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April 1, 2020

How AI Caught APT41 Exploiting Vulnerabilities

Analyzing how the cyber-criminal group APT41 exploited a zero-day vulnerability, we show how Darktrace’s AI detected and investigated the threat immediately.
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
Max Heinemeyer
Global Field CISO
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01
Apr 2020

Executive summary

  • Darktrace detected several highly targeted attacks in early March, well before any associated signatures had become available. Two weeks later, the attacks were attributed to Chinese threat-actor APT41.
  • APT41 exploited the Zoho ManageEngine zero-day vulnerability CVE-2020-10189. Darktrace automatically detected and reported on the attack in its earliest stages, enabling customers to contain the threat before it could make an impact.
  • The intrusions described here were part of a wider campaign aiming to gain initial access to as many companies as possible during the window of opportunity presented by CVE-2020-10189.
  • The reports generated by Darktrace highlighted and delineated every aspect of the incident in the form of a meaningful security narrative. Even a junior responder could have reviewed this output and acted on this zero-day APT attack in under 5 minutes.

Fighting APT41’s global attack

In early March, Darktrace detected several advanced attacks targeting customers in the US and Europe. A majority of these customers are in the legal sector. The attacks shared the same Techniques, Tools & Procedures (TTPs), targeting public-facing servers and exploiting recent high-impact vulnerabilities. Last week, FireEye attributed this suspicious activity to the Chinese cyber espionage group APT41.

This campaign used the Zoho ManageEngine zero-day vulnerability CVE-2020-10189 to get access to various companies, but little to no follow-up was detected after initial intrusion. This activity indicates a broad-brush campaign to get initial access to as many target companies as possible during the zero-day window of opportunity.

The malicious activity observed by Darktrace took place late on Sunday March 8, 2020 and in the morning of March 9, 2020 (UTC), broadly in line with office hours previously attributed to the Chinese cyber espionage group APT41.

The graphic below shows an exemplary timeline from one of the customers targeted by APT41. The attacks observed in other customer environments are identical.

Timeline of the APT41 attack
Figure 1: A timeline of the attack

Technical analysis

The attack described here centered around the Zoho ManageEngine zero-day vulnerability CVE-2020-10189. Most of the attack appears to have been automated.

We observed the initial intrusion, several follow-up payload downloads, and command and control (C2) traffic. In all cases, the activity was contained before any later steps in the attack lifecycle, such as lateral movement or data exfiltration, were identified.

The below screenshot shows an overview of the key AI Analyst detections reported. Not only did it report on the SSL and HTTP C2 traffic, but it also reported on the payload downloads:

Cyber AI Analyst breaks down the APT41 attack
Figure 2: SSL C2 detection by Cyber AI Analyst
Figure 3: Payload detection by Cyber AI Analyst

Initial compromise

The initial compromise began with the successful exploitation of the Zoho ManageEngine zero-day vulnerability CVE-2020-10189. Following the initial intrusion, the Microsoft BITSAdmin command line tool was used to fetch and install a malicious Batch file, described below:

install.bat (MD5: 7966c2c546b71e800397a67f942858d0) from infrastructure 66.42.98[.]220 on port 12345.

Source: 10.60.50.XX
Destination: 66.42.98[.]220
Destination Port: 12345
Content Type: application/x-msdownload
Protocol: HTTP
Host: 66.42.98[.]220
URI: /test/install.bat
Method: GET
Status Code: 200

Figure 4: Outbound connection fetching batch file

Shortly after the initial compromise, the first stage Cobalt Strike Beacon LOADER was downloaded.

Cobalt Strike Beacon loader screenshot
Figure 5: Detection of the Cobalt Strike Beacon LOADER

Command and Control traffic

Interestingly, TeamViewer activity and the download of Notepad++ was taking place at the same time as the C2 traffic was starting in some of the customer attacks. This indicates APT41 trying to use familiar tools instead of completely ‘Living off the Land’.

Storesyncsvc.dll was a Cobalt Strike Beacon implant (trial-version) which connected to exchange.dumb1[.]com. A successful DNS resolution to 74.82.201[.]8 was identified, which Darktrace discerned as a successful SSL connection to a hostname with Dynamic DNS properties.

Multiple connections to exchange.dumb1[.]com were identified as beaconing to a C2 center. This C2 traffic to the initial Cobalt Strike Beacon was leveraged to download a second stage payload.

Interestingly, TeamViewer activity and the download of Notepad++ was taking place at the same time as the C2 traffic was starting in some of the customer attacks. This indicates APT41 trying to use familiar tools instead of completely ‘Living off the Land’. There is at least high certainty that the use of these two tools can be attributed to this intrusion instead of regular business activity. Notepad++ was not normally used in the target customers’ environments, nor was TeamViewer – in fact, the use of both applications was 100% unusual for the targeted organizations.

Attack tools download

CertUtil.exe, a command line program installed as part of Certificate Services, was then leveraged to connect externally and download the second stage payload.

Detection associated with Meterpreter activity

Figure 6: Darktrace detecting the usage of CertUtil

A few hours after this executable download, the infected device made an outbound HTTP connection requesting the URI /TzGG, which was identified as Meterpreter downloading further shellcode for the Cobalt Strike Beacon.

Figure 7: Detection associated with Meterpreter activity. No lateral movement or significant data exfiltration was observed.

How Cyber AI Analyst reported on the zero-day exploit

Darktrace not only detected this zero-day attack campaign, but Cyber AI Analyst also saved security teams valuable time by investigating disparate security events and generating a report that immediately put them in a position to take action.

The below screenshot shows the AI Analyst incidents reported in one infected environment, over the eight days covering the intrusion period. The first incident on the left represents the APT activity described here. The other five incidents are independent of the APT activity and not as severe.

AI Analyst Security Incidents
Figure 8: The security incidents surfaced by AI Analyst

AI Analyst reported on six incidents in total over the eight-day period. Each AI Analyst incident includes a detailed timeline and summary of the incident, in a concise format that takes an average of two minutes to review. This means that with Cyber AI Analyst, even a non-technical person could have actioned a response to this sophisticated, zero-day incident in less than five minutes.

Conclusion

Without public Indicators of Compromise (IoCs) or any open-source intelligence available, targeted attacks are incredibly difficult to detect. Moreover, even the best detections are useless if they cannot be actioned by a security analyst at an early stage. Too often this occurs because of an overwhelming volume of alerts, or simply because the skills barrier to triage and investigation is too high.

This appears to be a broad campaign to gain initial access to many different companies and sectors. While very sophisticated in nature, the threat sacrificed stealth for speed by targeting many companies at the same time. APT41 wanted to utilize the limited window of opportunity that the Zoho zero-day provided before IT staff starts patching.

Darktrace’s Cyber AI is specifically designed to detect the subtle signs of targeted, unknown attacks at an early stage, without relying on prior knowledge or IoCs. It achieves this by continuously learning the normal patterns of behavior for every user, device, and associated peer group from scratch, and ‘on the job’.

In the face of this zero-day attack campaign, the AI’s ability to (a) detect unknown threats with self-learning AI and (b) augment strained responders with AI-driven investigations and reporting proved crucial. Indeed, it ensured that the attacks were swiftly contained before escalating to the later stages of the attack lifecycle.

Indicators of Compromise

Selection of Darktrace model breaches:

  • Anomalous File / Script from Rare External
  • Anomalous File / EXE from Rare External Location
  • Compromise / SSL to DynDNS
  • Compliance / CertUtil External Connection
  • Anomalous Connection / CertUtil Requesting Non Certificate
  • Anomalous Connection / CertUtil to Rare Destination
  • Anomalous Connection / New User-Agent to IP Without Hostname
  • Device / Initial Breach Chain Compromise
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / Beaconing Activity To External Rare
  • Anomalous File / Numeric Exe Download
  • Device / Large Number of Model Breaches
  • Anomalous Server Activity / Rare External from Server
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Compliance / Remote Management Tool On Server

The below screenshot shows Darktrace model breaches occurring together during the compromise of one customer:

Figure 9: Darktrace model breaches occurring together

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
Max Heinemeyer
Global Field CISO

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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.

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

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About the author
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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Toby Lewis
Head of Threat Analysis
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