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June 5, 2025

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!
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
Keanna Grelicha
Cyber Analyst
Woman on laptop in office buildingDefault blog image
05
Jun 2025

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:

  1. Open a Windows Run dialog box [press Windows Key + R]
  2. Automatically or manually copy and paste a malicious PowerShell command into the terminal [press CTRL+V]
  3. 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].

Attack timeline of ClickFix cyber attack

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.

Darktrace / NETWORK's detection of a device making an HTTP connection with new PowerShell user agent, indicating PowerShell abuse for C2 communications.
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.

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.
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].

PCAP highlighting HTTP POST connection with the numeric file per the URI /1744205200 that indicates data exfiltration to 193.36.38[.]237.
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

Advanced Search further highlights Darktrace's observation of the HTTP POST request, with the second numeric file representing 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.

Darktrace correlated the separate malicious connections that pertained to a single campaign.
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].

Darktrace Autonomous Response blocked connections to a suspicious endpoint following the observation of the numeric file download.
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.

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.
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)
  • Device / Initial Attack Chain Activity (Enhanced Monitoring Model)

Autonomous Response Models

  • Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block
  • Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block
  • Antigena / Network::External Threat::Antigena File then New Outbound Block
  • Antigena / Network::External Threat::Antigena Suspicious File Block
  • Antigena / Network::Significant Anomaly::Antigena Alerts Over Time Block
  • Antigena / Network::External Threat::Antigena Suspicious File Block

IoC - Type - Description + Confidence

·       141.193.213[.]11 – IP address – Possible C2 Infrastructure

·       141.193.213[.]10 – IP address – Possible C2 Infrastructure

·       64.94.84[.]217 – IP address – Possible C2 Infrastructure

·       138.199.156[.]22 – IP address – C2 server

·       94.181.229[.]250 – IP address – Possible C2 Infrastructure

·       216.245.184[.]181 – IP address – Possible C2 Infrastructure

·       212.237.217[.]182 – IP address – Possible C2 Infrastructure

·       168.119.96[.]41 – IP address – Possible C2 Infrastructure

·       193.36.38[.]237 – IP address – C2 server

·       188.34.195[.]44 – IP address – C2 server

·       205.196.186[.]70 – IP address – Possible C2 Infrastructure

·       rkuagqnmnypetvf[.]top – Hostname – C2 server

·       shorturl[.]at/UB6E6 – Hostname – Possible C2 Infrastructure

·       tlgrm-redirect[.]icu – Hostname – Possible C2 Infrastructure

·       diagnostics.medgenome[.]com – Hostname – Compromised Website

·       /1741714208 – URI – Possible malicious file

·       /1741718928 – URI – Possible malicious file

·       /1743871488 – URI – Possible malicious file

·       /1741200416 – URI – Possible malicious file

·       /1741356624 – URI – Possible malicious file

·       /ttt – URI – Possible malicious file

·       /1741965536 – URI – Possible malicious file

·       /1.txt – URI – Possible malicious file

·       /1744205184 – URI – Possible malicious file

·       /1744139920 – URI – Possible malicious file

·       /1744134352 – URI – Possible malicious file

·       /1744125600 – URI – Possible malicious file

·       /1[.]php?s=527 – URI – Possible malicious file

·       34ff2f72c191434ce5f20ebc1a7e823794ac69bba9df70721829d66e7196b044 – SHA-256 Hash – Possible malicious file

·       10a5eab3eef36e75bd3139fe3a3c760f54be33e3 – SHA-1 Hash – Possible malicious file

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique  

Spearphishing Link - INITIAL ACCESS - T1566.002 - T1566

Drive-by Compromise - INITIAL ACCESS - T1189

PowerShell - EXECUTION - T1059.001 - T1059

Exploitation of Remote Services - LATERAL MOVEMENT - T1210

Web Protocols - COMMAND AND CONTROL - T1071.001 - T1071

Automated Exfiltration - EXFILTRATION - T1020 - T1020.001

References

[1] https://www.logpoint.com/en/blog/emerging-threats/clickfix-another-deceptive-social-engineering-technique/

[2] https://www.proofpoint.com/us/blog/threat-insight/security-brief-clickfix-social-engineering-technique-floods-threat-landscape

[3] https://cyberresilience.com/threatonomics/understanding-the-clickfix-attack/

[4] https://www.group-ib.com/blog/clickfix-the-social-engineering-technique-hackers-use-to-manipulate-victims/

[5] https://www.virustotal.com/gui/ip-address/193.36.38.237/detection

[6] https://www.virustotal.com/gui/ip-address/188.34.195.44/community

[7] https://www.virustotal.com/gui/ip-address/138.199.156.22/detection

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
Keanna Grelicha
Cyber Analyst

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July 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

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Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

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Carlos Gray
Senior Product Marketing Manager, Email

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July 24, 2026

When Guardrails Break: Why Securing AI Requires Behavioral Detection and Autonomous Containment

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Bottom line up front: Governance, guardrails, identity controls, and secure development are necessary to secure AI, but they are not sufficient. AI systems are probabilistic, adaptive, and non-deterministic. Therefore, organizations need two critical layers of security:

  1. Behavioral-based detection that can identify when AI begins to act outside its intended purpose; and  
  2. Surgical, explainable autonomous containment that can stop risky activity before it causes material damage.  

That capability depends on multiple specialized AI models working together, not one LLM making every decision.

Organizations are embedding AI into development, business operations, and security workflows faster than most security programs can adapt. The risk is no longer limited to the model. It extends across prompts, data, identities, agents, memory, APIs, tools, permissions, and the trust relationships connecting them.

In my recent blog, Securing AI: Analysis of the Complete Security Stack with Governance and Controls, I outlined a defense-in-depth strategy spanning governance, identity, data security, secure development, runtime detection, autonomous containment, and recovery. The most urgent requirement across that architecture is the ability to understand how AI behaves in practice and contain it when that behavior becomes risky.  

Why non-deterministic systems require behavioral-based detection

Traditional controls remain foundational. Organizations need least privilege, strong identity controls, secure-by-design architecture, data governance, AI inventories, guardrails, testing, and clear boundaries on autonomy.

But deterministic controls, which assume predictable and repeatable behavior, cannot fully secure non-deterministic systems, where the same input may not always produce the same outcome.

AI agents can interpret the same instruction differently, chain individually authorized actions into an unsafe outcome, or pursue a legitimate goal through a method the organization did not anticipate. One of the most recent examples of this is the incident that OpenAI and Hugging Face jointly disclosed, where an autonomous agent escaped its intended testing boundaries and compromised Hugging Face infrastructure.  

An agent may have permission to access data and invoke a tool, but that does not mean every use of that access is appropriate. It is not enough to know whether an action is allowed. Organizations need to know whether it makes sense.

  • Is this normal for this agent?  
  • Is it acting within its intended purpose?  
  • Is it accessing unusual data, invoking an unexpected tool, or beginning to drift?  
  • Do a series of ordinary-looking actions become risky when viewed together?

Behavioral-based detection specific to an environment or organization with an understanding of context and risk enables provides the needed detection engineering for AI systems. It learns normal activity across people, systems, data, devices, and AI agents, then identifies deviations and evaluates their risk, intent, and context. This enables detection of misuse, abuse, compromise, manipulation, and unintended behavior even when no known attack signature or explicit policy violation exists.

Why accuracy is the foundation for SOC optimization

AI will only improve the SOC if it produces accurate, explainable, and actionable outcomes.

If analysts must manually validate every AI-generated finding because they cannot understand the evidence or confidence behind it, automation has not reduced workload. It has moved the workload. False positives increase fatigue. False negatives cause the most risk and damage to organizations. Inaccurate autonomous actions can disrupt critical operations.

Accuracy is therefore more than a model-performance metric. It is the prerequisite for analyst trust, SOC optimization, and safe autonomous response.

That accuracy is unlikely to come from one model.

Generative AI is valuable for natural-language analysis, summarization, and human interaction. But an LLM should not be the sole analytical engine for behavioral-based detection, investigation, risk assessment, and containment. Interpretability and consistency are required for high-consequence security decisions.

A stronger architecture uses multiple specialized AI systems collaboratively:  

  • Behavioral models can establish normal activity.  
  • Unsupervised learning can identify novel anomalies.  
  • Graph analysis can evaluate relationships among agents, identities, systems, and tools.  
  • Other models can correlate events, investigate competing hypotheses, and assess risk.  
  • Semantic models can analyze language where behavior-based language analysis is needed but this can be used in tandem with vector embeddings, graph neural networks, and a variety of other AI systems.

Each model contributes a different analytical perspective. Their outputs can corroborate one another, improving accuracy and creating a more reliable basis for response. The objective is not one model operating as an oracle. It is layered, adaptive intelligence designed to produce decisions the SOC can understand and trust.

Autonomous containment is required to secure autonomous systems

Many SOCs remain hesitant to trust LLM-based agents with autonomous containment. That concern is reasonable. A poorly selected response can isolate the wrong asset, stop a critical workflow, block a legitimate identity, or create more operational damage than the original incident.

But relying exclusively on human response is also not viable.

AI systems can operate at machine speed. They can expose sensitive data, execute workflows, modify records, call tools, or propagate actions across connected systems before an analyst can investigate and intervene. The behavior may be unintentional, the result of an agent optimizing toward a goal, or caused by misuse, compromise, prompt injection, or offensive AI.

Intent affects the investigation. It does not change the need to stop the damage.

Organizations need autonomous response, but it must be surgical and explainable. The objective is not to shut down an entire agent, user, application, or business process whenever an anomaly occurs. It is to interrupt the specific risky behavior: block an unusual connection, constrain a tool call, stop an abnormal data transfer, or temporarily limit an agent when it is performing anomalous, risky activity.  

That buys humans time. It stops the spread, limits damage, and allows the SOC to investigate without unnecessarily disrupting the business.

Layered, Adaptive AI provides a path forward

Darktrace has spent more than a decade researching and operationalizing layered, behavioral, Adaptive AI that learns a specific organization rather than relying only on historic attacks or predefined signatures.

The approach is designed to understand normal behavior, identify anomalous activity, assess its risk, correlate related events, autonomously investigate, and, when necessary, apply targeted containment while normal operations continue.

That sequence matters. Autonomous response cannot simply be added to the end of an LLM workflow. Trusted containment depends on broad visibility, continuous behavioral understanding, multiple analytical techniques, risk and context evaluation, autonomous investigation, explainability, and precise response actions.

This represents a more responsible model for security autonomy: not automation for its own sake, but controlled autonomy built to improve security outcomes and protect business operations.

Security must enable AI adoption

The answer for security teams is not to block AI. Organizations are adopting it to improve productivity, accelerate development, and create new business value.

But innovation without behavioral detection and autonomous containment is not sustainable.

Organizations should continue investing in governance, identity, least privilege, data security, secure MLOps, guardrails, testing, evaluation, validation, verification, kill switches, rollback, and forensic readiness. At the same time, they cannot wait for every governance program to mature before addressing runtime risk.

Behavioral-based detection and autonomous containment provide an immediate layer of resilience. They allow organizations to detect exploitation and risky AI behavior they did not anticipate, contain it at machine speed, and preserve human control over broader remediation.

The future of AI security will not be defined by a single model making every decision. It will be defined by multiple specialized AI systems working collaboratively, with sufficient accuracy, transparency, and context to support trusted autonomous action.

Surgical, explainable autonomous containment is no longer a future capability. It is a requirement for scaling AI securely today.

Learn how to build a defense-in-depth strategy for securing AI at scale in our talk at Black Hat on August 5 at 3:15 PM.  

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About the author
Nicole Carignan
SVP, Security & AI Strategy, Field CISO
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