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January 9, 2025

Detecting and Mitigating Adversary-in-the-Middle Phishing Attacks with Darktrace Services

Threat actors often use advanced phishing toolkits and Adversary-in-the-Middle (AitM) attacks in Business Email Compromise (BEC) campaigns, Discover how Darktrace detected and mitigated a sophisticated attack leveraging Dropbox, highlighting the importance of robust cybersecurity measures.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Justin Torres
Cyber Analyst
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09
Jan 2025

What is an Adversary-in-the-Middle Attack?

Threat actors are increasingly utilizing advanced phishing toolkits and techniques to carry out Adversary-in-the-Middle (AitM) attacks. These attacks involve the use of a proxy to a legitimate service, where the attacker’s webpage mimics the expected site. While the victim believes they are visiting the legitimate site, they are actually interacting with the attacker’s device, allowing the malicious actor to monitor all interactions and control the authenticated session, ultimately gaining access to the user’s account [1][2].

This blog will explore how Darktrace detected AitM techniques being leveraged in a Business Email Compromise (BEC) attack that used the widely used and trusted cloud storage service, Dropbox, for delivery. Dropbox’s popularity has made it a prime target for attackers to exploit in recent years. Threat actors can exploit the service for various malicious activities, including distributing malware and exposing sensitive information.

Attack Overview

In these types of AitM BEC attacks, recipients are often targeted with Dropbox-related emails, featuring subject headings like ‘FirstLast shared "Filename" with you,’ which suggest an individual is sharing an invoice-related attachment. These email subjects are common in such attacks, as threat actors attempt to encourage victims to access Dropbox links by masquerading them as legitimate files.

While higher priority users are, of course, targeted, the scope of these attacks remains broad. For instance, if a lower priority user is targeted by a phishing attack or their token is stolen, an attacker can still attempt BEC for further malicious intent and financial gain.

In October 2024, a Darktrace customer received a phishing email from a seemingly legitimate Dropbox address. This email originated from the IP, 54.240.39[.]219 and contained multiple link payloads to Dropbox-related hostnames were observed, inviting the user to access a file. Based on anomaly indicators and detection by Darktrace / EMAIL, Darktrace recognized that one of the payloads was attempting to abuse a legitimate cloud platform to share files or other unwanted material with the recipient.

Overview of the malicious email in the Darktrace / EMAIL console, highlighting Dropbox associated content/link payloads.
Figure 1: Overview of the malicious email in the Darktrace / EMAIL console, highlighting Dropbox associated content/link payloads.

Following the recipient’s engagement with this email, Darktrace / IDENTITY identified a series of suspicious activities within the customer’s environment.

AitM attacks allow threat actors to bypass multi-factor authentication (MFA). Initially, when a user is phished, the malicious infrastructure captures both the user’s credentials and the token. This includes replaying a token issued to user that has already completed the MFA requirement, allowing the threat actor to satisfy the validity of the requirement and gain access to sensitive organizational resources. Darktrace is able to analyze user activity and authentication patterns to determine whether MFA requirements were met. This capability helps verify and indicate token theft via AitM.

Darktrace observed the associated user account making requests over Microsoft 365 from the IP 41.90.175[.]46. Given the unusual nature and rare geolocation based in Kenya, Africa, this activity did not appear indicative of legitimate business operations.

Geographical location of the SaaS user
Figure 2: Geographical location of the SaaS user in relation to the source IP 41.90.175[.]46.

Further analysis using open-source intelligence (OSINT) revealed that the endpoint was likely associated with a call-back proxy network [3]. This suggested the presence of a network device capable of re-routing traffic and harvesting information.

Darktrace also detected that the same SaaS user was logging in from two different locations around the same time. One login was from a common, expected location, while the other was from an unusual location. Additionally, the user was observed registering security information using the Microsoft Authenticator app, indicating an attempt by an attacker to maintain access to the account by establishing a new method of MFA. This new MFA method could be used to bypass future MFA requirements, allowing the attacker to access sensitive material or carry out further malicious activities.

External sites summary for the SaaS account in relation to the source IP 13.74.161[.]104, observed with Registering Security Information.
Figure 3: External sites summary for the SaaS account in relation to the source IP 13.74.161[.]104, observed with Registering Security Information.

Ultimately, this anomalous behavior was escalated to the Darktrace Security Operations Centre (SOC) via the Managed Detection & Response service for prompt triage and investigation by Darktrace’s SOC Analysts who notified the customer of strong evidence of compromise.

Fortunately, since this customer had Darktrace enabled in Autonomous Response mode, the compromised SaaS account had already been disabled, containing the attack. Darktrace’s SOC elected to extend this action to ensure the malicious activity remained halted until the customer could take further remedial action.

Attack timeline of observed activity, in chronological order; This highlighted anomalous SaaS events such as, MailItemsAccessed’, ‘Use of Unusual Credentials’, ‘User Registered Security Info’ events, and a ‘Disable User’ Autonomous Response action.
Figure 4: Attack timeline of observed activity, in chronological order; This highlighted anomalous SaaS events such as, MailItemsAccessed’, ‘Use of Unusual Credentials’, ‘User Registered Security Info’ events, and a ‘Disable User’ Autonomous Response action.

Conclusion

AitM attacks can play a crucial role in BEC campaigns. These attacks are often part of multi-staged operations, where an initial AitM attack is leveraged to launch a BEC by delivering a malicious URL through a trusted vendor or service. Attackers often attempt to lay low on their target network, sometimes persisting for extended periods, as they monitor user accounts or network segments to intercept sensitive communications.

In this instance, Darktrace successfully identified and acted against AitM techniques being leveraged in a BEC attack that used Dropbox for delivery. While Dropbox is widely used for legitimate purposes, its popularity has also made it a target for exploitation by threat actors, who have used it for a variety of malicious purposes, including delivering malware and revealing sensitive information.

Darktrace’s Security Operations Support service, combined with its Autonomous Response technology, provided timely and effective mitigation. Dedicated Security Operations Support analysts triaged the incident and implemented preventative measures, ensuring the customer was promptly notified. Meanwhile, Darktrace swiftly disabled the compromised SaaS account, allowing the customer to take further necessary actions, such as resetting the user’s password.

This case highlights the capabilities of Darktrace’s solutions, enabling the customer to resume normal business operations despite the malicious activity.

Credit to Justin Torres (Senior Cyber Analyst), Stefan Rowe (Technical Director, SOC) and Ryan Traill (Analyst Content Lead)

Appendices

References

1.    https://www.proofpoint.com/us/threat-reference/man-in-the-middle-attack-mitm

2.    https://thehackernews.com/2024/08/how-to-stop-aitm-phishing-attack.html

3.    https://spur.us/context/41.90.175.46

Darktrace Model Detections

Darktrace / NETWORK Model Alert(s):

SaaS / Compromise::SaaS Anomaly Following Anomalous Login

SaaS / Unusual Activity::Multiple Unusual SaaS Activities

SaaS / Compromise::Unusual Login and Account Update

SaaS / Compromise::Login From Rare Endpoint While User Is Active

SaaS / Access::Unusual External Source for SaaS Credential Use

SaaS / Email Nexus::Unusual Login Location Following Link to File Storage

SaaS / Access::MailItemsAccessed from Rare Endpoint

Darktrace/Autonomous Response Model Alert(s):

Antigena / SaaS::Antigena Suspicious SaaS Activity Block

List of Indicators of Compromise (IoCs)

(IoC - Type - Description)

41.90.175[.]46 – Source IP Observed with Suspicious Login Behavior

MITRE ATT&CK Mapping

(Technique Name - Tactic - ID - Sub-Technique of)

Cloud Accounts - DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS - T1078.004 - T1078

Email Accounts - RESOURCE DEVELOPMENT - T1586.002 - T1586

Cloud Service Dashboard - DISCOVERY - T1538

Compromise Accounts - RESOURCE DEVELOPMENT - T1586

Steal Web Session Cookie - CREDENTIAL ACCESS - T1539

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
Justin Torres
Cyber Analyst

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May 21, 2026

Darktrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) For the Second Consecutive Year

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Continued recognition in NDR  

Darktrace has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), marking the second consecutive year in the Leaders quadrant.

We believe this consistency reflects sustained ability to execute, adapt, and deliver outcomes as the market evolves.

While we are immensely proud to be recognized by industry analysts as a Leader in NDR, that's just part of the story. Darktrace was also Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response based on direct customer feedback and real-world experience.

We believe the combination of these two signals is important. One reflects how the market is evaluated. The other reflects how technology performs in practice.

Why Darktrace continues to be recognized as a leader

We believe our position as a Leader for the second consecutive year reflects a combination of our sustained ability to execute in NDR, continued AI innovation, and proven delivery of security outcomes for customers and partners worldwide.

We also feel that our leadership in the NDR market is a testament to our unique and multi-layered AI approach, for which we were recognized as No.7 on Fast Company’s Most Innovative AI Companies of 2026 list, plus one of the hottest AI cybersecurity companies in CRN's AI 100.

Adapting to complex, real-world environments

Organizations are no longer protecting a single network perimeter. They are securing a mix of users, devices, applications, and data that move across hybrid environments.

Darktrace has focused on maintaining visibility and detection across these conditions, allowing security teams to understand activity as it scales.

Supporting organizations globally, not just technically

Security outcomes are shaped as much by deployment and support as they are by detection capability.

Darktrace continues to invest in regional presence across 29 countries around the world, helping organizations operationalize NDR in ways that align with local requirements, internal processes, and team structures.

Continuing to push AI beyond detection

AI in cybersecurity is often positioned as a way to improve detection accuracy. But the more important shift is how AI can influence decision-making and response.

Darktrace continues to develop models that learn from both live environments and historical incident data, combining real-time behavioral analysis with insights derived from prior attack patterns.

Using technologies such as the Incident Graph and DIGEST (Darktrace Incident Graph Evaluation for Security Threats), activity is not analyzed in isolation. Instead, relationships between users, devices, connections, and events are mapped over time, allowing the system to reconstruct how an incident is unfolding and how similar incidents have progressed in the past.

By evaluating these patterns, Darktrace can assess the likelihood that an incident will escalate, prioritizing the activity that poses the greatest risk and surfacing the most relevant context for investigation.

This shifts security operations from simply identifying anomalies to understanding their trajectory, helping teams anticipate potential impact and respond earlier with greater precision.

Why NDR is shifting from reactive detection to proactive, AI-driven security

Traditional approaches to NDR have been built around reactively identifying threats once they become clearly visible. That model is increasingly difficult to rely on.

Attackers are no longer operating in ways that stand out. They use valid credentials, trusted tools, and low-and-slow techniques that blend into everyday activity. By the time something looks obviously malicious, the impact is often already underway.

This is the core limitation of reactive detection. It depends on recognizing something that already looks like a threat.

As a result, many of the most consequential incidents today fall into a gap.

Insider activity, compromised credentials, and novel attacks rarely trigger traditional alerts because they do not follow known patterns. On the surface, they often appear legitimate, making them difficult to distinguish from normal behavior without deeper context.

This is why we believe this Gartner recognition reflects a broader shift in NDR toward autonomous, proactive and pre‑emptive security operations.

By understanding normal behavior within an environment, it is possible to identify subtle deviations rather than waiting for confirmation of threats as they are taking place.

Darktrace’s Self-Learning AI is designed for behavioral understanding. By continuously learning each organization’s normal patterns, it can detect deviations in real time, enabling a proactive and pre-emptive model of NDR where security teams can respond to early signs of risk as they emerge, reducing the window in which attacks can develop.

In multiple cases, this behavioral approach has led to early threat detection where Darktrace identified completely unknown threats, including pre-CVE zero-day activity. By detecting subtle behavioral changes before vulnerabilities were publicly disclosed or widely understood, organizations can mitigate threats before they do damage.

This shift is subtle but important. Modern NDR solutions must shift from a system that explains what happened to one that helps prevent threats from developing in the first place, and Darktrace is proud to be at the forefront of this shift - helping organizations build and maintain a state of proactive network resilience.

Continuing to innovate at the forefront of NDR

In our view, recognition as a Leader reflects where the market is today. Continuing to innovate defines what comes next.

As businesses evolve, new technologies like AI tools and agents introduce new security risks and challenges; security teams need more than simple detection. They need a complete understanding of risk as it develops, the ability to investigate it in context, and to contain threats at machine speed.  

Darktrace / NETWORK is built to deliver across that full spectrum. Its Self-Learning AI continuously adapts to each organization’s environment, identifying subtle behavioral changes that signal emerging threats. Integrated investigation and autonomous response reduce the time between detection and action, allowing teams to move with greater speed and confidence.

This combination enables organizations to detect and contain known, unknown, and insider threats as they develop, while also strengthening resilience over time.

As a two-time Leader in the Gartner® Magic Quadrant™ for NDR and the only 2025 Gartner® Peer Insights™ Customers’ Choice, we feel Darktrace continues to evolve its platform to meet the demands of modern environments, delivering a more complete and adaptive approach to network security.

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Disclaimer: The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) ,The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

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Mikey Anderson
Product Marketing Manager, Network Detection & Response

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May 21, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

Sign up today to stay informed about innovations across securing AI.

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Jamie Bali
Technical Author (AI) Developer
Your data. Our AI.
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