group of employees had hacked into the CCTV cameras, accessed audio-enabled recordings from boardroom meetings, and stored copies locally.

No other tool in our environment could have surfaced those connections the way Darktrace did. It was a clear example of why using AI to deeply understand how your organization, systems, and tools normally behave, matters: threats and risks don’t always look the way we expect.

Elevating a D-rating into a A-level security program

When I arrived at my last CISO role, the institution had recently experienced a significant ransomware attack. Attackers located  data  which informed their setting  ransom demands to an amount they knew would likely result in payment. It was a sobering example of how calculated and strategic modern cybercriminals have become.

Third-party cyber ratings reflected that reality, with a  D rating.

To raise the bar, we implemented a comprehensive security program and integrated layered defenses; -deploying state of the art tools and methods-  across the environment, with Darktrace at its core.

After a 90-day learning period to establish our behavioral baseline, we transitioned the platform into fully autonomous mode. In a single 30-day span, Darktrace conducted more than 2,500 investigations and autonomously resolved 92% of all false positives.

For a small team, that’s transformative. Instead of drowning in alerts, my staff focused on less than  200 meaningful cases that warranted human review.

Today, we maintain a perfect A rating from third-party assessors and have remained cybersafe.

Peace of mind isn’t about complacency

The effect of Darktrace as a force multiplier has a real human impact.

With the time reclaimed through automation, we expanded community education programs and implemented simulated phishing exercises. Through sustained training and awareness efforts, we reduced social engineering susceptibility from nearly 45% to under 5%.

On a personal level, Darktrace allows me to sleep better at night and take time off knowing we have intelligent systems monitoring and responding around the clock. For any CIO or CISO carrying institutional risk on their shoulders, that matters.

The next era: AI vs. AI

A new chapter in cybersecurity is unfolding as adversaries leverage AI to enhance scale, speed, and believability. Phishing campaigns are more personalized, impersonation attempts are more precise, and deepfake video technology, including live video, is disturbingly authentic. At the same time, organizations are rapidly adopting AI across their own environments —from GenAI assistants to embedded tools to autonomous agents. These systems don’t operate within fixed rules. They act across email, cloud, SaaS, and identity systems, often with broad permissions, and their behavior can evolve over time in ways that are difficult to predict or control.

That creates a new kind of security challenge. It’s not just about defending against AI-powered threats but understanding and governing how AI behaves within your environment, including what it can access, how it acts, and where risk begins to emerge.

From my perspective, this is a natural next step for Darktrace.

Darktrace brings a level of maturity and behavioral understanding uniquely suited to the complexity of AI environments. Self-Learning AI learns the normal patterns of each business to interpret context, uncover subtle intent, and detect meaningful deviations without relying on predefined rules or signatures. Extending into securing AI by bringing real-time visibility and control to GenAI assistants, AI agents, development environments and Shadow AI, feels like the logical evolution of what Darktrace already does so well.

Just as importantly, Darktrace is already built for dynamic, cross-domain environments where risk doesn’t sit in a single tool or control plane. In higher education, activity already spans multiple systems and, with AI, that interconnection only accelerates.

Having deployed Darktrace multiple times, I have confidence it’s uniquely positioned to lead in this space and help organizations adopt AI with greater visibility and control.

---

Since authoring this blog, Irving Bruckstein has transitioned to the role of Chief Executive Officer of the Cyberaigroup.

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Irving Bruckstein
CEO CyberAIgroup

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May 11, 2026

The Next Step After Mythos: Defending in a World Where Compromise is Expected

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Is Anthropic’s Mythos a turning point for cybersecurity?

Anthropic’s recent announcements around their Mythos model, alongside the launch of Project Glasswing, have generated significant interest across the cybersecurity industry.

The closed-source nature of the Mythos model has understandably attracted a degree of skepticism around some of the claims being made. Additionally, Project Glasswing was initially positioned as a way for software vendors to accelerate the proactive discovery of vulnerabilities in their own code; however, much of the attention has focused on the potential for AI to identify exploitable vulnerabilities for those with malicious intent.

Putting questions around the veracity of those claims to one side – which, for what it’s worth, do appear to be at least partially endorsed by independent bodies such as the UK’s AI Security Institute – this should not be viewed as a critical turning point for the industry. Rather, it reflects the natural direction of travel.

How Mythos affects cybersecurity teams  

At Darktrace, extolling the virtues of AI within cybersecurity is understandably close to our hearts. However, taking a step back from the hype, we’d like to consider what developments like this mean for security teams.

Whether it’s Mythos or another model yet to be released, it’s worth remembering that there is no fundamental difference between an AI discovered vulnerability and one discovered by a human. The change is in the pace of discovery and, some may argue, the lower the barrier to entry.

In the hands of a software developer, this is unquestionably positive. Faster discovery enables earlier remediation and more proactive security. But in the hands of an attacker, the same capability will likely lead to a greater number of exploitable vulnerabilities being used in the wild and, critically, vulnerabilities that are not yet known to either the vendor or the end user.

That said, attackers have always been able to find exploitable vulnerabilities and use them undetected for extended periods of time. The use of AI does not fundamentally change this reality, but it does make the process faster and, unfortunately, more likely to occur at scale.

While tools such as Darktrace / Attack Surface Management and / Proactive Exposure Management  can help security teams prioritize where to patch, the emergence of AI-driven vulnerability discovery reinforces an important point: patching alone is not a sufficient control against modern cyber-attacks.

Rethinking defense for a world where compromise is expected

Rather than assuming vulnerabilities can simply be patched away, defenders are better served by working from the assumption that their software is already vulnerable - and always will be -and build their security strategy accordingly.

Under that assumption, defenders should expect initial access, particularly across internet exposed assets, to become easier for attackers. What matters then is how quickly that foothold is detected, contained, and prevented from expanding.

For defenders, this places renewed emphasis on a few core capabilities:

  • Secure-by-design architectures and blast radius reduction, particularly around identity, MFA, segmentation, and Zero Trust principles
  • Early, scalable detection and containment, favoring behavioral and context-driven signals over signatures alone
  • Operational resilience, with the expectation of more frequent early-stage incidents that must be managed without burning out teams

How Darktrace helps organizations proactively defend against cyber threats

At Darktrace, we support security teams across all three of these critical capabilities through a multi-layered AI approach. Our Self-Learning AI learns what’s normal for your organization, enabling real-time threat detection, behavioral prediction, incident investigation and autonomous response. - all while empowering your security team with visibility and control.

To learn more about Darktrace’s application of AI to cybersecurity download our White Paper here.  

Reducing blast radius through visibility and control

Secure-by-design principles depend on understanding how users, devices, and systems behave. By learning the normal patterns of identity and network activity, Darktrace helps teams identify when access is being misused or when activity begins to move beyond expected boundaries. This makes it possible to detect and contain lateral movement early, limiting how far an attacker can progress even after initial access.

Detecting and containing threats at the earliest stage  

As AI accelerates vulnerability discovery, defenders need to identify exploitation before it is formally recognized. Darktrace’s behavioral understanding approach enables detection of subtle deviations from normal activity, including those linked to previously unknown vulnerabilities.

A key example of this is our research on identifying cyber threats before public CVE disclosures, demonstrating that assessing activity against what is normal for a specific environment, rather than relying on predefined indicators of compromise, enables detection of intrusions exploiting previously unknown vulnerabilities days or even weeks before details become publicly available.

Additionally, our Autonomous Response capability provides fast, targeted containment focused on the most concerning events, while allowing normal business operations to continue. This has consistently shown that even when attackers use techniques never seen before, Darktrace’s Autonomous Response can contain threats before they have a chance to escalate.

Scaling response without increasing operational burden

As early-stage incidents become more frequent, the ability to investigate and respond efficiently becomes critical. Darktrace’s Cyber AI Analyst’s AI-driven investigation capabilities automatically correlate activity across the environment, prioritizing the most significant threats and reducing the need for manual triage. This allows security teams to respond faster and more consistently, without increasing workload or burnout.

What effective defense looks like in an AI-accelerated landscape

Developments like Mythos highlight a reality that has been building for some time: the window between exposure and exploitation is shrinking, and in many cases, it may disappear entirely. In that environment, relying on patching alone becomes increasingly reactive, leaving little room to respond once access has been established.

The more durable approach is to assume that compromise will occur and focus on controlling what happens next. That means identifying early signs of misuse, containing threats before they spread, and maintaining visibility across the environment so that isolated signals can be understood in context.

AI plays a role on both sides of this equation. While it enables attackers to move faster, it also gives defenders the ability to detect subtle changes in behavior, prioritize what matters, and respond in real time. The advantage will not come from adopting AI in isolation, but from applying it in a way that reduces the gap between detection and action.

AI may be accelerating parts of the attack lifecycle, but the fundamentals of defense, detection, and containment still apply. If anything, they matter more than ever – and AI is just as powerful a tool for defenders as it is for attackers.

To learn more about Darktrace and Mythos read more on our blog: Mythos vs Ethos: Defending in an Era of AI‑Accelerated Vulnerability Discovery

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About the author
Toby Lewis
Head of Threat Analysis
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