Anomaly-Based Threat Hunting: Darktrace's Approach in Action
This blog outlines Darktrace's model-based anomaly detection and how security teams can leverage custom models for targeted threat hunts. Recently, Darktrace's Threat Research team applied this method in their report, "AI & Cybersecurity: The State of Cyber in UK and US Energy Sectors."
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
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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08
May 2025
What is threat hunting?
Threat hunting in cybersecurity involves proactively and iteratively searching through networks and datasets to detect threats that evade existing automated security solutions. It is an important component of a strong cybersecurity posture.
There are several frameworks that Darktrace analysts use to guide how threat hunting is carried out, some of which are:
MITRE Attack
Tactics, Techniques, Procedures (TTPs)
Diamond Model for Intrusion Analysis
Adversary, Infrastructure, Victims, Capabilities
Threat Hunt Model – Six Steps
Purpose, Scope, Equip, Plan, Execute, Feedback
Pyramid of Pain
These frameworks are important in baselining how to run a threat hunt. There are also a combination of different methods that allow defenders diversity– regardless of whether it is a proactive or reactive threat hunt. Some of these are:
Hypothesis-based threat hunting
Analytics-driven threat hunting
Automated/machine learning hunting
Indicator of Compromise (IoC) hunting
Victim-based threat hunting
Threat hunting with Darktrace
At its core, Darktrace relies on anomaly-based detection methods. It combines various machine learning types that allows it to characterize what constitutes ‘normal’, based on the analysis of many different measures of a device or actor’s behavior. Those types of learning are then curated into what are called models.
Darktrace models leverage anomaly detection and integrate outputs from Darktrace Deep Packet Inspection, telemetry inputs, and additional modules, creating tailored activity detection.
This dynamic understanding allows Darktrace to identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign. On top of machine learning models for detection, there is also the ability to change and create models showcasing the tool’s diversity. The Model Editor allows security teams to specify values, priorities, thresholds, and actions they want to detect. That means a team can create custom detection models based on specific use cases or business requirements. Teams can also increase the priority of existing detections based on their own risk assessments to their environment.
This level of dexterity is particularly useful when conducting a threat hunt. As described above, and in previous ‘Inside the SOC’ blogs such a threat hunt can be on a specific threat actor, specific sector, or a hypothesis-based threat hunt combined with ‘experimenting’ with some of Darktrace’s models.
Conducting a threat hunt in the energy sector with experimental models
In Darktrace’s recent Threat Research report “AI & Cybersecurity: The state of cyber in UK and US energy sectors” Darktrace’s Threat Research team crafted hypothesis-driven threat hunts, building experimental models and investigating existing models to test them and detect malicious activity across Darktrace customers in the energy sector.
For one of the hunts, which hypothesised utilization of PerfectData software and multi-factor authentication (MFA) bypass to compromise user accounts and destruct data, an experimental model was created to detect a Software-as-a-Service (SaaS) user performing activity relating to 'PerfectData Software’, known to allow a threat actor to exfiltrate whole mailboxes as a PST file. Experimental model alerts caused by this anomalous activity were analyzed, in conjunction with existing SaaS and email-related models that would indicate a multi-stage attack in line with the hypothesis.
Whilst hunting, Darktrace researchers found multiple model alerts for this experimental model associated with PerfectData software usage, within energy sector customers, including an oil and gas investment company, as well as other sectors. Upon further investigation, it was also found that in June 2024, a malicious actor had targeted a renewable energy infrastructure provider via a PerfectData Software attack and demonstrated intent to conduct an Operational Technology (OT) attack.
The actor logged into Azure AD from a rare US IP address. They then granted Consent to ‘eM Client’ from the same IP. Shortly after, the actor granted ‘AddServicePrincipal’ via Azure to PerfectData Software. Two days later, the actor created a new email rule from a London IP to move emails to an RSS Feed Folder, stop processing rules, and mark emails as read. They then accessed mail items in the “\Sent” folder from a malicious IP belonging to anonymization network, Private Internet Access Virtual Private Network (PIA VPN) [1]. The actor then conducted mass email deletions, deleting multiple instances of emails with subject “[Name] shared "[Company Name] Proposal" With You” from the “\Sent folder”. The emails’ subject suggests the email likely contains a link to file storage for phishing purposes. The mass deletion likely represented an attempt to obfuscate a potential outbound phishing email campaign.
Figure 1: The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.
A month later, the same user was observed downloading mass mLog CSV files related to proprietary and Operational Technology information. In September, three months after the initial attack, another mass download of operational files occurred by this actor, pertaining to operating instructions and measurements, The observed patience and specific file downloads seemingly demonstrated an intent to conduct or research possible OT attack vectors. An attack on OT could have significant impacts including operational downtime, reputational damage, and harm to everyday operations. Darktrace alerted the impacted customer once findings were verified, and subsequent actions were taken by the internal security team to prevent further malicious activity.
Conclusion
Harnessing the power of different tools in a security stack is a key element to cyber defense. The above hypothesis-based threat hunt and custom demonstrated intent to conduct an experimental model creation demonstrates different threat hunting approaches, how Darktrace’s approach can be operationalized, and that proactive threat hunting can be a valuable complement to traditional security controls and is essential for organizations facing increasingly complex threat landscapes.
Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO at Darktrace) and Zoe Tilsiter (EMEA Consultancy Lead)
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.
Unpacking ClickFix: Darktrace’s detection of a prolific social engineering tactic
ClickFix is a social engineering technique that exploits human error through fake prompts, leading users to unknowingly run malicious commands. Learn how Darktrace detects and responds to such threats!
Darktrace investigated “PumaBot,” a Go-based Linux botnet targeting IoT devices. It avoids internet-wide scanning, instead using a C2 server to get targets and brute-force SSH credentials. Once inside, it executes remote commands and ensures persistence.
Unpacking ClickFix: Darktrace’s detection of a prolific social engineering tactic
What is ClickFix and how does it work?
Amid heightened security awareness, threat actors continue to seek stealthy methods to infiltrate target networks, often finding the human end user to be the most vulnerable and easily exploited entry point.
ClickFix baiting is an exploitation of the end user, making use of social engineering techniques masquerading as error messages or routine verification processes, that can result in malicious code execution.
Since March 2024, the simplicity of this technique has drawn attention from a range of threat actors, from individual cybercriminals to Advanced Persistent Threat (APT) groups such as APT28 and MuddyWater, linked to Russia and Iran respectively, introducing security threats on a broader scale [1]. ClickFix campaigns have been observed affecting organizations in across multiple industries, including healthcare, hospitality, automotive and government [2][3].
Actors carrying out these targeted attacks typically utilize similar techniques, tools and procedures (TTPs) to gain initial access. These include spear phishing attacks, drive-by compromises, or exploiting trust in familiar online platforms, such as GitHub, to deliver malicious payloads [2][3]. Often, a hidden link within an email or malvertisements on compromised legitimate websites redirect the end user to a malicious URL [4]. These take the form of ‘Fix It’ or fake CAPTCHA prompts [4].
From there, users are misled into believing they are completing a human verification step, registering a device, or fixing a non-existent issue such as a webpage display error. As a result, they are guided through a three-step process that ultimately enables the execution of malicious PowerShell commands:
Open a Windows Run dialog box [press Windows Key + R]
Automatically or manually copy and paste a malicious PowerShell command into the terminal [press CTRL+V]
And run the prompt [press ‘Enter’] [2]
Once the malicious PowerShell command is executed, threat actors then establish command and control (C2) communication within the targeted environment before moving laterally through the network with the intent of obtaining and stealing sensitive data [4]. Malicious payloads associated with various malware families, such as XWorm, Lumma, and AsyncRAT, are often deployed [2][3].
Based on investigations conducted by Darktrace’s Threat Research team in early 2025, this blog highlights Darktrace’s capability to detect ClickFix baiting activity following initial access.
Darktrace’s coverage of a ClickFix attack chain
Darktrace identified multiple ClickFix attacks across customer environments in both Europe, the Middle East, and Africa (EMEA) and the United States. The following incident details a specific attack on a customer network that occurred on April 9, 2025.
Although the initial access phase of this specific attack occurred outside Darktrace’s visibility, other affected networks showed compromise beginning with phishing emails or fake CAPTCHA prompts that led users to execute malicious PowerShell commands.
Darktrace’s visibility into the compromise began when the threat actor initiated external communication with their C2 infrastructure, with Darktrace / NETWORK detecting the use of a new PowerShell user agent, indicating an attempt at remote code execution.
Figure 1: Darktrace / NETWORK's detection of a device making an HTTP connection with new PowerShell user agent, indicating PowerShell abuse for C2 communications.
Download of Malicious Files for Lateral Movement
A few minutes later, the compromised device was observed downloading a numerically named file. Numeric files like this are often intentionally nondescript and associated with malware. In this case, the file name adhered to a specific pattern, matching the regular expression: /174(\d){7}/. Further investigation into the file revealed that it contained additional malicious code designed to further exploit remote services and gather device information.
Figure 2: Darktrace / NETWORK's detection of a numeric file, one minute after the new PowerShell User Agent alert.
The file contained a script that sent system information to a specified IP address using an HTTP POST request, which also processed the response. This process was verified through packet capture (PCAP) analysis conducted by the Darktrace Threat Research team.
By analyzing the body content of the HTTP GET request, it was observed that the command converts the current time to Unix epoch time format (i.e., 9 April 2025 13:26:40 GMT), resulting in an additional numeric file observed in the URI: /1744205200.
Figure 3: PCAP highlighting the HTTP GET request that sends information to the specific IP, 193.36.38[.]237, which then generates another numeric file titled per the current time.
Across Darktrace’s investigations into other customers' affected by ClickFix campaigns, both internal information discovery events and further execution of malicious code were observed.
Data Exfiltration
By following the HTTP stream in the same PCAP, the Darktrace Threat Research Team assessed the activity as indicative of data exfiltration involving system and device information to the same command-and-control (C2) endpoint, , 193.36.38[.]237. This endpoint was flagged as malicious by multiple open-source intelligence (OSINT) vendors [5].
Figure 4: PCAP highlighting HTTP POST connection with the numeric file per the URI /1744205200 that indicates data exfiltration to 193.36.38[.]237.
Further analysis of Darktrace’s Advanced Search logs showed that the attacker’s malicious code scanned for internal system information, which was then sent to a C2 server via an HTTP POST request, indicating data exfiltration
Figure 5: Advanced Search further highlights Darktrace's observation of the HTTP POST request, with the second numeric file representing data exfiltration.
Actions on objectives
Around ten minutes after the initial C2 communications, the compromised device was observed connecting to an additional rare endpoint, 188.34.195[.]44. Further analysis of this endpoint confirmed its association with ClickFix campaigns, with several OSINT vendors linking it to previously reported attacks [6].
In the final HTTP POST request made by the device, Darktrace detected a file at the URI /init1234 in the connection logs to the malicious endpoint 188.34.195[.]44, likely depicting the successful completion of the attack’s objective, automated data egress to a ClickFix C2 server.
Darktrace / NETWORK grouped together the observed indicators of compromise (IoCs) on the compromised device and triggered an Enhanced Monitoring model alert, a high-priority detection model designed to identify activity indicative of the early stages of an attack. These models are monitored and triaged 24/7 by Darktrace’s Security Operations Center (SOC) as part of the Managed Threat Detection service, ensuring customers are promptly notified of malicious activity as soon as it emerges.
Figure 6: Darktrace correlated the separate malicious connections that pertained to a single campaign.
Darktrace Autonomous Response
In the incident outlined above, Darktrace was not configured in Autonomous Response mode. As a result, while actions to block specific connections were suggested, they had to be manually implemented by the customer’s security team. Due to the speed of the attack, this need for manual intervention allowed the threat to escalate without interruption.
However, in a different example, Autonomous Response was fully enabled, allowing Darktrace to immediately block connections to the malicious endpoint (138.199.156[.]22) just one second after the initial connection in which a numerically named file was downloaded [7].
Figure 7: Darktrace Autonomous Response blocked connections to a suspicious endpoint following the observation of the numeric file download.
This customer was also subscribed to our Managed Detection and Response service, Darktrace’s SOC extended a ‘Quarantine Device’ action that had already been autonomously applied in order to buy their security team additional time for remediation.
Figure 8: Autonomous Response blocked connections to malicious endpoints, including 138.199.156[.]22, 185.250.151[.]155, and rkuagqnmnypetvf[.]top, and also quarantined the affected device. These actions were later manually reinforced by the Darktrace SOC.
Conclusion
ClickFix baiting is a widely used tactic in which threat actors exploit human error to bypass security defenses. By tricking end point users into performing seemingly harmless, everyday actions, attackers gain initial access to systems where they can access and exfiltrate sensitive data.
Darktrace’s anomaly-based approach to threat detection identifies early indicators of targeted attacks without relying on prior knowledge or IoCs. By continuously learning each device’s unique pattern of life, Darktrace detects subtle deviations that may signal a compromise. In this case, Darktrace's Autonomous Response, when operating in a fully autonomous mode, was able to swiftly contain the threat before it could progress further along the attack lifecycle.
Credit to Keanna Grelicha (Cyber Analyst) and Jennifer Beckett (Cyber Analyst)
Appendices
NETWORK Models
Device / New PowerShell User Agent
Anomalous Connection / New User Agent to IP Without Hostname
Anomalous Connection / Posting HTTP to IP Without Hostname
Anomalous Connection / Powershell to Rare External
Device / Suspicious Domain
Device / New User Agent and New IP
Anomalous File / New User Agent Followed By Numeric File Download (Enhanced Monitoring Model)
Security teams are drowning in vulnerability alerts, but only a fraction of those issues pose a real threat. The new Exploit Prediction Assessment feature in Darktrace / Attack Surface Management helps teams cut through the noise by validating which vulnerabilities on their external attack surface can be actively exploited.
Instead of relying solely on CVSS scores or waiting for patch cycles, Exploit Prediction Assessment uses safe, targeted simulations to test whether exposed systems can be compromised, delivering fast, evidence-based results in under 72 hours.
This capability augments traditional pen testing and complements existing ASM workflows by transforming passive discovery into actionable insight. With EPA, security teams move from reacting to long lists of potential vulnerabilities to making confident, risk-based decisions on what actually matters.
Key highlights of Exploit Prediction Assessment
Simulated attacks to validate real risk
Exploit Prediction Assessment conducts safe, simulated attacks on assets with potential security vulnerabilities that have been identified by Darktrace / Attack Surface Management. This real-time testing validates your systems' susceptibility to compromise by confirming which vulnerabilities are present and exploitable on your attack surface.
Prioritize what matters most
Confirmed security risks can be prioritized for mitigation, ensuring that the most critical threats are promptly addressed. This takes the existing letter ranking system and brings it a step further by drilling down to yet another level. Even in the most overwhelming situations, teams will be able to act on a pragmatic, clear-cut plan.
Fast results, tailored to your environment
Customers set the scope of the Exploit Prediction Assessment within Darktrace / Attack Surface Management and receive the results of the surgical vulnerability testing within 72 hours. Users will see 1 of 2 shields:
1. A green shield with a check mark: Meaning no vulnerabilities were found on scanned CVEs for the asset.
2. A red shield with a red x: Meaning at least one vulnerability was found on scanned CVEs for the asset.
Why it's a game changer
Traditionally, attack surface management tools have focused on identifying exposed assets and vulnerabilities but lacked the context to determine which issues posed the greatest risk. Without context on what’s exploitable, security teams are left triaging long lists of potential risks, operating in isolation from broader business objectives. This misalignment ultimately leads to both weakened risk posture and cross team communication and execution.
This is where Continuous Threat Exposure Management (CTEM) becomes essential. Introduced by Gartner, CTEM is a framework that helps organizations continuously assess, validate, and improve their exposure to real-world threats. The goal isn’t just visibility, it’s to understand how an attacker could move through your environment today, and what to fix first to stop them.
Exploit Prediction Assessment brings this philosophy to life within Darktrace / Attack Surface Management. By safely simulating exploit attempts against identified vulnerabilities, it validates which exposures are truly at risk—transforming ASM from a discovery tool into a risk-based decision engine.
This capability directly supports the validation and prioritization phases of CTEM, helping teams focus on exploitable vulnerabilities rather than theoretical ones. This shift from visibility to action reduces the risk of critical vulnerabilities in the technology stack being overlooked, turning overwhelming vulnerability data into focused, clear actionable insights.
As attack surfaces continue to grow and change, organizations need more than static scans they need continuous, contextual insight. Exploit Prediction Assessment ensures your ASM efforts evolve with the threat landscape, making CTEM a practical reality, not just a strategy.
Exploit Prediction Assessment in action
With Darktrace / Attack Surface Management organizations can get Exploit Prediction Assessment, and the cyber risk team no longer guesses which vulnerabilities matter most. Instead, they identify several externally exposed areas of their attack surface, then use the feature to surgically test for exploitability across these exposed endpoints. Within 72 hours, they receive a report:
Positive outcome: Based on information in the html or the headers it seems that a vulnerable software version is running on an externally exposed infrastructure. By running a targeted attack on this infrastructure, we can confirm that it cannot be abused.
Negative outcome: Based on information in the html or the headers it seems that a vulnerable software version is running on an externally exposed infrastructure. By running a targeted attack on this infrastructure, we can confirm that it can be exploited, so we can predict it being exploited.
This second outcome changes everything. The team immediately prioritizes the exploitable asset for patching and takes the necessary adjustments to mitigate exposure until the fix is deployed.
Instead of spreading their resources thin across dozens of alerts, they focus on what poses a real threat, saving time, reducing risk, and demonstrating actionable results to stakeholders.
Conclusion
Exploit Predication Assessment bolsters Darktrace’s commitment to proactive cybersecurity. It supports intelligent prioritization of vulnerabilities, keeping organizations ahead of emerging threats. With this new addition to / Attack Surface Management, teams have another tool to empower a more efficient approach to addressing security gaps in real-time.
Stay tuned for more updates and insights on how Darktrace continues to develop a culture of proactive security across the entire ActiveAI Security Platform.
![PCAP highlighting the HTTP GET request that sends information to the specific IP, 193.36.38[.]237, which then generates another numeric file titled per the current time.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/684068fac1b4368f672c17cc_Screenshot%202025-06-04%20at%208.40.32%E2%80%AFAM.png)
![PCAP highlighting HTTP POST connection with the numeric file per the URI /1744205200 that indicates data exfiltration to 193.36.38[.]237.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/6840694509d92b6220ccf5f1_Screenshot%202025-06-04%20at%208.41.48%E2%80%AFAM.png)
![Autonomous Response blocked connections to malicious endpoints, including 138.199.156[.]22, 185.250.151[.]155, and rkuagqnmnypetvf[.]top, and also quarantined the affected device. These actions were later manually reinforced by the Darktrace SOC.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/68406a1f723c9d6fda9c5268_Screenshot%202025-06-04%20at%208.45.23%E2%80%AFAM.png)
