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August 4, 2021

Detecting a Cobalt Strike Attack With Darktrace AI

See how Darktrace AI was able to detect Cobalt Strike attacks by identifying anomalous connections and performing automated network reconnaissance.
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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager
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04
Aug 2021

Since its release in 2012, Cobalt Strike has become a popular platform for red teams and ethical hackers. Robust and reliable software combined with innovative features such as DNS tunnelling, lateral movement tools for privilege escalation, and PowerShell support, have made it a desirable option for organizations wanting to test their own cyber defenses. As the framework was previously only available with a commercial license, it gave security teams a distinct advantage over threat actors when preparing for attacks.

That all changed in late 2020, when a GitHub repository appeared hosting a decompiled version of the framework. Users claimed that the leaked platform did indeed function similarly, if not identically, to the commercial version, and even included a commented-out licensing check. This suddenly made the software readily available, and highly appealing for cyber-criminals: rather than requiring a paper trail and licensing, its source code was freely available for customization and use in offensive campaigns.

With sophisticated capabilities of subtle command and control (C2), privilege escalation, and lateral movement, the tools have become a favorite for ransomware gangs. Even prior to the reporting of the leaked version, 66% of ransomware attacks were found to use Cobalt Strike.

Overview of a Cobalt Strike attack

Cobalt Strike has distinctive TTPs (tools, techniques and procedures) and evasive features for each stage of the attack.

Figure 1: Cyber kill chain with Cobalt Strike

Initial compromise can be achieved with a native module for modifying emails. This includes the insertion of malicious links into existing emails or the creation of convincing spear phishing emails.

The initial payload is intentionally lightweight and can be delivered from cheaply hosted infrastructure. The smaller file size is easier to obfuscate and can be implemented in several ways, including injection into libraries or trusted processes, or creating a series of persistence mechanisms (such as turning off anti-virus prior to downloading the full payload). As such, it is remarkably difficult to detect with blocking rules or signatures.

Network reconnaissance can be done through a variety of subtle methods, using commonly used protocols such as DNS and DCE-RPC to interrogate the network. These services are frequently used in legitimate operations, so it is challenging to apply sufficiently strict controls to prevent this stage of the attack.

Lateral movement and privilege escalation are easily accessible with pre-packaged versions of common attack tools such as Mimikatz. They can interrogate an Active Directory (AD) or steal credentials, while also using SMB pipes for peer-to-peer C2. There is little space for perimeter-based security controls to monitor and restrict these abuses, even if sufficiently granular controls could be imposed.

Payload execution is a straightforward matter as Cobalt Strike beacon allows the delivery of effectively arbitrary payloads, including portability for ransomware. As the previous evasive steps can afford the attacker privileged credentials, the deployment of such payloads could look like non-threatening administrative behavior.

AI detections

Initial compromise

Cobalt Strike has utilities for creating spear phishing documents. As email remains a prolific source of perimeter breaches, threat actors will frequently implant the tool through phishes.

One such example was detected by Darktrace’s AI at Canadian manufacturer in June 2021. The compromise started when an end user appeared to open a phishing document, evidenced by connections to Adobe and VeriSign shortly prior to an HTTP connection to a rare external IP address.

A packet capture of the anomalous connection revealed the creation of an object using a base64 encoded string – a common obfuscation technique. If the customer had been using Darktrace/Email, the threat would have been nullified before it hit the mailbox.

Shortly after the HTTP connection, Darktrace identified unusual use of SSL, which appears to have been leveraged to upgrade to HTTPS using self-signed certificates. The endpoint served an executable, which was later confirmed as a Cobalt Strike beacon based on open-source intelligence (OSINT). Such beacons are supported by the framework, with a variety of common C2 protocols available to the attacker.

Figure 2: Event log for ‘Patient Zero’ of a Sodinokibi infection

Darktrace’s detection was based on the anomalous nature of the connection (suspicious violations of standard SSL protocols) and not a pre-defined rule. The initial compromise was detected in a matter of minutes.

Network reconnaissance

In another example at a Swiss telecommunications company in April 2021, Darktrace alerted the security team that a device – normally used for data collection – was engaging in suspicious lateral movement activity.

The host was abusing privileged credentials to perform AD reconnaissance and SMB enumeration. The alert then prompted a broader investigation, revealing that multiple devices, including domain controllers, were compromised with IoCs related to Cobalt Strike.

Thanks to Darktrace’s deep understanding of the business and recognition that this behavior was anomalous, the security team were able to remediate the infection before file encryption or large data exfiltration had occurred.

Privilege escalation and ransomware deployment

In a ransomware attack against a South African insurance company in May 2021, where a phishing email resulted in the deployment of ransomware, Darktrace first identified the creation of new administrative credentials. The devices which used the credentials were then seen making anomalous connections to various C2 endpoints associated with Cobalt Strike beacons.

Darktrace enabled the rapid identification of compromised hosts, which in turn allowed for a faster remediation and mitigated fears of a resurgent infection.

Cyber AI Analyst performed a machine-speed investigation of the activity, and automatically produced a report highlighting unusual connections on TCP port 4444 as well as other mail related ports. Port 4444 is the default port for Metasploit, another hacking platform which is often seen in conjunction with Cobalt Strike beacon. It then presented the human analysts with a full list of compromised hosts.

Figure 3: Cyber AI Analyst summary of an affected host using non-standard ports for C2 and subsequently scanning the network

Cobalt Strike malware

As it appears that a cheaply accessible analog of Cobalt Strike has been leaked, detection of the framework is critical to defend against active attackers. Signatures and rule-based restrictions prove ineffective in this regard, as the framework was designed specifically to evade such tools.

Darktrace offers the capability to detect malicious activity in its earliest stages, to triage at the speed of AI, and to autonomously block the proliferation of active threats.

Thanks to Darktrace analyst Roberto Romeu for his insights on the above threat find.

Learn how Darktrace caught APT41 leveraging Cobalt Strike

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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager

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April 21, 2026

How a Compromised eScan Update Enabled Multi‑Stage Malware and Blockchain C2

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The Rise of supply chain attacks

In recent years, the abuse of trusted software has become increasingly common, with supply chain compromises emerging as one of the fastest growing vectors for cyber intrusions. As highlighted in Darktrace’s Annual Threat Report 2026, attackers and state-actors continue to find significant value in gaining access to networks through compromised trusted links, third-party tools, or legitimate software. In January 2026, a supply chain compromise affecting MicroWorld Technologies’ eScan antivirus product was reported, with malicious updates distributed to customers through the legitimate update infrastructure. This, in turn, resulted in a multi‑stage loader malware being deployed on compromised devices [1][2].

An overview of eScan exploitation

According to eScan’s official threat advisory, unauthorized access to a regional update server resulted in an “incorrect file placed in the update distribution path” [3]. Customers associated with the affected update servers who downloaded the update during a two-hour window on January 20 were impacted, with affected Windows devices subsequently have experiencing various errors related to update functions and notifications [3].

While eScan did not specify which regional update servers were affected by the malicious update, all impacted Darktrace customer environments were located in the Europe, Middle East, and Africa (EMEA) region.

External research reported that a malicious 32-bit executable file , “Reload.exe”, was first installed on affected devices, which then dropped the 64-bit downloader, “CONSCTLX.exe”. This downloader establishes persistence by creating scheduled tasks such as “CorelDefrag”, which are responsible for executing PowerShell scripts. Subsequently, it evades detection by tampering with the Windows HOSTS file and eScan registry to prevent future remote updates intended for remediation. Additional payloads are then downloaded from its command-and-control (C2) server [1].

Darktrace’s Coverage of eScan Exploitation

Initial Access and Blockchain as multi-distributed C2 Infrastructure

On January 20, the same day as the aforementioned two‑hour exploit window, Darktrace observed multiple devices across affected networks downloading .dlz package files from eScan update servers, followed by connections to an anomalous endpoint, vhs.delrosal[.]net, which belongs to the attackers’ C2 infrastructure.

The endpoint contained a self‑signed SSL certificate with the string “O=Internet Widgits Pty Ltd, ST=SomeState, C=AU”, a default placeholder commonly used in SSL/TLS certificates for testing and development environments, as well as in malicious C2 infrastructure [4].

Utilizing a multi‑distributed C2 infrastructure, the attackers also leveraged domains linked with the Solana open‑source blockchain for C2 purposes, namely “.sol”. These domains were human‑readable names that act as aliases for cryptocurrency wallet addresses. As browsers do not natively resolve .sol domains, the Solana Naming System (formerly known as Bonfida, an independent contributor within the Solana ecosystem) provides a proxy service, through endpoints such as sol-domain[.]org, to enable browser access.

Darktrace observed devices connecting to blackice.sol-domain[.]org, indicating that attackers were likely using this proxy to reach a .sol domain for C2 activity. Given this behavior, it is likely that the attackers leveraged .sol domains as a dead drop resolver, a C2 technique in which threat actors host information on a public and legitimate service, such as a blockchain. Additional proxy resolver endpoints, such as sns-resolver.bonfida.workers[.]dev, were also observed.

Solana transactions are transparent, allowing all activity to be viewed publicly. When Darktrace analysts examined the transactions associated with blackice[.]sol, they observed that the earliest records dated November 7, 2025, which coincides with the creation date of the known C2 endpoint vhs[.]delrosal[.]net as shown in WHOIS Lookup information [4][5].

WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.
Figure 1: WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.
 Earliest observed transaction record for blackice[.]sol on public ledgers.
Figure 2: Earliest observed transaction record for blackice[.]sol on public ledgers.

Subsequent instructions found within the transactions contained strings such as “CNAME= vhs[.]delrosal[.]net”, indicating attempts to direct the device toward the malicious endpoint. A more recent transaction recorded on January 28 included strings such as “hxxps://96.9.125[.]243/i;code=302”, suggesting an effort to change C2 endpoints. Darktrace observed multiple alerts triggered for these endpoints across affected devices.

Similar blockchain‑related endpoints, such as “tumama.hns[.]to”, were also observed in C2 activities. The hns[.]to service allows web browsers to access websites registered on Handshake, a decentralized blockchain‑based framework designed to replace centralized authorities and domain registries for top‑level domains. This shift toward decentralized, blockchain‑based infrastructure likely reflects increased efforts by attackers to evade detection.

In outgoing connections to these malicious endpoints across affected networks, Darktrace / NETWORK recognized that the activity was 100% rare and anomalous for both the devices and the wider networks, likely indicative of malicious beaconing, regardless of the underlying trusted infrastructure. In addition to generating multiple model alerts to capture this malicious activity across affected networks, Darktrace’s Cyber AI Analyst was able to compile these separate events into broader incidents that summarized the entire attack chain, allowing customers’ security teams to investigate and remediate more efficiently. Moreover, in customer environments where Darktrace’s Autonomous Response capability was enabled, Darktrace took swift action to contain the attack by blocking beaconing connections to the malicious endpoints, even when those endpoints were associated with seemingly trustworthy services.

Conclusion

Attacks targeting trusted relationships continue to be a popular strategy among threat actors. Activities linked to trusted or widely deployed software are often unintentionally whitelisted by existing security solutions and gateways. Darktrace observed multiple devices becoming impacted within a very short period, likely because tools such as antivirus software are typically mass‑deployed across numerous endpoints. As a result, a single compromised delivery mechanism can greatly expand the attack surface.

Attackers are also becoming increasingly creative in developing resilient C2 infrastructure and exploiting legitimate services to evade detection. Defenders are therefore encouraged to closely monitor anomalous connections and file downloads. Darktrace’s ability to detect unusual activity amidst ever‑changing tactics and indicators of compromise (IoCs) helps organizations maintain a proactive and resilient defense posture against emerging threats.

Credit to Joanna Ng (Associate Principal Cybersecurity Analyst) and Min Kim (Associate Principal Cybersecurity Analyst) and Tara Gould (Malware Researcher Lead)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

  • Anomalous File::Zip or Gzip from Rare External Location
  • Anomalous Connection / Suspicious Self-Signed SSL
  • Anomalous Connection / Rare External SSL Self-Signed
  • Anomalous Connection / Suspicious Expired SSL
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device

List of Indicators of Compromise (IoCs)

  • vhs[.]delrosal[.]net – C2 server
  • tumama[.]hns[.]to – C2 server
  • blackice.sol-domain[.]org – C2 server
  • 96.9.125[.]243 – C2 Server

MITRE ATT&CK Mapping

  • T1071.001 - Command and Control: Web Protocols
  • T1588.001 - Resource Development
  • T1102.001 - Web Service: Dead Drop Resolver
  • T1195 – Supple Chain Compromise

References

[1] https://www.morphisec.com/blog/critical-escan-threat-bulletin/

[2] https://www.bleepingcomputer.com/news/security/escan-confirms-update-server-breached-to-push-malicious-update/

[3] hxxps://download1.mwti.net/documents/Advisory/eScan_Security_Advisory_2026[.]pdf

[4] https://www.virustotal.com/gui/domain/delrosal.net

[5] hxxps://explorer.solana[.]com/address/2wFAbYHNw4ewBHBJzmDgDhCXYoFjJnpbdmeWjZvevaVv

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About the author
Joanna Ng
Senior Cyber Analyst

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April 17, 2026

Why Behavioral AI Is the Answer to Mythos

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How AI is breaking the patch-and-prevent security model

The business world was upended last week by the news that Anthropic has developed a powerful new AI model, Claude Mythos, which poses unprecedented risk because of its ability to expose flaws in IT systems.  

Whether it’s Mythos or OpenAI’s GPT-5.4-Cyber, which was just announced on Tuesday, supercharged AI models in the hands of hackers will allow them to carry out attacks at machine speed, much faster than most businesses can stop them.  

This news underscores a stark reality for all leaders: Patching holes alone is not a sufficient control against modern cyberattacks. You must assume that your software is already vulnerable right now. And while LLMs are very good at spotting vulnerabilities, they’re pretty bad at reliably patching them.

Project Glasswing members say it could take months or years for patches to be applied. While that work is done, enterprises must be protected against Zero-Day attacks, or security holes that are still undiscovered.  

Most cybersecurity strategies today are built like a daily multivitamin: broad, preventative, and designed to keep the system generally healthy over time. Patch regularly. Update software. Reduce known vulnerabilities. It’s necessary, disciplined, and foundational. But it’s also built for a world where the risks are well known and defined, cycles are predictable, and exposure unfolds at a manageable pace.

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

The vulnerabilities exposed by AI systems like Mythos aren’t the well-understood risks your “multivitamin” was designed to address. They are transient, fast-emerging entry points that exist just long enough to be exploited.

In that environment, prevention alone isn’t enough. You don’t need more vitamins—you need a painkiller. The future of cybersecurity won’t be defined by how well you maintain baseline health. It will be defined by how quickly you respond when something breaks and every second counts.

That’s why behavioral AI gives businesses a durable cyber advantage. Rather than trying to figure out what the attacker looks like, it learns what “normal” looks like across the digital ecosystem of each individual business.  

That’s exactly how behavioral AI works. It understands the self, or what's normal for the organization, and then it can spot deviations in from normal that are actually early-stage attacks.

The Darktrace approach to cybersecurity

At Darktrace, we’ve been defending our 10,000 customers using behavioral AI cybersecurity developed in our AI Research Centre in Cambridge, U.K.

Darktrace was built on the understanding that attacks do not arrive neatly labeled, and that the most damaging threats often emerge before signatures, indicators, or public disclosures can catch up.  

Our AI algorithms learn in real time from your personalized business data to learn what’s normal for every person and every asset, and the flows of data within your organization. By continuously understanding “normal” across your entire digital ecosystem, Darktrace identifies and contains threats emerging from unknown vulnerabilities and compromised supply chain dependencies, autonomously curtailing attacks at machine speed.  

Security for novel threats

Darktrace is built for a world where AI is not just accelerating attacks, but fundamentally reshaping how they originate. What makes our AI so unique is that it's proven time and again to identify cyber threats before public vulnerability disclosures, such as critical Ivanti vulnerabilities in 2025 and SAP NetWeaver exploitations tied to nation-state threat actors.  

As AI reshapes how vulnerabilities are found and exploited, cybersecurity must be anchored in something more durable than a list of known flaws. It requires a real-time understanding of the business itself: what belongs, what does not, and what must be stopped immediately.

What leaders should do right now

The leadership priority must shift accordingly.

First, stop treating unknown vulnerabilities as an edge case. AI‑driven discovery makes them the norm. Security programs built primarily around known flaws, signatures, and threat intelligence will always lag behind an attacker that is operating in real time.

Second, insist on an understanding of what is actually normal across the business. When threats are novel, labels are useless. The earliest and most reliable signal of danger is abnormal behavior—systems, users, or data flows that suddenly depart from what is expected. If you cannot see that deviation as it happens, you are effectively blind during the most critical window.

Finally, assume that the next serious incident will occur before remediation guidance is available. Ask what happens in those first minutes and hours. The organizations that maintain resilience are not the ones waiting for disclosure cycles to catch up—they are the ones that can autonomously identify and contain emerging threats as they unfold.

This is the reality of cybersecurity in an AI‑shaped world. Patching and prevention remain important foundations, but the advantage now belongs to those who can respond instantly when the unpredictable occurs.

Behavioral AI is security designed not just for known threats, but for the ones that AI will discover next.

[related-resource]

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About the author
Ed Jennings
President and CEO
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