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April 26, 2023

Gozi ISFB Malware Detection Insights and Analysis

Uncover how Gozi ISFB operates and how Darktrace’s detection capabilities help secure your systems against this versatile malware.
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
Justin Torres
Cyber Analyst
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26
Apr 2023

Mirroring the overall growth of the cybersecurity landscape and the advancement of security tool capabilities, threat actors are continuously forced to keep pace. Today, threat actors are bringing novel malware into the wild, creating new attack vectors, and finding ways to avoid the detection of security tools. 

One notable example of a constantly adapting type of malware can be seen with banking trojans, a type of malware designed to steal confidential information, such as banking credentials, used by attackers for financial gain. Gozi-ISFB is a widespread banking trojan that has previously been referred to as ‘the malware with a thousand faces’ and, as it name might suggest, has been known under various names such as Gozi, Ursnif, Papras and Rovnix to list a few.

Between November 2022 and January 2023, a rise in Gozi-ISFB malware related activity was observed across Darktrace customer environments and was investigated by the Darktrace Threat Research team. Leveraging its Self-Learning AI, Darktrace was able to identify activity related to this banking trojan, regardless of the attack vectors or delivery methods utilized by threat actors.

We have moderate to high confidence that the series of activities observed is associated with Gozi-ISFB malware and high confidence in the indicators of compromise identified which are related to the post-compromise activities from Gozi-ISFB malware. 

Gozi-ISFB Background

The Gozi-ISFB malware was first observed in 2011, stemming from the source code of another family of malware, Gozi v1, which in turn borrowed source code from the Ursnif malware strain.  

Typically, the initial access payloads of Gozi-ISFB would require an endpoint to enable a macro on their device, subsequently allowing a pre-compiled executable file (.exe) to be gathered from an attacker-controlled server, and later executed on the target device.

However, researchers have recently observed Gozi-ISFB actors using additional and more advanced capabilities to gain access to organizations networks. These capabilities range from credential harvest, surveilling user keystrokes, diverting browser traffic from banking websites, remote desktop access, and the use of domain generation algorithms (DGA) to create command-and-control (C2) domains to avoid the detection and blocking of traditional security tools. 

Ultimately, the goal of Gozi-ISFB malware is to gather confidential information from infected devices by connecting to C2 servers and installing additional malware modules on the network. 

Darktrace Coverage of Gozi-ISFB 

Unlike traditional security approaches, Darktrace DETECT/Network™ can identify malicious activity because Darktrace models build an understanding of a device’s usual pattern of behavior, rather than using a static list of indicators of compromise (IoCs) or rules and signatures. As such, Darktrace is able to instantly detect compromised devices that deviate from their expected behavioral patterns, engaging in activity such as unusual SMB connections or connecting to newly created malicious endpoints or C2 infrastructure. In the event that Darktrace detects malicious activity, it would automatically trigger an alert, notifying the customer of an ongoing security concern. 

Regarding the Gozi-ISFB attack process, initial attack vectors commonly include targeted phishing campaigns, where the recipient would receive an email with an attached Microsoft Office document containing macros or a Zip archive file. Darktrace frequently observes malicious emails like this across the customer base and is able to autonomously detect and action them using Darktrace/Email™. In the following cases, the clients who had Darktrace/Email did not have evidence of compromise through their corporate email infrastructure, suggesting devices were likely compromised via the access of personal email accounts. In other cases, the customers did not have Darktrace/Email enabled on their networks.

Upon downloading and opening the malicious attachment included in the phishing email, the payload subsequently downloads an additional .exe or dynamic link library (DLL) onto the device. Following this download, the malware will ultimately begin to collect sensitive data from the infected device, before exfiltrating it to the C2 server associated with Gozi-ISFB. Darktrace was able to demonstrate and detect the retrieval of Gozi-ISFB malware, as well as subsequent malicious communication on multiple customer environments. 

In some attack chains observed, the infected device made SMB connections to the rare external endpoint ’62.173.138[.]28’ via port 445. Darktrace recognized that the device used unusual credentials for this destination endpoint and further identified it performing SMB reads on the share ‘\\62.173.138[.]28\Agenzia’. Darktrace also observed that the device downloaded the executable file ‘entrat.exe’ from this connection as can be seen in Figure 1.

Figure 1: Model breach event log showing an infected device making SMB read actions on the share ‘\\62.173.138[.]28\Agenzia’. Darktrace observed the device downloading the executable file ‘entrat.exe’ from this connection.

Subsequently, the device performed a separate SMB login to the same external endpoint using a credential identical to the device's name. Shortly after, the device performed a SMB directory query from the root share drive for the file path to the same endpoint. 

Figure 2:SMB directory query from the root share drive for the file path to the same endpoint, ’62.173.138[.]28’.

In Gozi-ISFB compromises investigated by the Threat Research team, Darktrace commonly observed model breaches for ‘Multiple HTTP POSTs to Rare Hostname’ and the use of the Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 10.0; Win64; x64)’ user agent. 

Devices were additionally observed making external connections over port 80 (TCP, HTTP) to endpoints associated with Gozi-ISFB. Regarding these connections, C2 communication was observed used configurations of URI path and resource file extension that claimed to be related to images within connections that were actually GET or POST request URIs. This is a commonly used tactic by threat actors to go under the radar and evade the detection of security teams.  

An example of this type of masqueraded URI can be seen below:

In another similar example investigated by the Threat Research team, Darktrace detected similar external connectivity associated with Gozi-ISFB malware. In this case, DETECT identified external connections to two separate hostnames, namely ‘gameindikdowd[.]ru’ and ‘jhgfdlkjhaoiu[.]su’,  both of which have been associated to Gozi-ISFB by OSINT sources. This specific detection included HTTP beaconing connections to endpoint, gameindikdowd[.]ru .

Details observed from this event: 

Destination IP: 134.0.118[.]203

Destination port: 80

ASN: AS197695 Domain names registrar REG.RU, Ltd

User agent: Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 10.0; Win64; x64

The same device later made anomalous HTTP POST requests to a known Gozi-ISFB endpoint, jhgfdlkjhaoiu[.]su. 

Details observed:

Destination port: 80

ASN: AS197695 Domain names registrar REG.RU, Ltd

User agent: Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 10.0; Win64; x64

Figure 3: Packet Capture (PCAP) with the device conducting anomalous HTTP POST requests to a Gozi-ISFB related IOC, ‘jhgfdlkjhaoiu[.]su’.

Conclusions 

With constantly changing attack infrastructure and new methods of exploitation tested and leveraged hour upon hour, it is critical for security teams to employ tools that help them stay ahead of the curve to avoid critical damage from compromise.  

Faced with a notoriously adaptive malware strain like Gozi-ISFB, Darktrace demonstrated its ability to autonomously detect malicious activity based upon more than just known IoCs and attack vectors. Despite the multitude of different attack vectors utilized by threat actors, Darktrace was able to detect Gozi-ISFB activity at various stages of the kill chain using its anomaly-based detection to identify unusual activity or deviations from normal patterns of life. Using its Self-Learning AI, Darktrace successfully identified infected devices and brought them to the immediate attention of customer security teams, ultimately preventing infections from leading to further compromise.  

The Darktrace suite of products, including DETECT/Network, is uniquely placed to offer customers an unrivaled level of network security that can autonomously identify and respond to arising threats against their networks in real time, preventing suspicious activity from leading to damaging network compromises.

Credit to: Paul Jennings, Principal Analyst Consultant and the Threat Research Team

Appendices

List of IOCs

134.0.118[.]203 - IP Address - Gozi-ISFB C2 Endpoint

62.173.138[.]28 - IP Address - Gozi-ISFB  C2 Endpoint

45.130.147[.]89 - IP Address - Gozi-ISFB  C2 Endpoint

94.198.54[.]97 - IP Address - Gozi-ISFB C2 Endpoint

91.241.93[.]111 - IP Address - Gozi-ISFB  C2 Endpoint

89.108.76[.]56 - IP Address - Gozi-ISFB  C2 Endpoint

87.106.18[.]141 - IP Address - Gozi-ISFB  C2 Endpoint

35.205.61[.]67 - IP Address - Gozi-ISFB  C2 Endpoint

91.241.93[.]98 - IP Address - Gozi-ISFB  C2 Endpoint

62.173.147[.]64 - IP Address - Gozi-ISFB C2 Endpoint

146.70.113[.]161 - IP Address - Gozi-ISFB  C2 Endpoint 

iujdhsndjfks[.]ru - Hostname - Gozi-ISFB C2 Hostname

reggy505[.]ru - Hostname - Gozi-ISFB  C2 Hostname

apr[.]intoolkom[.]at - Hostname - Gozi-ISFB  C2 Hostname

jhgfdlkjhaoiu[.]su - Hostname - Gozi-ISFB  C2 Hostname

gameindikdowd[.]ru - Hostname - Gozi-ISFB  Hostname

chnkdgpopupser[.]at - Hostname – Gozi-ISFB C2 Hostname

denterdrigx[.]com - Hostname – Gozi-ISFB C2 Hostname

entrat.exe - Filename – Gozi-ISFB Related Filename

Darktrace Model Coverage

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

Anomalous Connection / Posting HTTP to IP Without Hostname

Anomalous Connection / New User Agent to IP Without Hostname

Compromise / Agent Beacon (Medium Period)

Anomalous File / Application File Read from Rare Endpoint

Device / Suspicious Domain

Mitre Attack and Mapping

Tactic: Application Layer Protocol: Web Protocols

Technique: T1071.001

Tactic: Drive-by Compromise

Technique: T1189

Tactic: Phishing: Spearphishing Link

Technique: T1566.002

Model Detection

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname - T1071.001

Anomalous Connection / Posting HTTP to IP Without Hostname - T1071.001

Anomalous Connection / New User Agent to IP Without Hostname - T1071.001

Compromise / Agent Beacon (Medium Period) - T1071.001

Anomalous File / Application File Read from Rare Endpoint - N/A

Device / Suspicious Domain - T1189, T1566.002

References

https://threatfox.abuse.ch/browse/malware/win.isfb/

https://www.cisa.gov/news-events/cybersecurity-advisories/aa22-216a

https://www.fortinet.com/blog/threat-research/new-variant-of-ursnif-continuously-targeting-italy#:~:text=Ursnif%20(also%20known%20as%20Gozi,Italy%20over%20the%20past%20year

https://medium.com/csis-techblog/chapter-1-from-gozi-to-isfb-the-history-of-a-mythical-malware-family-82e592577fef

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
Justin Torres
Cyber 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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Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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