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April 17, 2024

Cerber Ransomware: Dissecting the three heads

Cerber ransomware's Linux variant is actively exploiting CVE-2023-22518 in Confluence servers. It uses three UPX-packed C++ payloads: a primary stager, a log checker for environment assessment, and an encryptor that renames files with a .L0CK3D extension.
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
Nate Bill
Threat Researcher
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17
Apr 2024

Introduction: Cerber ransomware

Researchers at Cado Security Labs (now part of Darktrace) received reports of the Cerber ransomware being deployed onto servers running the Confluence application via the CVE-2023-22518 exploit. [1] There is a large amount of coverage on the Windows variant, however there is very little about the Linux variant. This blog will discuss an analysis of the Linux variant. 

Cerber emerged and was at the peak of its activity around 2016, and has since only occasional campaigns, most recently targeting the aforementioned Confluence vulnerability. It consists of three highly obfuscated C++ payloads, compiled as a 64-bit Executable and Linkable Format (ELF, the format for executable binary files on Linux) and packed with UPX. UPX is a very common packer used by many threat actors. It allows the actual program code to be stored encoded in the binary, and at runtime extracted into memory and executed (“unpacked”). This is done to prevent software from scanning the payload and detecting the malware.

Pure C++ payloads are becoming less common on Linux, with many threat actors now employing newer programming languages such as Rust or Go. [2] This is likely due to the Cerber payload first being released almost 8 years ago. While it will have certainly received updates, the language and tooling choices are likely to have stuck around for the lifetime of the payload.

Initial access

Cado researchers observed instances of the Cerber ransomware being deployed after a threat actor leveraged CVE-2023-22518 in order to gain access to vulnerable instances of Confluence [3]. It is an improper authorization vulnerability that allows an attacker to reset the Confluence application and create a new administrator account using an unprotected configuration restore endpoint used by the setup wizard.

[19/Mar/2024:15:57:24 +0000] - http-nio-8090-exec-10 13.40.171.234 POST /json/setup-restore.action?synchronous=true HTTP/1.1 302 81796ms - - python-requests/2.31.0 
[19/Mar/2024:15:57:24 +0000] - http-nio-8090-exec-3 13.40.171.234 GET /json/setup-restore-progress.action?taskId= HTTP/1.1 200 108ms 283 - python-requests/2.31.0 

Once an administrator account is created, it can be used to gain code execution by uploading & installing a malicious module via the admin panel. In this case, the Effluence web shell plugin is directly uploaded and installed, which provides a web UI for executing arbitrary commands on the host.

Web Shell recreation
Figure 1: Recreation of installing a web shell on a Confluence instance

The threat actor uses this web shell to download and run the primary Cerber payload. In a default install, the Confluence application is executed as the “confluence” user, a low privilege user. As such, the data the ransomware is able to encrypt is limited to files owned by the confluence user. It will of course succeed in encrypting the datastore for the Confluence application, which can store important information. If it was running as a higher privilege user, it would be able to encrypt more files, as it will attempt to encrypt all files on the system.

Primary payload

Summary of payload:

  • Written in C++, highly obfuscated, and packed with UPX
  • Serves as a stager for further payloads
  • Uses a C2 server at 45[.]145[.]6[.]112 to download and unpack further payloads
  • Deletes itself off disk upon execution

The primary payload is packed with UPX, just like the other payloads. Its main purpose is to set up the environment and grab further payloads in order to run.

Upon execution it unpacks itself and tries to create a file at /var/lock/0init-ld.lo. It is speculated that this was meant to serve as a lock file and prevent duplicate execution of the ransomware, however if the lock file already exists the result is discarded, and execution continues as normal anyway. 

It then connects to the (now defunct) C2 server at 45[.]145[.]6[.]112 and pulls down the secondary payload, a log checker, known internally as agttydck. It does this by doing a simple GET /agttydcki64 request to the server using HTTP and writing the payload body out to /tmp/agttydck.bat. It then executes it with /tmp and ck.log passed as arguments. The execution of the payload is detailed in the next section.

Once the secondary payload has finished executing, the primary payload checks if the log file at /tmp/ck.log it wrote exists. If it does, it then proceeds to delete itself and agttydcki64 from the disk. As it is still running in memory, it then downloads the encryptor payload, known internally as agttydcb, and drops it at /tmp/agttydcb.bat. The packing on this payload is more complex. The file command reports it as a DOS executable and the bat extension would imply this as well. However, it does not have the correct magic bytes, and the high entropy of the file suggests that it is potentially encoded or encrypted. Indeed, the primary payload reads it in and then writes out a decoded ELF file back using the same stream, overwriting the content. It is unclear the exact mechanism used to decode agttydcb. The primary payload then executes the decoded agttydcb, the behavior of which is documented in a later section.

2283  openat(AT_FDCWD, "/tmp/agttydcb.bat", O_RDWR) = 4 
2283  read(4, "\353[\254R\333\372\22,\1\251\f\235 'A>\234\33\25E3g\335\0252\344vBg\177\356\321"..., 450560) = 450560 
2283  lseek(4, 0, SEEK_SET)             = 0 
2283  write(4, "\177ELF\2\1\1\0\0\0\0\0\0\0\0\0\2\0>\0\1\0\0\0X\334F\0\0\0\0\0"..., 450560) = 450560 
2283  close(4)                          = 0 

Truncated strace output for the decoding process

Log check payload - agttydck

Summary of payload:

  • Written in C++, highly obfuscated, and packed with UPX
  • Tries to write the phrase “success” to a given file passed in arguments
  • Likely a check for sandboxing, or to check the permission level of the malware on the system

The log checker payload, agttydck, likely serves as a permission checker. It is a very simple payload and was easy to analyze statically despite the obfuscation. Like the other payloads, it is UPX packed.

When run, it concatenates each argument passed to it and delimits with forward slashes in order to obtain a full path. In this case, it is passed /tmp and ck.log, which becomes /tmp/ck.log. It then tries to open this file in write mode, and if it succeeds writes the word “success” and returns 0. If it does not succeed, it returns 1.

cleaned-up routine
Figure 2: Cleaned-up routine that writes out the success phrase

The purpose of this check isn’t exactly clear. It could be to check if the tmp directory is writable and that it can write, which may be a check for if the system is too locked down for the encryptor to work. Given the check is run in a process separate to the primary payload, it could also be an attempt to detect sandboxes that may not handle files correctly, resulting in the primary payload not being told about the file created by the child.

Encryptor - agttydck

Summary of payload:

  • Written in C++, highly obfuscated, and packed with UPX
  • Writes log file /tmp/log.0 on start and /tmp/log.1 on completion, likely for debugging
  • Walks the root directory looking for directories it can encrypt
  • Writes a ransom note to each directory
  • Overwrites all files in directory with their encrypted content and adds a .L0CK3D extension

The encryptor, agttydcb, achieves the goal of the ransomware, which is to encrypt files on the filesystem. Like the other payloads, it is UPX packed and written with heavily obfuscated C++. Upon launch, it deletes itself off disk so as to not leave any artefacts. It then creates a file at /tmp/log.0, but with no content. As it creates a second file at /tmp/log.1 (also with no content) after encryption finishes, it is possible these were debug markers that the attacker mistakenly left in.

The encryptor then spawns a new thread to do the actual encryption. The payload attempts to write a ransom note at /<directory>/read-me3.txt. If it succeeds, it will walk all files in the directory and attempt to encrypt them. If it fails, it moves on to the next directory. The encryptor chooses to pick which directories to encrypt by walking the root file system. For example, it will try to encrypt /usr, and then /var, etc.

Cerber ransom note
Figure 3: Ransom note left by Cerber

When it has identified a file to encrypt, it opens a read-write file stream to the file and reads in the entire file. It is then encrypted in memory before it seeks to the start of the stream and writes the encrypted data, overwriting the file content, and rendering the file fully encrypted. It then renames the file to have the .L0CK3D extension. Rewriting the same file instead of making a new file and deleting the old one is useful on Linux as directories may be set to append only, preventing the outright deletion of files. Rewriting the file may also rewrite the data on the underlying storage, making recovery with advanced forensics also impossible.

2290  openat(AT_FDCWD, "/home/ubuntu/example", O_RDWR) = 6 
2290  read(6, "file content"..., 3691) = 3691 
2290  write(6, "\241\253\270'\10\365?\2\300\304\275=\30B\34\230\254\357\317\242\337UD\266\362\\\210\215\245!\255f"
..., 3691) = 3691 
2290  close(6)                          = 0 
2290  rename("/home/ubuntu/example", "/home/ubuntu/example.L0CK3D") = 0 

Truncated strace of the encryption process

Once this finishes, it tries to delete itself again (which fails as it already deleted itself) and creates /tmp/log.1. It then gracefully exits. Despite the ransom note claiming the files were exfiltrated, Cado researchers did not observe any behavior that showed this.

Conclusion

Cerber is a relatively sophisticated, albeit aging, ransomware payload. While the use of the Confluence vulnerability allows it to compromise a large amount of likely high value systems, often the data it is able to encrypt will be limited to just the confluence data and in well configured systems this will be backed up. This greatly limits the efficacy of the ransomware in extracting money from victims, as there is much less incentive to pay up.

IoCs

The payloads are packed with UPX so will match against existing UPX Yara rules.

Hashes (sha256)

cerber_primary 4ed46b98d047f5ed26553c6f4fded7209933ca9632b998d265870e3557a5cdfe

agttydcb 1849bc76e4f9f09fc6c88d5de1a7cb304f9bc9d338f5a823b7431694457345bd

agttydck ce51278578b1a24c0fc5f8a739265e88f6f8b32632cf31bf7c142571eb22e243

IPs

C2 (Defunct) 45[.]145[.]6[.]112

References

  1. https://confluence.atlassian.com/security/cve-2023-22518-improper-authorization-vulnerability-in-confluence-data-center-and-server-1311473907.html
  1. https://www.proofpoint.com/uk/threat-reference/cerber-ransomware  
  1. https://nvd.nist.gov/vuln/detail/CVE-2023-22518

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
Nate Bill
Threat Researcher

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May 8, 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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May 6, 2026

When Trust Becomes the Attack Surface: Supply-Chain Attacks in an Era of Automation and Implicit Trust

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Software supply-chain attacks in 2026

Software supply-chain attacks now represent the primary threat shaping the 2026 security landscape. Rather than relying on exploits at the perimeter, attackers are targeting the connective tissue of modern engineering environments: package managers, CI/CD automation, developer systems, and even the security tools organizations inherently trust.

These incidents are not isolated cases of poisoned code. They reflect a structural shift toward abusing trusted automation and identity at ecosystem scale, where compromise propagates through systems designed for speed, not scrutiny. Ephemeral build runners, regardless of provider, represent high‑trust, low‑visibility execution zones.

The Axios compromise and the cascading Trivy campaign illustrate how quickly this abuse can move once attacker activity enters build and delivery workflows. This blog provides an overview of the latest supply chain and security tool incidents with Darktrace telemetry and defensive actions to improve organizations defensive cyber posture.

1. Why the Axios Compromise Scaled

On 31 March 2026, attackers hijacked the npm account of Axios’s lead maintainer, publishing malicious versions 1.14.1 and 0.30.4 that silently pulled in a malicious dependency, plain‑crypto‑[email protected]. Axios is a popular HTTP client for node.js and  processes 100 million weekly downloads and appears in around 80% of cloud and application environments, making this a high‑leverage breach [1].

The attack chain was simple yet effective:

  • A compromised maintainer account enabled legitimate‑looking malicious releases.
  • The poisoned dependency executed Remote Access Trojans (RATs) across Linux, macOS and Windows systems.
  • The malware beaconed to a remote command-and-control (C2) server every 60 seconds in a loop, awaiting further instructions.
  • The installer self‑cleaned by deleting malicious artifacts.

All of this matters because a single maintainer compromise was enough to project attacker access into thousands of trusted production environments without exploiting a single vulnerability.

A view from Darktrace

Multiple cases linked with the Axios compromise were identified across Darktrace’s customer base in March 2026, across both Darktrace / NETWORK and Darktrace / CLOUD deployments.

In one Darktrace / CLOUD deployment, an Azure Cloud Asset was observed establishing new external HTTP connectivity to the IP 142.11.206[.]73 on port 8000. Darktrace deemed this activity as highly anomalous for the device based on several factors, including the rarity of the endpoint across the network and the unusual combination of protocol and port for this asset. As a result, the triggering the "Anomalous Connection / Application Protocol on Uncommon Port" model was triggered in Darktrace / CLOUD. Detection was driven by environmental context rather than a known indicator at the time. Subsequent reporting later classified the destination as malicious in relation to the Axios supply‑chain compromise, reinforcing the gap that often exists between initial attacker activity and the availability of actionable intelligence. [5]

Additionally, shortly before this C2 connection, the device was observed communicating with various endpoints associated with the NPM package manager, further reinforcing the association with this attack.

Darktrace’s detection of the unusual external connection to 142.11[.]206[.]73 via port 8000.  
Figure 1: Darktrace’s detection of the unusual external connection to 142.11[.]206[.]73 via port 8000.  

Within Axios cases observed within Darktrace / NETWORK customer environments, activity generally focused on the use of newly observed cURL user agents in outbound connections to the C2 URL sfrclak[.]com/6202033, alongside the download of malicious files.

In other cases, Darktrace / NETWORK customers with Microsoft Defender for Endpoint integration received alerts flagging newly observed system executables and process launches associated with C2 communication.

A Security Integration Alert from Microsoft Defender for Endpoint associated with the Axios supply chain attack.
Figure 2: A Security Integration Alert from Microsoft Defender for Endpoint associated with the Axios supply chain attack.

2. Why Trivy bypassed security tooling trust

Between late February and March 22, 2026, the threat group TeamPCP leveraged credentials from a previous incident to insert malicious artifacts across Trivy’s distribution ecosystem, including its CI automation, release binaries, Visual Studio Code extensions, and Docker container images [2].

While public reporting has emphasized GitHub Actions, Darktrace telemetry highlights attacker execution within CI/CD runner environments, including ephemeral build runners. These execution contexts are typically granted broad trust and limited visibility, allowing malicious activity within build automation to blend into expected operational workflows, regardless of provider.

This was a coordinated multi‑phase attack:

  • 75 of 76  of trivy-action tags and all setup‑trivy tags were force‑pushed to deliver a malicious payload.
  • A malicious binary (v0.69.4) was distributed across all major distribution channels.
  • Developer machines were compromised, receiving a persistent backdoor and a self-propagating worm.
  • Secrets were exfiltrated at scale, including SSH keys, Kuberenetes tokens, database passwords, and cloud credentials across Amazon Web Service (AWS), Azure, and Google Cloud Platform (GCP).

Within Darktrace’s customer base, an AWS EC2 instance monitored by Darktrace / CLOUD  appeared to have been impacted by the Trivy attack. On March 19, the device was seen connecting to the attacker-controlled C2 server scan[.]aquasecurtiy[.]org (45.148.10[.]212), triggering the model 'Anomalous Server Activity / Outgoing from Server’ in Darktrace / CLOUD.

Despite this limited historical context, Darktrace assessed this activity as suspicious due to the rarity of the destination endpoint across the wider deployment. This resulted in the triggering of a model alert and the generation of a Cyber AI Analyst incident to further analyze and correlate the attack activity.

TeamPCP’s continued abused of GitHub Actions against security and IT tooling has also been observed more recently in Darktrace’s customer base. On April 22, an AWS asset was seen connecting to the C2 endpoint audit.checkmarx[.]cx (94.154.172[.]43). The timing of this activity suggests a potential link to a malicious Bitwarden package distributed by the threat actor, which was only available for a short timeframe on April 22. [4][3]

Figure 3: A model alert flagging unusual external connectivity from the AWS asset, as seen in Darktrace / CLOUD .

While the Trivy activity originated within build automation, the underlying failure mode mirrors later intrusions observed via management tooling. In both cases, attackers leveraged platforms designed for scale and trust to execute actions that blended into normal operational noise until downstream effects became visible.

Quest KACE: Legacy Risk, Real Impact

The Quest KACE System Management Appliance (SMA) incident reinforces that software risk is not confined to development pipelines alone. High‑trust infrastructure and management platforms are increasingly leveraged by adversaries when left unpatched or exposed to the internet.

Throughout March 2026, attackers exploited CVE 2025-32975 to authentication on outdated, internet-facing KACE appliances, gaining administrative control and pushing remote payloads into enterprise environments. Organizations still running pre-patch versions effectively handed adversaries a turnkey foothold, reaffirming a simple strategic truth: legacy management systems are now part of the supply-chain threat surface, and treating them as “low-risk utilities” is no longer defensible [3].

Within the Darktrace customer base, a potential case was identified in mid-March involving an internet-facing server that exhibited the use of a new user agent alongside unusual file downloads and unexpected external connectivity. Darktrace identified the device downloading file downloads from "216.126.225[.]156/x", "216.126.225[.]156/ct.py" and "216.126.225[.]156/n", using the user agents, "curl/8.5.0" & "Python-urllib/3.9".

The timeframe and IoCs observed point towards likely exploitation of CVE‑2025‑32975. As with earlier incidents, the activity became visible through deviations in expected system behavior rather than through advance knowledge of exploitation or attacker infrastructure. The delay between observed exploitation and its addition to the Known Exploited Vulnerabilities (KEV) catalogue underscores a recurring failure: retrospective validation cannot keep pace with adversaries operating at automation speed.

The strategic pattern: Ecosystem‑scale adversaries

The Axios and Trivy compromises are not anomalies; they are signals of a structural shift in the threat landscape. In this post-trust era, the compromise of a single maintainer, repository token, or CI/CD tag can produce large-scale blast radiuses with downstream victims numbering in the thousands. Attackers are no longer just exploiting vulnerabilities; they are exploiting infrastructure privileges, developer trust relationships, and automated build systems that the industry has generally under secured.

Supply‑chain compromise should now be treated as an assumed breach scenario, not a specialized threat class, particularly across build, integration, and management infrastructure. Organizations must operate under the assumption that compromise will occur within trusted software and automation layers, not solely at the network edge or user endpoint. Defenders should therefore expect compromise to emerge from trusted automation layers before it is labelled, validated, or widely understood.

The future of supply‑chain defense lies in continuous behavioral visibility, autonomous detection across developer and build environments, and real‑time anomaly identification.

As AI increasingly shapes software development and security operations, defenders must assume adversaries will also operate with AI in the loop. The defensive edge will come not from predicting specific compromises, but from continuously interrogating behavior across environments humans can no longer feasibly monitor at scale.

Credit to Nathaniel Jones (VP, Security & AI Strategy, FCISCO), Emma Foulger (Global Threat Research Operations Lead), Justin Torres (Senior Cyber Analyst), Tara Gould (Malware Research Lead)

Edited by Ryan Traill (Content Manager)

Appendices

References:

1)         https://www.infosecurity-magazine.com/news/hackers-hijack-axios-npm-package/

2)         https://thehackernews.com/2026/03/trivy-hack-spreads-infostealer-via.html

3)         https://thehackernews.com/2026/03/hackers-exploit-cve-2025-32975-cvss-100.html

4)         https://www.endorlabs.com/learn/shai-hulud-the-third-coming----inside-the-bitwarden-cli-2026-4-0-supply-chain-attack

5)         https://socket.dev/blog/axios-npm-package-compromised?trk=public_post_comment-text

IoCs

- 142.11.206[.]73 – IP Address – Axios supply chain C2

- sfrclak[.]com – Hostname – Axios supply chain C2

- hxxp://sfrclak[.]com:8000/6202033 - URI – Axios supply chain payload

- 45.148.10[.]212 – IP Address – Trivy supply chain C2

- scan.aquasecurtiy[.]org – Hostname - Trivy supply chain C2

- 94.154.172[.]43 – IP Address - Checkmarx/Bitwarden supply chain C2

- audit.checkmarx[.]cx – Hostname - Checkmarx/Bitwarder supply chain C2

- 216.126.225[.]156 – IP Address – Quest KACE exploitation C2

- 216.126.225[.]156/32 - URI – Possible Quest KACE exploitation payload

- 216.126.225[.]156/ct.py - URI - Possible Quest KACE exploitation payload

- 216.126.225[.]156/n - URI - Possible Quest KACE exploitation payload

- 216.126.225[.]156/x - URI - Possible Quest KACE exploitation payload

- e1ec76a0e1f48901566d53828c34b5dc – MD5 - Possible Quest KACE exploitation payload

- d3beab2e2252a13d5689e9911c2b2b2fc3a41086 – SHA1 - Possible Quest KACE exploitation payload

- ab6677fcbbb1ff4a22cc3e7355e1c36768ba30bbf5cce36f4ec7ae99f850e6c5 – SHA256 - Possible Quest KACE exploitation payload

- 83b7a106a5e810a1781e62b278909396 – MD5 - Possible Quest KACE exploitation payload

- deb4b5841eea43cb8c5777ee33ee09bf294a670d – SHA1 - Possible Quest KACE exploitation payload

- b1b2f1e36dcaa36bc587fda1ddc3cbb8e04c3df5f1e3f1341c9d2ec0b0b0ffaf – SHA256 - Possible Quest KACE exploitation payload

Darktrace Model Detections

Anomalous Connection / Application Protocol on Uncommon Port

Anomalous Server Activity / Outgoing from Server

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous File / EXE from Rare External Location

Anomalous File / Script from Rare External Location

Anomalous Server Activity / New User Agent from Internet Facing System

Anomalous Server Activity / Rare External from Server

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

Device / New User Agent

Device / Internet Facing Device with High Priority Alert

Anomalous File / New User Agent Followed By Numeric File Download

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Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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