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September 26, 2024

Thread Hijacking: Infiltration Tactics Explained

Read about thread hijacking and how attackers exploit trusted conversations, compromising network security and user data. Stay informed.
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
Maria Geronikolou
Cyber Analyst
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26
Sep 2024

What is thread hijacking?

Cyberattacks are becoming increasingly stealthy and targeted, with malicious actors focusing on high-value individuals to gain privileged access to their organizations’ digital environments. One technique that has gained prominence in recent years is thread hijacking. This method allows attackers to infiltrate ongoing conversations, exploiting the trust within these threads to access sensitive systems.

Thread hijacking typically involves attackers gaining access to a user’s email account, monitoring ongoing conversations, and then inserting themselves into these threads. By replying to existing emails, they can send malicious links, request sensitive information, or manipulate the conversation to achieve their goals, such as redirecting payments or stealing credentials. Because such emails appear to come from a trusted source, they often bypass human security teams and traditional security filters.

How does thread hijacking work?

  1. Initial Compromise: Attackers first gain access to a user’s email account, often through phishing, malware, or exploiting weak passwords.
  2. Monitoring: Once inside, they monitor the user’s email threads, looking for ongoing conversations that can be exploited.
  3. Infiltration: The attacker then inserts themselves into these conversations, often replying to existing emails. Because the email appears to come from a trusted source within an ongoing thread, it bypasses many traditional security filters and raises less suspicion.
  4. Exploitation: Using the trust established in the conversation, attackers can send malicious links, request sensitive information, or manipulate the conversation to achieve their goals, such as redirecting payments or stealing credentials.

A recent incident involving a Darktrace customer saw a malicious actor attempt to manipulate trusted email communications, potentially exposing critical data. The attacker created a new mailbox rule to forward specific emails to an archive folder, making it harder for the customer to notice the malicious activity. This highlights the need for advanced detection and robust preventive tools.

Darktrace’s Self-Learning AI is able to recognize subtle deviations in normal behavior, whether in a device or a Software-as-a-Service (SaaS) user. This capability enables it to detect emerging attacks in their early stages. In this post, we’ll delve into the attacker’s tactics and illustrate how Darktrace / IDENTITY™ successfully identified and mitigated a thread hijacking attempt, preventing escalation and potential disruption to the customer’s network.

Thread hijacking attack overview & Darktrace coverage

On August 8, 2024, Darktrace detected an unusual email received by a SaaS account on a customer’s network. The email appeared to be a reply to a previous chain discussing tax and payment details, likely related to a transaction between the customer and one of their business partners.

Headers of the suspicious email received.
Figure 1: Headers of the suspicious email received.

A few hours later, Darktrace detected the same SaaS account creating a new mailbox rule named “.”, a tactic commonly used by malicious actors to evade detection when setting up new email rules [2]. This rule was designed to forward all emails containing a specific word to the user’s “Archives” folder. This evasion technique is typically used to move any malicious emails or responses to a rarely opened folder, ensuring that the genuine account holder does not see replies to phishing emails or other malicious messages sent by attackers [3].

Darktrace recognized the newly created email rule as suspicious after identifying the following parameters:

  • AlwaysDeleteOutlookRulesBlob: False
  • Force: False
  • MoveToFolder: Archive
  • Name: “.”
  • FromAddressContainsWords: [Redacted]
  • MarkAsRead: True
  • StopProcessingRules: True

Darktrace also noted that the user attempting to create this new email rule had logged into the SaaS environment from an unusual IP address. Although the IP was located in the same country as the customer and the ASN used by the malicious actor was typical for the customer’s network, the rare IP, coupled with the anomalous behavior, raised suspicions.

Figure 2: Hijacked SaaS account creating the new mailbox rule.

Given the suspicious nature of this activity, Darktrace’s Security Operations Centre (SOC) investigated the incident and alerted the customer’s security team of this incident.

Due to a public holiday in the customer's location (likely an intentional choice by the threat actor), their security team did not immediately notice or respond to the notification. Fortunately, the customer had Darktrace's Autonomous Response capability enabled, which allowed it to take action against the suspicious SaaS activity without human intervention.

In this instance, Darktrace swiftly disabled the seemingly compromised SaaS user for 24 hours. This action halted the spread of the compromise to other accounts on the customer’s SaaS platform and prevented any sensitive data exfiltration. Additionally, it provided the security team with ample time to investigate the threat and remove the user from their environment. The customer also received detailed incident reports and support through Darktrace’s Security Operations Support service, enabling direct communication with Darktrace’s expert Analyst team.

Conclusion

Ultimately, Darktrace’s anomaly-based detection allowed it to identify the subtle deviations from the user’s expected behavior, indicating a potential compromise on the customer’s SaaS platform. In this case, Darktrace detected a login to a SaaS platform from an unusual IP address, despite the attacker’s efforts to conceal their activity by using a known ASN and logging in from the expected country.

Despite the attempted SaaS hijack occurring on a public holiday when the customer’s security team was likely off-duty, Darktrace autonomously detected the suspicious login and the creation of a new email rule. It swiftly blocked the compromised SaaS account, preventing further malicious activity and safeguarding the organization from data exfiltration or escalation of the compromise.

This highlights the growing need for AI-driven security capable of responding to malicious activity in the absence of human security teams and detect subtle behavioral changes that traditional security tools.

Credit to: Ryan Traill, Threat Content Lead for his contribution to this blog

Appendices

Darktrace Model Detections

SaaS / Compliance / Anomalous New Email Rule

Experimental / Antigena Enhanced Monitoring from SaaS Client Block

Antigena / SaaS / Antigena Suspicious SaaS Activity Block

Antigena / SaaS / Antigena Email Rule Block

References

[1] https://blog.knowbe4.com/whats-the-best-name-threadjacking-or-man-in-the-inbox-attacks

[2] https://darktrace.com/blog/detecting-attacks-across-email-saas-and-network-environments-with-darktraces-combined-ai-approach

[3] https://learn.microsoft.com/en-us/defender-xdr/alert-grading-playbook-inbox-manipulation-rules

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
Maria Geronikolou
Cyber Analyst

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May 12, 2026

Resilience at the Speed of AI: Defending the Modern Campus with Darktrace

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Why higher education is a different cybersecurity battlefield

After four decades in IT, now serving as both CIO and CISO, I’ve learned one simple truth: cybersecurity is never “done.” It’s a constant game of cat and mouse. Criminals evolve. Technologies advance. Regulations expand. But in higher education, the challenge is uniquely complex.

Unlike a bank or a military installation, we can’t lock down networks to a narrow set of approved applications. Higher education environments are open by design. Students collaborate globally, faculty conduct cutting-edge research, and administrators manage critical operations, all of which require seamless access to the internet, global networks, cloud platforms, and connected systems.

Combine that openness with expanding regulatory mandates and tight budgets, and the balancing act becomes clear.

Threat actors don’t operate under the same constraints. Often well-funded and sponsored by nation-states with significant resources, they’re increasingly organized, strategic, and innovative.

That sophistication shows up in the tactics we face every day, from social engineering and ransomware to AI-driven impersonation attacks. We’re dealing with massive volumes of data, countless signals, and a very small window between detection and damage.

No human team, no matter how talented or how numerous, can manually sift through that noise at the speed required.

Discovering a force multiplier

Nothing in cybersecurity is 100% foolproof. I never “set it and forget it.” But for institutions balancing rising threats and finite resources, the Darktrace ActiveAI Security Platform™ offers something incredibly valuable: peace of mind through speed and scale.

It closes the gap between detection and response in a way humans can’t possibly match. At the speed of light, it can quarantine, investigate, and contain anomalous activity.

I’ve purchased and deployed Darktrace three separate times at three different institutions because I’ve seen firsthand what it can do and what it enables teams like mine to achieve.

I first encountered Darktrace while serving as CIO for a large multi-campus college system. What caught my attention was Darktrace's Self-Learning AI, and its ability to learn what "normal" looked like across our network. Instead of relying solely on static signatures or rigid rules, Darktrace built a behavioral baseline unique to our environment and alerted us in real time when something simply didn’t look right.

In higher education, where strict lockdowns aren’t realistic, that behavioral model made all the difference. We deployed it across five campuses, and the impact was immediate. Operating 24/7, Darktrace surfaced threats in ways our team couldn’t replicate manually.

Over time, the Darktrace platform evolved alongside the changing threat landscape, expanding into intrusion prevention, cloud visibility, and email security. At subsequent institutions, including Washington College, Darktrace was one of my first strategic investments.

Revealing the hidden threat other tools missed

One of the most surprising investigations of my career involved a data leak. Leadership suspected sensitive information from high-level meetings was being exposed, but our traditional tools couldn’t provide any answers.

Using Darktrace’s deep network visibility, down to packet-level data, we traced unusual connections to our CCTV camera system, which had been configured with a manufacturer’s default password. A small group of employees had hacked into the CCTV cameras, accessed audio-enabled recordings from boardroom meetings, and stored copies locally.

No other tool in our environment could have surfaced those connections the way Darktrace did. It was a clear example of why using AI to deeply understand how your organization, systems, and tools normally behave, matters: threats and risks don’t always look the way we expect.

Elevating a D-rating into a A-level security program

When I arrived at my last CISO role, the institution had recently experienced a significant ransomware attack. Attackers located  data  which informed their setting  ransom demands to an amount they knew would likely result in payment. It was a sobering example of how calculated and strategic modern cybercriminals have become.

Third-party cyber ratings reflected that reality, with a  D rating.

To raise the bar, we implemented a comprehensive security program and integrated layered defenses; -deploying state of the art tools and methods-  across the environment, with Darktrace at its core.

After a 90-day learning period to establish our behavioral baseline, we transitioned the platform into fully autonomous mode. In a single 30-day span, Darktrace conducted more than 2,500 investigations and autonomously resolved 92% of all false positives.

For a small team, that’s transformative. Instead of drowning in alerts, my staff focused on less than  200 meaningful cases that warranted human review.

Today, we maintain a perfect A rating from third-party assessors and have remained cybersafe.

Peace of mind isn’t about complacency

The effect of Darktrace as a force multiplier has a real human impact.

With the time reclaimed through automation, we expanded community education programs and implemented simulated phishing exercises. Through sustained training and awareness efforts, we reduced social engineering susceptibility from nearly 45% to under 5%.

On a personal level, Darktrace allows me to sleep better at night and take time off knowing we have intelligent systems monitoring and responding around the clock. For any CIO or CISO carrying institutional risk on their shoulders, that matters.

The next era: AI vs. AI

A new chapter in cybersecurity is unfolding as adversaries leverage AI to enhance scale, speed, and believability. Phishing campaigns are more personalized, impersonation attempts are more precise, and deepfake video technology, including live video, is disturbingly authentic. At the same time, organizations are rapidly adopting AI across their own environments —from GenAI assistants to embedded tools to autonomous agents. These systems don’t operate within fixed rules. They act across email, cloud, SaaS, and identity systems, often with broad permissions, and their behavior can evolve over time in ways that are difficult to predict or control.

That creates a new kind of security challenge. It’s not just about defending against AI-powered threats but understanding and governing how AI behaves within your environment, including what it can access, how it acts, and where risk begins to emerge.

From my perspective, this is a natural next step for Darktrace.

Darktrace brings a level of maturity and behavioral understanding uniquely suited to the complexity of AI environments. Self-Learning AI learns the normal patterns of each business to interpret context, uncover subtle intent, and detect meaningful deviations without relying on predefined rules or signatures. Extending into securing AI by bringing real-time visibility and control to GenAI assistants, AI agents, development environments and Shadow AI, feels like the logical evolution of what Darktrace already does so well.

Just as importantly, Darktrace is already built for dynamic, cross-domain environments where risk doesn’t sit in a single tool or control plane. In higher education, activity already spans multiple systems and, with AI, that interconnection only accelerates.

Having deployed Darktrace multiple times, I have confidence it’s uniquely positioned to lead in this space and help organizations adopt AI with greater visibility and control.

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Since authoring this blog, Irving Bruckstein has transitioned to the role of Chief Executive Officer of the Cyberaigroup.

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About the author
Irving Bruckstein
CEO CyberAIgroup

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May 11, 2026

The Next Step After Mythos: Defending in a World Where Compromise is Expected

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Is Anthropic’s Mythos a turning point for cybersecurity?

Anthropic’s recent announcements around their Mythos model, alongside the launch of Project Glasswing, have generated significant interest across the cybersecurity industry.

The closed-source nature of the Mythos model has understandably attracted a degree of skepticism around some of the claims being made. Additionally, Project Glasswing was initially positioned as a way for software vendors to accelerate the proactive discovery of vulnerabilities in their own code; however, much of the attention has focused on the potential for AI to identify exploitable vulnerabilities for those with malicious intent.

Putting questions around the veracity of those claims to one side – which, for what it’s worth, do appear to be at least partially endorsed by independent bodies such as the UK’s AI Security Institute – this should not be viewed as a critical turning point for the industry. Rather, it reflects the natural direction of travel.

How Mythos affects cybersecurity teams  

At Darktrace, extolling the virtues of AI within cybersecurity is understandably close to our hearts. However, taking a step back from the hype, we’d like to consider what developments like this mean for security teams.

Whether it’s Mythos or another model yet to be released, it’s worth remembering that there is no fundamental difference between an AI discovered vulnerability and one discovered by a human. The change is in the pace of discovery and, some may argue, the lower the barrier to entry.

In the hands of a software developer, this is unquestionably positive. Faster discovery enables earlier remediation and more proactive security. But in the hands of an attacker, the same capability will likely lead to a greater number of exploitable vulnerabilities being used in the wild and, critically, vulnerabilities that are not yet known to either the vendor or the end user.

That said, attackers have always been able to find exploitable vulnerabilities and use them undetected for extended periods of time. The use of AI does not fundamentally change this reality, but it does make the process faster and, unfortunately, more likely to occur at scale.

While tools such as Darktrace / Attack Surface Management and / Proactive Exposure Management  can help security teams prioritize where to patch, the emergence of AI-driven vulnerability discovery reinforces an important point: patching alone is not a sufficient control against modern cyber-attacks.

Rethinking defense for a world where compromise is expected

Rather than assuming vulnerabilities can simply be patched away, defenders are better served by working from the assumption that their software is already vulnerable - and always will be -and build their security strategy accordingly.

Under that assumption, defenders should expect initial access, particularly across internet exposed assets, to become easier for attackers. What matters then is how quickly that foothold is detected, contained, and prevented from expanding.

For defenders, this places renewed emphasis on a few core capabilities:

  • Secure-by-design architectures and blast radius reduction, particularly around identity, MFA, segmentation, and Zero Trust principles
  • Early, scalable detection and containment, favoring behavioral and context-driven signals over signatures alone
  • Operational resilience, with the expectation of more frequent early-stage incidents that must be managed without burning out teams

How Darktrace helps organizations proactively defend against cyber threats

At Darktrace, we support security teams across all three of these critical capabilities through a multi-layered AI approach. Our Self-Learning AI learns what’s normal for your organization, enabling real-time threat detection, behavioral prediction, incident investigation and autonomous response. - all while empowering your security team with visibility and control.

To learn more about Darktrace’s application of AI to cybersecurity download our White Paper here.  

Reducing blast radius through visibility and control

Secure-by-design principles depend on understanding how users, devices, and systems behave. By learning the normal patterns of identity and network activity, Darktrace helps teams identify when access is being misused or when activity begins to move beyond expected boundaries. This makes it possible to detect and contain lateral movement early, limiting how far an attacker can progress even after initial access.

Detecting and containing threats at the earliest stage  

As AI accelerates vulnerability discovery, defenders need to identify exploitation before it is formally recognized. Darktrace’s behavioral understanding approach enables detection of subtle deviations from normal activity, including those linked to previously unknown vulnerabilities.

A key example of this is our research on identifying cyber threats before public CVE disclosures, demonstrating that assessing activity against what is normal for a specific environment, rather than relying on predefined indicators of compromise, enables detection of intrusions exploiting previously unknown vulnerabilities days or even weeks before details become publicly available.

Additionally, our Autonomous Response capability provides fast, targeted containment focused on the most concerning events, while allowing normal business operations to continue. This has consistently shown that even when attackers use techniques never seen before, Darktrace’s Autonomous Response can contain threats before they have a chance to escalate.

Scaling response without increasing operational burden

As early-stage incidents become more frequent, the ability to investigate and respond efficiently becomes critical. Darktrace’s Cyber AI Analyst’s AI-driven investigation capabilities automatically correlate activity across the environment, prioritizing the most significant threats and reducing the need for manual triage. This allows security teams to respond faster and more consistently, without increasing workload or burnout.

What effective defense looks like in an AI-accelerated landscape

Developments like Mythos highlight a reality that has been building for some time: the window between exposure and exploitation is shrinking, and in many cases, it may disappear entirely. In that environment, relying on patching alone becomes increasingly reactive, leaving little room to respond once access has been established.

The more durable approach is to assume that compromise will occur and focus on controlling what happens next. That means identifying early signs of misuse, containing threats before they spread, and maintaining visibility across the environment so that isolated signals can be understood in context.

AI plays a role on both sides of this equation. While it enables attackers to move faster, it also gives defenders the ability to detect subtle changes in behavior, prioritize what matters, and respond in real time. The advantage will not come from adopting AI in isolation, but from applying it in a way that reduces the gap between detection and action.

AI may be accelerating parts of the attack lifecycle, but the fundamentals of defense, detection, and containment still apply. If anything, they matter more than ever – and AI is just as powerful a tool for defenders as it is for attackers.

To learn more about Darktrace and Mythos read more on our blog: Mythos vs Ethos: Defending in an Era of AI‑Accelerated Vulnerability Discovery

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About the author
Toby Lewis
Head of Threat Analysis
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