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October 3, 2024

From Call to Compromise: Darktrace’s Response to a Vishing-Induced Network Attack

When a remote user fell victim to a vishing attack, allowing a malicious actor to gain access to a customer network, Darktrace swiftly detected the intrusion and responded effectively. This prompt action prevented any data loss and reinforced trust in Darktrace’s robust security measures.
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
Rajendra Rushanth
Cyber Analyst
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03
Oct 2024

What is vishing?

Vishing, or voice phishing, is a type of cyber-attack that utilizes telephone devices to deceive targets. Threat actors typically use social engineering tactics to convince targets that they can be trusted, for example, by masquerading as a family member, their bank, or trusted a government entity. One method frequently used by vishing actors is to intimidate their targets, convincing them that they may face monetary fines or jail time if they do not provide sensitive information.

What makes vishing attacks dangerous to organizations?

Vishing attacks utilize social engineering tactics that exploit human psychology and emotion. Threat actors often impersonate trusted entities and can make it appear as though a call is coming from a reputable or known source.  These actors often target organizations, specifically their employees, and pressure them to obtain sensitive corporate data, such as privileged credentials, by creating a sense of urgency, intimidation or fear. Corporate credentials can then be used to gain unauthorized access to an organization’s network, often bypassing traditional security measures and human security teams.

Darktrace’s coverage of vishing attack

On August 12, 2024, Darktrace / NETWORK identified malicious activity on the network of a customer in the hospitality sector. The customer later confirmed that a threat actor had gained unauthorized access through a vishing attack. The attacker successfully spoofed the IT support phone number and called a remote employee, eventually leading to the compromise.

Figure 1: Timeline of events in the kill chain of this attack.

Establishing a Foothold

During the call, the remote employee was requested to authenticate via multi-factor authentication (MFA). Believing the caller to be a member of their internal IT support, using the legitimate caller ID, the remote user followed the instructions and confirmed the MFA prompt, providing access to the customer’s network.

This authentication allowed the threat actor to login into the customer’s environment by proxying through their Virtual Private Network (VPN) and gain a foothold in the network. As remote users are assigned the same static IP address when connecting to the corporate environment, the malicious actor appeared on the network using the correct username and IP address. While this stealthy activity might have evaded traditional security tools and human security teams, Darktrace’s anomaly-based threat detection identified an unusual login from a different hostname by analyzing NTLM requests from the static IP address, which it determined to be anomalous.

Observed Activity

  • On 2024-08-12 the static IP was observed using a credential belonging to the remote user to initiate an SMB session with an internal domain controller, where the authentication method NTLM was used
  • A different hostname from the usual hostname associated with this remote user was identified in the NTLM authentication request sent from a device with the static IP address to the domain controller
  • This device does not appear to have been seen on the network prior to this event.

Darktrace, therefore, recognized that this login was likely made by a malicious actor.

Internal Reconnaissance

Darktrace subsequently observed the malicious actor performing a series of reconnaissance activities, including LDAP reconnaissance, device hostname reconnaissance, and port scanning:

  • The affected device made a 53-second-long LDAP connection to another internal domain controller. During this connection, the device obtained data about internal Active Directory (AD) accounts, including the AD account of the remote user
  • The device made HTTP GET requests (e.g., HTTP GET requests with the Target URI ‘/nice ports,/Trinity.txt.bak’), indicative of Nmap usage
  • The device started making reverse DNS lookups for internal IP addresses.
Figure 2: Model alert showing the IP address from which the malicious actor connected and performed network scanning activities via port 9401.
Figure 3: Model Alert Event Log showing the affected device connecting to multiple internal locations via port 9401.

Lateral Movement

The threat actor was also seen making numerous failed NTLM authentication requests using a generic default Windows credential, indicating an attempt to brute force and laterally move through the network. During this activity, Darktrace identified that the device was using a different hostname than the one typically used by the remote employee.

Cyber AI Analyst

In addition to the detection by Darktrace / NETWORK, Darktrace’s Cyber AI Analyst launched an autonomous investigation into the ongoing activity. The investigation was able to correlate the seemingly separate events together into a broader incident, continuously adding new suspicious linked activities as they occurred.

Figure 4: Cyber AI Analyst investigation showing the activity timeline, and the activities associated with the incident.

Upon completing the investigation, Cyber AI Analyst provided the customer with a comprehensive summary of the various attack phases detected by Darktrace and the associated incidents. This clear presentation enabled the customer to gain full visibility into the compromise and understand the activities that constituted the attack.

Figure 5: Cyber AI Analyst displaying the observed attack phases and associated model alerts.

Darktrace Autonomous Response

Despite the sophisticated techniques and social engineering tactics used by the attacker to bypass the customer’s human security team and existing security stack, Darktrace’s AI-driven approach prevented the malicious actor from continuing their activities and causing more harm.

Darktrace’s Autonomous Response technology is able to enforce a pattern of life based on what is ‘normal’ and learned for the environment. If activity is detected that represents a deviation from expected activity from, a model alert is triggered. When Darktrace’s Autonomous Response functionality is configured in autonomous response mode, as was the case with the customer, it swiftly applies response actions to devices and users without the need for a system administrator or security analyst to perform any actions.

In this instance, Darktrace applied a number of mitigative actions on the remote user, containing most of the activity as soon as it was detected:

  • Block all outgoing traffic
  • Enforce pattern of life
  • Block all connections to port 445 (SMB)
  • Block all connections to port 9401
Figure 6: Darktrace’s Autonomous Response actions showing the actions taken in response to the observed activity, including blocking all outgoing traffic or enforcing the pattern of life.

The growing threat of vishing in a remote workforce

This vishing attack underscores the significant risks remote employees face and the critical need for companies to address vishing threats to prevent network compromises. The remote employee in this instance was deceived by a malicious actor who spoofed the phone number of internal IT Support and convinced the employee to perform approve an MFA request. This sophisticated social engineering tactic allowed the attacker to proxy through the customer’s VPN, making the malicious activity appear legitimate due to the use of static IP addresses.

Despite the stealthy attempts to perform malicious activities on the network, Darktrace’s focus on anomaly detection enabled it to swiftly identify and analyze the suspicious behavior. This led to the prompt determination of the activity as malicious and the subsequent blocking of the malicious actor to prevent further escalation.

While the exact motivation of the threat actor in this case remains unclear, the 2023 cyber-attack on MGM Resorts serves as a stark illustration of the potential consequences of such threats. MGM Resorts experienced significant disruptions and data breaches following a similar vishing attack, resulting in financial and reputational damage [1]. If the attack on the customer had not been detected, they too could have faced sensitive data loss and major business disruptions. This incident underscores the critical importance of robust security measures and vigilant monitoring to protect against sophisticated cyber threats.

Insights from Darktrace’s First 6: Half-year threat report for 2024

First 6: half year threat report darktrace screenshot

Darktrace’s First 6: Half-Year Threat Report 2024 highlights the latest attack trends and key threats observed by the Darktrace Threat Research team in the first six months of 2024.

  • Focuses on anomaly detection and behavioral analysis to identify threats
  • Maps mitigated cases to known, publicly attributed threats for deeper context
  • Offers guidance on improving security posture to defend against persistent threats

Appendices

Credit to Rajendra Rushanth (Cyber Security Analyst) and Ryan Traill (Threat Content Lead)

Darktrace Model Detections

  • Device / Unusual LDAP Bind and Search Activity
  • Device / Attack and Recon Tools
  • Device / Network Range Scan
  • Device / Suspicious SMB Scanning Activity
  • Device / RDP Scan
  • Device / UDP Enumeration
  • Device / Large Number of Model Breaches
  • Device / Network Scan
  • Device / Multiple Lateral Movement Model Breaches (Enhanced Monitoring)
  • Device / Reverse DNS Sweep
  • Device / SMB Session Brute Force (Non-Admin)

List of Indicators of Compromise (IoCs)

IoC - Type – Description

/nice ports,/Trinity.txt.bak - URI – Unusual Nmap Usage

MITRE ATT&CK Mapping

Tactic – ID – Technique

INITIAL ACCESS – T1200 – Hardware Additions

DISCOVERY – T1046 – Network Service Scanning

DISCOVERY – T1482 – Domain Trust Discovery

RECONNAISSANCE – T1590 – IP Addresses

T1590.002 – DNS

T1590.005 – IP Addresses

RECONNAISSANCE – T1592 – Client Configurations

T1592.004 – Client Configurations

RECONNAISSANCE – T1595 – Scanning IP Blocks

T1595.001 – Scanning IP Blocks

T1595.002 – Vulnerability Scanning

References

[1] https://www.bleepingcomputer.com/news/security/securing-helpdesks-from-hackers-what-we-can-learn-from-the-mgm-breach/

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
Rajendra Rushanth
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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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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