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August 22, 2023

Darktrace’s Detection of Unattributed Ransomware

Leveraging anomaly-based detection, we successfully identified an ongoing ransomware attack on the network of a customer and the activity that preceded it.
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
Natalia Sánchez Rocafort
Cyber Security Analyst
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22
Aug 2023

In the current threat landscape, much of the conversation around ransomware focusses on high-profile strains and notorious threat groups. While organizations and their security teams are justified in these concerns, it is important not to underestimate the danger posed by smaller scale, unattributed ransomware attacks.

Unlike attributed ransomware strains, there are often no playbooks or lists of previously observed indicators of compromise (IoCs) that security teams can consult to help them shore up their cyber defenses. As such, anomaly detection is critical to ensure that emerging threats can be detected based on their abnormality on the network, rather than relying heavily on threat intelligence.

In mid-March 2023, a Darktrace customer requested analytical support from the Darktrace Security Operations Center (SOC) after they had been hit by a ransomware attack a few hours earlier. Darktrace was able to uncover a myriad of malicious activity that preceded the eventual ransomware deployment, ultimately assisting the customer to identify compromised devices and contain the ransomware attack.

Attack Overview

While there were a small number of endpoints that had been flagged as malicious by open-source intelligence (OSINT), Darktrace DETECT™ focused on the unusualness of the activity surrounding this emerging ransomware attack. This provided unparalleled visibility over this ransomware attack at every stage of the cyber kill chain, whilst also revealing the potential origins of the compromise which came months area.

Initial Compromise

Initial investigation revealed that several devices that Darktrace were observed performing suspicious activity had previously engaged in anomalous behavior several months before the ransomware event, indicating this could be a part of a repeated compromise or the result of initial access brokers.

Most notably, in late January 2023 there was a spike in unusual activity when some of the affected devices were observed performing activity indicative of network and device scanning.

Darktrace DETECT identified some of the devices establishing unusually high volumes of internal failed connections via TCP and UDP, and the SMB protocol. Various key ports, such as 135, 139, and 445, were also scanned.

Due to the number of affected devices, the exact initial attack vector is unclear; however, one likely scenario is associated with an internet-facing DNS server. Towards the end of January 2023, the server began to receive unusual TCP DNS requests from the rare external endpoint, 103.203.59[.]3, which had been flagged as potentially malicious by OSINT [4]. Based on a portion of the hostname of the device, dc01, we can assume that this server served as a gateway to the domain controller. If a domain controller is compromised, a malicious actor would gain access to usernames and passwords within a network allowing attackers to obtain administrative-level access to an organization’s digital estate.

Around the same time as the unusual TCP DNS requests, Darktrace DETECT observed the domain controller engaging in further suspicious activity. As demonstrated in Figure 1, Darktrace recognized that this server was not responding to common requests from multiple internal devices, as it would be expected to. Following this, the device was observed carrying out new or uncommon Windows Management Instrumentation (WMI) activity. WMI is typically used by network administrators to manage remote and local Windows systems [3].

Figure 1: Device event log depicting the possible Initial attack vector.


Had Darktrace RESPOND™ been enabled in autonomous response mode, it would have to blocked connections originating from the compromised internal devices as soon as they were detected, while also limiting affected devices to their pre-established patterns of file to prevent them from carrying out any further malicious activity.

Darktrace subsequently observed multiple devices establishing various chains of connections that are indicative of lateral movement activity, such as unusual internal RDP and WMI requests. While there may be devices within an organization that do regularly partake these types of connections, Darktrace recognized that this activity was extremely unusual for these devices.

Darktrace’s Self-Learning AI allows for a deep understanding of customer networks and the devices within them. It’s anomaly-based threat detection capability enables it to recognize subtle deviations in a device’s normal patterns of behavior, without depending on known IoCs or signatures and rules to guide it.

Figure 2: Observed chain of possible lateral movement.


Persistence

Darktrace DETECT observed several affected devices communicating with rare external endpoints that had also been flagged as potentially malicious by OSINT tools. Multiple devices were observed performing activity indicative of NTLM brute-forcing activity, as seen in the Figure 3 which highlights the event log of the aforementioned domain controller. Said domain controller continuously engaged in anomalous behavior throughout the course of the attack. The same device was seen using a potentially compromise credential, ‘cvd’, which was observed via an SMB login event.

Figure 3: Continued unusual external connectivity.


Affected devices, including the domain controller, continued to engage in consistent communication with the endpoints prior to the actual ransomware attack. Darktrace identified that some of these malicious endpoints had likely been generated by Domain Generation Algorithms (DGA), a classic tactic utilized by threat actors. Subsequent OSINT investigation revealed that one such domain had been associated with malware such as TrojanDownloader:Win32/Upatre!rfn [5].

All external engagements were observed by Darktrace DETECT and would have been actioned on by Darktrace RESPOND, had it been configured in autonomous response mode. It would have blocked any suspicious outgoing connections originating from the compromised devices, thus preventing additional external engagement from taking place. Darktrace RESPOND works in tandem with DETECT to autonomously take action against suspicious activity based on its unusualness, rather than relying on static lists of ‘known-bads’ or malicious IoCs.

Reconnaissance

On March 14, 2023, a few days before the ransomware attack, Darktrace observed multiple internal devices failing to establish connections in a manner that suggests SMB, RDP and network scanning. Among these devices once more was the domain controller, which was seen performing potential SMB brute-forcing, representing yet another example of malicious activity carried out by this device.

Lateral Movement

Immediately prior to the attack, many compromised devices were observed mobilizing to conduct an array of high-severity lateral movement activity. Darktrace detected one device using two administrative credentials, namely ‘Administrator’ and ‘administrator’, while it also observed a notable spike in the volume of successful SMB connections from the device around the same time.

At this point, Darktrace DETECT was observing the progression of this attack along the cyber kill chain. What had started as internal recognisance, had escalated to exploitation and ensuing command-and-control activity. Following an SMB brute-force attempt, Darktrace DETECT identified a successful DCSync attack.

A DCSync attack occurs when a malicious actor impersonates a domain controller in an effort to gather sensitive information, such as user credentials and passwords hashes, by replicating directory services [1]. In this case, a device sent various successful DRSGetNCChanges operation requests to the DRSUAPI endpoint.

Data Exfiltration

Around the same time, Darktrace detected the compromised server transferring a high volume of data to rare external endpoints associated with Bublup, a third-party project management application used to save and share files. Although the actors attempted to avoid the detection of security tools by using a legitimate file storage service, Darktrace understood that this activity represented a deviation in this device’s expected pattern of life.

In one instance, around 8 GB of data was transferred, and in another, over 4 GB, indicating threat actors were employing a tactic known as ‘low and slow’ exfiltration whereby data is exfiltrated in small quantities via multiple connections, in an effort to mask their suspicious activity. While this tactic may have evaded the detection of traditional security measures, Darktrace’s anomaly-based detection allowed it to recognize that these two incidents represented a wider exfiltration event, rather than viewing the transfers in isolation.

Impact

Finally, Darktrace began to observe a large amount of suspicious SMB activity on the affected devices, most of which was SMB file encryption. DETECT observed the file extension ‘uw9nmvw’ being appended to many files across various internal shares and devices. In addition to this, a potential ransom note, ‘RECOVER-uw9nmvw-FILES.txt’, was detected on the network shortly after the start of the attack.

Figure 4: Depiction of the high-volume of suspicious SMB activity, including file encryption.


Conclusion

Ultimately, this incident show cases how Darktrace was able to successfully identify an emerging ransomware attack using its unrivalled anomaly-based detection capabilities, without having to rely on any previously established threat intelligence. Not only was Darktrace DETECT able to identify the ransomware at multiple stages of the kill chain, but it was also able to uncover the anomalous activity that took place in the buildup to the attack itself.

As the attack progressed along the cyber kill chain, escalating in severity at every juncture, DETECT was able to provide full visibility over the events. Through the successful identification of compromised devices, anomalous administrative credentials usage and encrypted files, Darktrace was able to greatly assist the customer, ensuring they were well-equipped to contain the incident and begin their incident management process.

Darktrace would have been able to aid the customer even further had they enabled its autonomous response technology on their network. Darktrace RESPOND would have taken targeted, mitigative action as soon as suspicious activity was detected, preventing the malicious actors from achieving their goals.

Credit to: Natalia Sánchez Rocafort, Cyber Security Analyst, Patrick Anjos, Senior Cyber Analyst.

MITRE Tactics/Techniques Mapping

RECONNAISSANCE

Scanning IP Blocks  (T1595.001)

RECONNAISSANCE

Vulnerability Scanning  (T1595.002)

IMPACT

Service Stop  (T1489)

LATERAL MOVEMENT

Taint Shared Content (T1080)

IMPACT

Data Encrypted for Impact (T1486)

INITIAL ACCESS

Replication Through Removable Media (T1200)

DEFENSE EVASION

Rogue Domain Controller (T1207)

COMMAND AND CONTROL

Domain Generation Algorithms (T1568.002)

EXECUTION

Windows Management Instrumentation (T1047)

INITIAL ACCESS

Phishing (T1190)

EXFILTRATION

Exfiltration Over C2 Channel (T1041)

IoC Table

IoC ----------- TYPE ------------- DESCRIPTION + PROBABILITY

CVD --------- credentials -------- Possible compromised credential

.UW9NMVW - File extension ----- Possible appended file extension

RECOVER-UW9NMVW-FILES.TXT - Ransom note - Possible ransom note observed

84.32.188[.]186 - IP address ------ C2 Endpoint

AS.EXECSVCT[.]COM - Hostname - C2 Endpoint

ZX.EXECSVCT[.]COM - Hostname - C2 Endpoint

QW.EXECSVCT[.]COM - Hostname - C2 Endpoint

EXECSVCT[.]COM - Hostname ------ C2 Endpoint

15.197.130[.]221 --- IP address ------ C2 Endpoint

AS59642 UAB CHERRY SERVERS - ASN - Possible ASN associated with C2 Endpoints

108.156.28[.]43

108.156.28[.]22

52.84.93[.]26

52.217.131[.]241

54.231.193[.]89 - IP addresses - Possible IP addresses associated with data exfiltration

103.203.59[.]3 -IP address ---- Possible IP address associated with initial attack vector

References:

[1] https://blog.netwrix.com/2021/11/30/what-is-dcsync-an-introduction/

[2] https://www.easeus.com/computer-instruction/delete-system32.html#:~:text=System32%20is%20a%20folder%20on,DLL%20files%2C%20and%20EXE%20files.

[3] https://www.techtarget.com/searchwindowsserver/definition/Windows-Management-Instrumentation#:~:text=WMI%20provides%20users%20with%20information,operational%20environments%2C%20including%20remote%20systems.

[4] https://www.virustotal.com/gui/ip-address/103.203.59[.]3

[5] https://otx.alienvault.com/indicator/ip/15.197.130[.]221

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
Natalia Sánchez Rocafort
Cyber Security Analyst

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May 7, 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, behavioural 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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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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