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March 20, 2025

Cyberhaven Supply Chain Attack: Exploiting Browser Extensions

In late 2024, Darktrace detected unusual activity linked to Cyberhaven's Chrome browser extension. Read more about Darktrace’s investigation here.
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
Rushanth Ramanathan
Cyber Analyst
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20
Mar 2025

The evolution of supply chain attacks

Supply chain attacks are becoming increasingly sophisticated. As network defenses improve, threat actors continuously adapt and refine their tactics, techniques, and procedures (TTPs) to achieve their goals. In recent years, this has led to a rise in the exploitation of trusted services and software, including legitimate browser extensions. Exploitation of these extensions can provide adversaries with a stealthy means to infiltrate target networks and access high-value accounts undetected.

A notable example of this trend was the compromise of the Cyberhaven Chrome extension at the end of 2024. This incident appeared to be part of a broader campaign targeting multiple Chrome browser extensions, highlighting the evolving nature of supply chain attacks [1].

What is Cyberhaven?

Cyberhaven, a US-based data security organization, experienced a security breach on December 24, 2024, when a phishing attack reportedly compromised one of their employee's credentials [2]. This allowed attackers to publish a malicious version of the Cyberhaven Chrome extension, which exfiltrated cookies and authenticated sessions from targeted websites. The malicious extension was active from December 25 to December 26 – a time when most businesses and employees were out of office and enjoying the festive period, a fact not lost on threat actors. The attackers, likely a well-organized and financially motivated group, compromised more than 30 additional Chrome extensions, affecting more than 2.6 million users [3]. They used sophisticated phishing techniques to authorize malicious OAuth applications, bypassing traditional security measures and exploiting vulnerabilities in OAuth authorizations. The primary motive appeared to be financial gain, targeting high-value platforms like social media advertising and AI services [4].

In late December 2024, multiple Darktrace customers were compromised via the Cyberhaven Chrome extension; this blog will primarily focus on Darktrace / NETWORK detections from one affected customer.

Darktrace’s coverage of Cyberhaven compromises

On December 26, 2024, Darktrace identified a series of suspicious activities across multiple customer environments, uncovering a structured attack sequence that progressed from initial intrusion to privilege escalation and data exfiltration. The attack was distributed through a malicious update to the Cyberhaven Chrome extension [2]. The malicious update established a foothold in customer environments almost immediately, leading to further anomalies.

As with other Chrome browser extensions, Cyberhaven Chrome extensions were updated automatically with no user interaction required. However, in this instance, the automatic update included a malicious version which was deployed to customer environments. This almost immediately introduced unauthorized activity, allowing attackers to establish a foothold in customer networks. The update allowed attackers to execute their objectives in the background, undetected by traditional security tools that rely on known indicators of compromise (IoCS) rather than identifying anomalies.

While multiple customer devices were seen connecting to cyberhaven[.]io, a legitimate Cyberhaven domain, Darktrace detected persistent beaconing behavior to cyberhavenext[.]pro, which appeared to be attempting to masquerade as another legitimate Cyberhaven domain. Darktrace recognized this activity as unusual, triggering several model alerts in Darktrace / NETWORK to highlight the persistent outbound connections to the suspicious domain.

Further analysis of external connectivity patterns indicated  an increase in anomalous HTTP requests alongside this beaconing activity. Multiple open-source intelligence (OSINT) sources also suggest that the cyberhavenext[.]pro endpoint is associated with malicious activities [5].

Darktrace / NETWORK’s detection of beaconing activity to cyberhavenext[.]pro
Figure 1: Darktrace / NETWORK’s detection of beaconing activity to cyberhavenext[.]pro

Analysis using Darktrace’s Advanced Search revealed that some of these connections were directed to the suspicious external IP address 149.28.124[.]84. Further investigation confirmed that the IP correlated with two SSL hostnames, including the malicious cyberhavenext[.]pro, further reinforcing its connection to the attack infrastructure.

Darktrace Advanced Search analysis showing the IP address 149.28.124[.]84 correlating to two SSL hostnames, one of which is cyberhavenext[.]pro.
Figure 2: Darktrace Advanced Search analysis showing the IP address 149.28.124[.]84 correlating to two SSL hostnames, one of which is cyberhavenext[.]pro.

Between December 23 and December 27, Darktrace observed sustained beaconing-like activity from affected devices on the customer’s network.

Darktrace’s detection of beaconing activities from a customer device to the endpoint 149.28.124[.]84 between December 23 and December 27.
Figure 3: Darktrace’s detection of beaconing activities from a customer device to the endpoint 149.28.124[.]84 between December 23 and December 27.

Darktrace observed 27 unique devices connecting to the malicious command-and-control (C2) infrastructure as far back as December 3. While most connections were brief, they represented an entry point for malicious activity. Over a two-day period, two devices transmitted 5.57 GiB of incoming data and 859.37 MiB of outgoing data, generating over 3 million log events across SSL, HTTP, and connection data.

Subsequent analysis identified a significant increase in unauthorized data transfers to the aforementioned 149.28.124[.]84 IP on another customer network, highlighting the potential broader impact of this compromise. The volume and frequency of these transfers suggested that attackers were leveraging automated data collection techniques, further underscoring the sophistication of the attack.

Darktrace’s detection of the likely exfiltration of 859.37 MiB to the endpoint 149.28.124[.]84.
Figure 4: Darktrace’s detection of the likely exfiltration of 859.37 MiB to the endpoint 149.28.124[.]84.

External research suggested that once active, the Cyberhaven extension would begin silently collecting session cookies and authentication tokens, specifically targeting high-value accounts such as Facebook Ads accounts [4]. Darktrace’s analysis of another affected customer noted many HTTP POST connections directed to a specific URI ("ai-cyberhaven"), while GET requests contained varying URIs prefixed with "/php/urlblock?args=AAAh....--redirect." This activity indicated an exfiltration mechanism, consistent with techniques observed in other compromised Chrome extensions. By compromising session cookies, attackers could potentially gain administrative access to connected accounts, further escalating their privileges [4].

Conclusion

This incident highlights the importance of monitoring not just endpoint security, but also cloud and browser-based security solutions, as attackers increasingly target these trusted and oft overlooked vectors.

Ultimately, by focusing on anomaly detection and behavioral analysis rather than static signatures and lists of ‘known bads’, Darktrace was able to successfully detect devices affected by the Cyberhaven Chrome browser extension compromise, by identifying activity that would likely have been considered legitimate and benign by traditional security solutions.

This compromise also serves as a reminder that supply chain attacks are not limited to traditional software vendors. Browser extensions, cloud-based applications, and SaaS services are equally vulnerable, as evidenced by Darktrace's detection of Balada Injector malware exploiting WordPress vulnerabilities to gain unauthorized network access [6]. Therefore, increased targeting of browser-based security tools, and a greater exploitation of OAuth and session hijacking techniques are to be expected. Attackers will undoubtedly refine their methods to infiltrate legitimate vendors and distribute malicious updates through trusted channels. By staying informed, vigilant, and proactive, organizations can mitigate exposure to evolving supply chain threats and safeguard their critical assets from emerging browser-based attack techniques.

Credit to Rajendra Rushanth (Cyber Analyst) Justin Torres (Senior Cyber Analyst) and Ryan Traill (Analyst Content Lead)

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Appendices

Darktrace Model Detections

·       Compromise / Beaconing Activity To External Rare (AP: C2 Comms)

·       Compromise / Beacon for 4 Days (AP: C2 Comms)

·       Compromise / HTTP Beaconing to Rare Destination (AP: C2 Comms)

·       Device / Suspicious Domain (AP: C2 Comms, AP: Tooling)

·       Compromise / Sustained TCP Beaconing Activity To Rare Endpoint (AP: C2 Comms)

·       Anomalous Server Activity / Rare External from Server (AP: C2 Comms)

·       Anomalous Connection / Multiple Failed Connections to Rare Endpoint (AP: C2 Comms)

·       Anomalous Server Activity / Anomalous External Activity from Critical Network Device (AP: C2 Comms)

·       Compromise / Slow Beaconing Activity To External Rare (AP: C2 Comms)

·       Compromise / Repeating Connections Over 4 Days (AP: C2 Comms)

·       Anomalous Connection / Multiple HTTP POSTs to Rare Hostname (AP: C2 Comms)

·       Anomalous Server Activity / Outgoing from Server (AP: C2 Comms)

·       Compromise / High Volume of Connections with Beacon Score (AP: C2 Comms)

·       Compromise / Large Number of Suspicious Failed Connections (AP: C2 Comms)

·       Email Nexus / Connection to Hijacked Correspondent Link

·       Compromise / Suspicious TLS Beaconing To Rare External (AP: C2 Comms)

·       Compromise / Quick and Regular Windows HTTP Beaconing (AP: C2 Comms)

List of IoCs

IoC - Type - Description + Confidence

cyberhavenext[.]pro - Hostname - Used for C2 communications and data exfiltration (cookies and session tokens)

149.28.124[.]84 - IP - Associated with malicious infrastructure

45.76.225[.]148 - IP - Associated with malicious infrastructure

136.244.115[.]219 - IP - Associated with malicious infrastructure

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique

INITIAL ACCESS - T1176 - Browser Extensions

EXECUTION - T1204.002 - Malicious Browser Extensions

PERSISTENCE - T1176 - Browser Extensions

COMMAND AND CONTROL - T1071.001 - Web Protocols

COMMAND AND CONTROL - T1001 - Data Obfuscation

CREDENTIAL ACCESS - T1539 - Steal Web Session Cookie

DISCOVERY - T1518.001 - Security Software Discovery

LATERAL MOVEMENT - T1557.003 - Man-in-the-Browser

EXFILTRATION - T1041 - Exfiltration Over C2 Channel

EXFILTRATION - T1567.002 - Exfiltration to Cloud Storage

IMPACT - T1583.006 - Session Hijacking

References

[1] https://thehackernews.com/2024/12/16-chrome-extensions-hacked-exposing.html

[2] https://www.cyberhaven.com/blog/cyberhavens-chrome-extension-security-incident-and-what-were-doing-about-it

[3] https://www.infosecurity-magazine.com/news/chrome-browser-extensions-hijacked/

[4] https://www.theverge.com/2024/12/28/24330758/chrome-extension-cyberhaven-hijack-phishing-cyberattack-facebook-ads-authentication-theft

[5] https://www.virustotal.com/gui/domain/cyberhavenext.pro

[6] https://darktrace.com/blog/balada-injector-darktraces-investigation-into-the-malware-exploiting-wordpress-vulnerabilities

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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
Rushanth Ramanathan
Cyber Analyst

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September 17, 2026

The Problem of Re-defining Human Value in the Agentic Age

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Newsfeeds are constantly informing us about the rapid escalation of agentic AI systems. These systems move far beyond simple machine-based computations, and  the focused, defined and bounded assistance that most AI systems started out as.

The next evolution of AI will harness the agentic properties of orchestration, automation, and heightened value-chains in IT, taking on the burden of workflow management, not just workflow delivery.

In nearly all of these instances, promises are made such as ‘this will free up human time’ or ‘this will allow people to focus on higher-order strategy’. However that message is delivered, one thing is clear: in ceding the orchestration and management of work to increasingly sophisticated AI agents and agentic systems, human value will be elevated to a particular and specific layer: the ability to judge its outputs.  As AI takes on more tasks, humans should be able to focus on a higher level of governance; making sure the decisions that AI offers us are ethical, responsible and worthwhile.  

But there are two major problems with that approach.

This blog discusses the problem of how Agentic systems are re-shaping how we review information, where we fit in, and when we make decisions.  It also discusses the problem of how the increased flow of confident, generated information affects the way we make judgement.  This blog considers how human judgement needs to adapt, and how a behavioral defense approach – using techniques pioneered by Darktrace – can help us do that.

The challenge of knowing where human judgement belongs

As we confer more automated decision-making to agentic systems, it might look increasingly less like ‘granting permissions’, and more like ‘surrendering authority’.  

The judgement layer for AI-generated work is not a fixed boundary. We have become used to the idea of a ‘human in the loop’ (HITL) and, historically, relationships between humans and IT systems were reasonably clear and bounded.  Computer and software systems were programmed to carry out certain tasks or automated functions, and humans could control the gates and decision points where actions were undertaken. Even across highly complex computational workflows, human interaction was a controllable node within the process; we were able to configure and regulate. But in the agentic age, where that human interaction sits, and what it can influence shifts every time AI systems are granted autonomy.  

This leads us to the first problem: if humans are moving themselves (or are being moved) into the ‘judgement’ part of the value chain, exactly where and when do we exercise that judgement?  

Humans are no longer the sole shepherds of computer-based or software-controlled outputs.  We are at times at least one step further (and slower) behind the new agentic shepherds.  We might also be blind to what they are doing.  Not only might we be removed and blind to the actions of our AI shepherds, but with the challenge of unknown, unapproved AI systems operating beyond our control, humans might not even know that our work is being shepherded by an AI at all.  Simply put, with the advent of greater levels of autonomy and orchestration, humans are at risk of not even knowing where to apply our newly-extended powers of strategic judgement.

Shadow AI – the use of unapproved AI systems or processes – is a growing threat to the role of effective governance and oversight. Shadow AI isn't just the AI you can't see. Its the AI you already know about being used in an unapproved way. The ability to generate effective oversight of the AI systems you use (or that are used on your behalf) will be increasingly important to ensure that human judgement in the AI value chain is effective, and deliberately placed.

The problem of what makes good judgement

The second problem lies in how flawed human judgement can be.  Humans are historically, notoriously, and, sometimes dangerously, unreliable when it comes to exercising judgement.  Humans are prone to the worst kinds of bias, the seduction of malign influence, and the sometimes-overwhelming urge to succeed. AI has long had a known flaw of operating with sycophancy, providing outputs that tend to agree with or flatter the human user.  But as AI grows ever more effective, there is a risk of both hyper-enablement (where humans increasingly and knowingly enable AI despite potential harm), as well as the greater risk of suggestion. Both of these aspects could skew the newly-elevated input of human judgement.

Imagine a highly competent AI system that has just orchestrated and managed a dizzying array of processes and workflows.  The AI is designed to present the human decision-maker with recommendations; based on analysis, comparison and other programmed factors.  This is where the human judgement layer is enabled.  But what if that judgement is summarily diffused by an AI-based recommendation that emulates the decision, provides plausible but unattractive alternatives, then suggests (or, worse, directs) the human end-user to take a particular course of action.

The risk here is that you are given a recommendation, tailored to your preferences (which the AI has learned, or which you have divulged), and which appears to make perfect sense.  It appears to be a well-weighted recommendation, with sound arguments that tap into our inherent biases or inclinations so that a specific decision-path is followed. With the growth of agentic systems specifically designed to match user profiles (from Cowork agents to ‘digital twin’ models), the likelihood of agentic influence could badly skew human judgement or, at the least, devalue the proposition that humans are taking a higher-layer of strategic control over AI-based decisions.

If AI convincingly recommends something that may be problematic, it can be difficult to discern both accurate data, and the context required to make the right judgement.

Given the two problems described above, the job of exercising valuable human judgement in the agentic age can draw down to these two questions:

  • When should humans intervene in the agentic process?  
  • How can we make the best possible judgement calls?

What humans contribute that AI cannot

For all the flaws that make human judgement unreliable, people have the edge over even the most sophisticated and powerful AI systems when it comes to issues such as ethics and social context.  An AI system can, with startling granularity, rank the value of adopting a new business proposal: offering predictive metrics on costs, returns, market value, time-to-deliver operations, conformance with legal registers, etc.  But it can’t tell if the business proposal is ethically sound, or if the business venture will potentially affect groups outside of the analyzed proposal. It can’t tell you if the CEO has a ‘bad feeling’ about this effort.  It can’t tell you if this is the right thing to do.  

The ability to add social context, balance complex interpersonal dynamics, understand nuance, and to go beyond what seems economically reasonable is where human judgement can add value.  

Human judgement is difficult to encapsulate in metrics. And the way we train our development may need to adapt too. Rather than building up a gradual, experiential knowledge base, we should think about training the skill of judgement itself; especially for an agentic age.

How behavioral security strengthens AI governance

If this all feels like a vicious circle (‘I need AI help to make good judgements’ / ‘AI can twist what I need to judge’) it needn’t be. The key to this is having a defense-in-depth approach, with tools that can actually help.

This is precisely where behavioral security becomes important. The complex and nuanced way that humans exercise judgement is often rooted in our ability to recognize behavior that doesn't look right. We may not always be able to articulate it immediately, but we can often identify when an action, recommendation, or outcome feels inconsistent with the context around it. As AI systems take on more responsibility across the decision chain, preserving that ability to recognize meaningful deviations becomes increasingly important.

Darktrace’s / SECURE AI is designed to do exactly that. It applies behavioral security to AI ecosystems, helping organizations understand how people, AI tools, identities, and agents interact across the business. By learning the patterns of normal AI usage and surfacing activity that deviates from those patterns, it provides security teams with the context needed to investigate risk, understand unusual behavior, and make informed governance decisions. Rather than relying solely on predefined rules or assumptions, this behavioral understanding helps organizations distinguish between expected AI activity and behavior that warrants closer scrutiny.

This matters because we are already in an era of information overload. If humans are expected to elevate their value through strategic judgement, the ability to do this without being overwhelmed by data (good or bad) will be critical.  

We need the ability to discern when we're being misled by AI, and whether our judgement calls are being made on the basis of accurate, contextual information. Darktrace / SECURE AI provides that additional layer of defensive security for activity we cannot easily see. Whether it is suspected Shadow AI or skewed recommendations, the net result is a protected organization, where users can more effectively use AI to make positive judgements.

For those where that judgement is a critical skill (both individuals, as well as those working in security teams), improving our metacognition - the ability to understand information in a broader context - will supercharge the value of human judgement. When those judgements are grounded in context rather than assumptions we have better information to make sound decisions.

Conclusion

Human judgement is a skill that is honed over time and experience.  Darktrace’s / SECURE AI employs the same principles, but at machine-speed. Rather than influencing or directing, Darktrace / SECURE AI offers AI-enabled assurance; providing human-based judgement with the right context to make a balanced decision.  

What we judge can be valued by the legitimacy of its outputs. For AI, those outputs are valued on the speed and accuracy of the information provided.  Increasingly for humans, the value of our outputs will be based on the validity of our judgement, and how we justify our decisions in ways that engineer confidence.  

Humans often know more than we can express, while AI is prone to expressing more than it truly understands. Humans can bridge the context AI often fails to appreciate. When that judgement is supported by relevant, impartial AI systems, this is the future space where good AI governance will be exercised.

Discover Darktrace / SECURE AI.

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Jason Lusted
AI Governance Advisor

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September 4, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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About the author
Dr. Tim Bazalgette
Chief AI Officer, Darktrace
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