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May 25, 2022

Understanding Grief Ransomware Attacks

Discover the latest insights on Grief ransomware and how to protect your organization. Stay informed on evolving cybersecurity threats with the cyber experts.
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
Oakley Cox
Director of Product
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25
May 2022

The Grief ransomware strain, also referred to as PayOrGrief, quickly gained a reputation for disruption in mid-to-late 2021. The gang behind the malware used quadruple-extortion ransomware tactics and targeted a range of victims including municipalities and school districts.

In July 2021, just weeks after the strain was first reported to cyber security teams, Grief successfully targeted Thessaloniki, the second largest city in Greece. Faced with a $20 million ransom demand, the municipality’s security team was forced to shut down all of its websites and public-facing services and launch a full investigation into the breach.

Double act: Grief and DoppelPaymer

From its emergence in May 2021, Grief used novel malware which confounded security tools trained on historical attacks. By July, however, the sophistication and efficiency of the group’s attacks led many to suspect that Grief’s operators had experience beyond their supposed two months of operation.

Grief is now widely reported to be a rebrand of the DoppelPaymer ransomware gang, which ended its operations in May 2021 and was believed to be affiliated with the Russian ransomware gang Evil Corp. After adopting the new moniker, however, Grief regularly blew past traditional security tools, amassing well over $10 million in ransom payments in just four months.

Adaptations and rebrands are common techniques adopted by criminal gangs using the Ransomware-as-a-Service business model. The success of Grief’s rebrand illustrates how rapidly a ransomware group can update its attacks and render them unrecognizable to signature-based tools.

Revealing Grief’s tricks with Cyber AI Analyst

In July 2021, PayOrGrief targeted a European manufacturing company which had Darktrace deployed across its network. Darktrace’s early detection of the attack, along with the real-time visibility into its lifecycle offered by Darktrace’s Cyber AI Analyst, meant that each stage of the attack was clear to see.

Figure 1: Timeline of the PayOrGrief attack

The initial intrusion compromised four devices, which Darktrace detected when these devices connected to rare external IPs and downloaded encoded text files. It is likely that the devices were compromised as the result of a targeted phishing campaign, which are often used in Grief attacks as a way of injecting malware such as Dridex onto devices. If deployed within the targeted organization, Antigena Email would have identified the phishing campaign and halted it, before it reached employee inboxes. In this case, however, the attack continued.

Following the initial compromise, C2 (Command and Control) connections were made over an encrypted channel using invalid SSL certificates. An upload of 50MB of data was made from one of the infected devices to the company’s corporate server, which gave the attackers access to the company’s crown jewels: its most sensitive data. From this privileged position, and with keep-alive beacons in place, the attack was ready for detonation.

Several devices were detected attempting to upload data totaling more than 100 GB to the external file storage platform, Mega, using encrypted HTTPS on port 443. However, the attackers did not receive the total package of data they had expected. The organization had deployed Darktrace’s Autonomous Response to protect its key assets and most sensitive data. The AI recognized the anomalous behavior as a significant deviation from the business’s normal ‘pattern of life’ and autonomously blocked uploads from protected devices, preventing exfiltration wherever it was able to do so.

Figure 2: Data exfiltration from a single device, investigated by Cyber AI Analyst

The attackers then continued to spread through the digital environment. Using ‘Living off the Land’ techniques including RDP and SMB, they performed internal reconnaissance, escalated their privileges and moved laterally to additional digital assets. With access to new admin credentials, just ten hours after the initial C2 communications, the attackers commenced ransomware encryption.

It’s highly possible, therefore, that Grief has targeted Darktrace customers previously and been neutralized too early for the attack to be identified and attributed. In this instance, the organization had deployed Autonomous Response only on certain areas of the network, and we are therefore able to see how the attack progressed on unprotected devices.

Unusual suspects

The Indicators of Compromise (IoCs) for Grief ransomware have now been incorporated by many traditional security tools, but this is a short-term solution, and won’t account for further changes in both threat actor tactics and the digital environments they target. Once the Grief moniker has been exhausted, it is more than likely that another will be adopted in its place.

The AI-driven approach to cyber security tackles threats regardless of when and where they arrive, or what name they arrive under. By focusing on developing its sophisticated understanding of the entire digital estate, Darktrace’s Autonomous Response targets specific anomalies with specific, proportionate responses, even when they are part of entirely novel attacks. And when given the freedom to take action against these threats the moment they’re detected, Autonomous Response can ensure that organizations stay protected even when human teams are unavailable.

Thanks to Darktrace analyst Beverly McCann for her insights on the above threat find.

Technical details

Darktrace model detections

  • Device / Suspicious SMB Scanning Activity
  • Device / New User Agents
  • Anomalous Server Activity / Rare External from Server
  • Compliance / External Windows Communications
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Connection / Anomalous SSL without SNI to New External
  • Anomalous Connection / Rare External SSL Self-Signed
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Compliance / Remote Management Tool on Server
  • Anomalous Server Activity / Outgoing from Server
  • Anomalous Connection / Multiple HTTP POSTs to Rare Hostname
  • Anomalous Connection / Data Sent to Rare Domain
  • Anomalous Connection / Lots of New Connections
  • Unusual Activity / Unusual File Storage Data Transfer
  • Unusual Activity / Enhanced Unusual External Data Transfer [Enhanced Monitoring]
  • Anomalous Connection / Uncommon 1GiB Outbound
  • Unusual Activity / Unusual External Data to New Ips
  • Anomalous Connection / SMB Enumeration
  • Multiple Device Correlations / Behavioral Change Across Multiple Devices
  • Device / New or Uncommon WMI Activity
  • Unusual Activity / Unusual External Connections
  • Device / ICMP Address Scan
  • Anomalous Connection / Unusual Admin RDP Session
  • Compliance / SMB Version 1 Usage
  • Anomalous Connection / Unusual SMB Version 1
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Unusual Activity / Anomalous SMB Move and Write
  • Compromise / Ransomware / Suspicious SMB Activity [Enhanced Monitoring]
  • Anomalous Connection / Suspicious Read Write Ratio and Unusual SMB
  • Anomalous Connection / New or Uncommon Service Control
  • Device / New or Unusual Remote Command Execution
  • User / New Admin Credentials On Client
  • Device / New or Uncommon SMB Named Pipe
  • Device / Multiple Lateral Movement Model Breaches [Enhanced Monitoring]
  • Anomalous Connection / Suspicious Read Write Ratio
  • Device / SMA Lateral Movement
  • Anomalous File / Internal / Unusual Internal EXE File Transfer
  • Anomalous Server Activity / Unusual Unresponsive Server
  • Device / Internet Facing Device with High Priority Alert
  • Multiple Device Correlations / Spreading Unusual SMB Activity
  • Multiple Device Correlations / Multiple Devices Breaching the Same Model

Darktrace Autonomous Response alerts

  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Antigena / Network / Insider Threat / Antigena Breaches Over Time Block
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly Block
  • Antigena / Network / Significant Anomaly / Antigena Breaches over Time Block
  • Antigena / Network / Insider Threat / Antigena Large Data Volume Outbound Block
  • Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block
  • Antigena / Network / Insider Threat / Antigena SMB Enumeration Block
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
  • Antigena / Network / Insider Threat / Antigena Internal Anomalous File Activity
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Antigena / Network / External Threat / Antigena Ransomware Block
  • Antigena / Network / External Threat / SMB Ratio Antigena Block

MITRE ATT&CK techniques observed

Reconnaissance
T1595 — Active Scanning

Resource Development
T1608 — Stage Capabilities

Initial Access
T1190 — Exploit Public-Facing Application

Persistence
T1133 — External Remote Services

Defense Evasion
T1079 — Valid Accounts

Discovery
T1046 — Network Service Scanning
T1083 — File and Directory Discovery
T1018 — Remote System Discovery

Lateral Movement
T1210 — Exploitation of Remote Services
T1080 — Taint Shared Content
T1570 — Lateral Tool Transfer
T1021 — Remote Services

Command and Control
T1071 — Application Layer Protocol
T1095 — Non-Application Layer Protocol
T1571 — Non-Standard Port

Exfiltration
T1041 — Exfiltration over C2 Channel
T1567 — Exfiltration Over Web Service
T1029 — Scheduled Transfer


Impact
T1486 — Data Encrypted for Impact
T1489 — Service Stop
T1529 — System Shutdown/Reboot

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
Oakley Cox
Director of Product

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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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About the author
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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