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January 9, 2025

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

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09
Jan 2025
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.

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.
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Justin Torres
Cyber Analyst
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January 29, 2025

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Inside the SOC

Bytesize Security: Insider Threats in Google Workspace

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What is an insider threat?

An insider threat is a cyber risk originating from within an organization. These threats can involve actions such as an employee inadvertently clicking on a malicious link (e.g., a phishing email) or an employee with malicious intent conducting data exfiltration for corporate sabotage.

Insiders often exploit their knowledge and access to legitimate corporate tools, presenting a continuous risk to organizations. Defenders must protect their digital estate against threats from both within and outside the organization.

For example, in the summer of 2024, Darktrace / IDENTITY successfully detected a user in a customer environment attempting to steal sensitive data from a trusted Google Workspace service. Despite the use of a legitimate and compliant corporate tool, Darktrace identified anomalies in the user’s behavior that indicated malicious intent.

Attack overview: Insider threat

In June 2024, Darktrace detected unusual activity involving the Software-as-a-Service (SaaS) account of a former employee from a customer organization. This individual, who had recently left the company, was observed downloading a significant amount of data in the form of a “.INDD” file (an Adobe InDesign document typically used to create page layouts [1]) from Google Drive.

While the use of Google Drive and other Google Workspace platforms was not unexpected for this employee, Darktrace identified that the user had logged in from an unfamiliar and suspicious IPv6 address before initiating the download. This anomaly triggered a model alert in Darktrace / IDENTITY, flagging the activity as potentially malicious.

A Model Alert in Darktrace / IDENTITY showing the unusual “.INDD” file being downloaded from Google Workspace.
Figure 1: A Model Alert in Darktrace / IDENTITY showing the unusual “.INDD” file being downloaded from Google Workspace.

Following this detection, the customer reached out to Darktrace’s Security Operations Center (SOC) team via the Security Operations Support service for assistance in triaging and investigating the incident further. Darktrace’s SOC team conducted an in-depth investigation, enabling the customer to identify the exact moment of the file download, as well as the contents of the stolen documents. The customer later confirmed that the downloaded files contained sensitive corporate data, including customer details and payment information, likely intended for reuse or sharing with a new employer.

In this particular instance, Darktrace’s Autonomous Response capability was not active, allowing the malicious insider to successfully exfiltrate the files. If Autonomous Response had been enabled, Darktrace would have immediately acted upon detecting the login from an unusual (in this case 100% rare) location by logging out and disabling the SaaS user. This would have provided the customer with the necessary time to review the activity and verify whether the user was authorized to access their SaaS environments.

Conclusion

Insider threats pose a significant challenge for traditional security tools as they involve internal users who are expected to access SaaS platforms. These insiders have preexisting knowledge of the environment, sensitive data, and how to make their activities appear normal, as seen in this case with the use of Google Workspace. This familiarity allows them to avoid having to use more easily detectable intrusion methods like phishing campaigns.

Darktrace’s anomaly detection capabilities, which focus on identifying unusual activity rather than relying on specific rules and signatures, enable it to effectively detect deviations from a user’s expected behavior. For instance, an unusual login from a new location, as in this example, can be flagged even if the subsequent malicious activity appears innocuous due to the use of a trusted application like Google Drive.

Credit to Vivek Rajan (Cyber Analyst) and Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Detections

SaaS / Resource::Unusual Download Of Externally Shared Google Workspace File

References

[1]https://www.adobe.com/creativecloud/file-types/image/vector/indd-file.html

MITRE ATT&CK Mapping

Technqiue – Tactic – ID

Data from Cloud Storage Object – COLLECTION -T1530

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Vivek Rajan
Cyber Analyst

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January 28, 2025

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Reimaginar su SOC: cómo lograr una seguridad de red proactiva

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Introduction: Challenges and solutions to SOC efficiency

For Security Operation Centers (SOCs), reliance on signature or rule-based tools – solutions that are always chasing the latest update to prevent only what is already known – creates an excess of false positives. SOC analysts are therefore overwhelmed by a high volume of context-lacking alerts, with human analysts able to address only about 10% due to time and resource constraints. This forces many teams to accept the risks of addressing only a fraction of the alerts while novel threats go completely missed.

74% of practitioners are already grappling with the impact of an AI-powered threat landscape, which amplifies challenges like tool sprawl, alert fatigue, and burnout. Thus, achieving a resilient network, where SOC teams can spend most of their time getting proactive and stopping threats before they occur, feels like an unrealistic goal as attacks are growing more frequent.

Despite advancements in security technology (advanced detection systems with AI, XDR tools, SIEM aggregators, etc...), practitioners are still facing the same issues of inefficiency in their SOC, stopping them from becoming proactive. How can they select security solutions that help them achieve a proactive state without dedicating more human hours and resources to managing and triaging alerts, tuning rules, investigating false positives, and creating reports?

To overcome these obstacles, organizations must leverage security technology that is able to augment and support their teams. This can happen in the following ways:

  1. Full visibility across the modern network expanding into hybrid environments
  2. Have tools that identifies and stops novel threats autonomously, without causing downtime
  3. Apply AI-led analysis to reduce time spent on manual triage and investigation

Your current solutions might be holding you back

Traditional cybersecurity point solutions are reliant on using global threat intelligence to pattern match, determine signatures, and consequently are chasing the latest update to prevent only what is known. This means that unknown threats will evade detection until a patient zero is identified. This legacy approach to threat detection means that at least one organization needs to be ‘patient zero’, or the first victim of a novel attack before it is formally identified.

Even the point solutions that claim to use AI to enhance threat detection rely on a combination of supervised machine learning, deep learning, and transformers to

train and inform their systems. This entails shipping your company’s data out to a large data lake housed somewhere in the cloud where it gets blended with attack data from thousands of other organizations. The resulting homogenized dataset gets used to train AI systems — yours and everyone else’s — to recognize patterns of attack based on previously encountered threats.

While using AI in this way reduces the workload of security teams who would traditionally input this data by hand, it emanates the same risk – namely, that AI systems trained on known threats cannot deal with the threats of tomorrow. Ultimately, it is the unknown threats that bring down an organization.

The promise and pitfalls of XDR in today's threat landscape

Enter Extended Detection and Response (XDR): a platform approach aimed at unifying threat detection across the digital environment. XDR was developed to address the limitations of traditional, fragmented tools by stitching together data across domains, providing SOC teams with a more cohesive, enterprise-wide view of threats. This unified approach allows for improved detection of suspicious activities that might otherwise be missed in siloed systems.

However, XDR solutions still face key challenges: they often depend heavily on human validation, which can aggravate the already alarmingly high alert fatigue security analysts experience, and they remain largely reactive, focusing on detecting and responding to threats rather than helping prevent them. Additionally, XDR frequently lacks full domain coverage, relying on EDR as a foundation and are insufficient in providing native NDR capabilities and visibility, leaving critical gaps that attackers can exploit. This is reflected in the current security market, with 57% of organizations reporting that they plan to integrate network security products into their current XDR toolset[1].

Why settling is risky and how to unlock SOC efficiency

The result of these shortcomings within the security solutions market is an acceptance of inevitable risk. From false positives driving the barrage of alerts, to the siloed tooling that requires manual integration, and the lack of multi-domain visibility requiring human intervention for business context, security teams have accepted that not all alerts can be triaged or investigated.

While prioritization and processes have improved, the SOC is operating under a model that is overrun with alerts that lack context, meaning that not all of them can be investigated because there is simply too much for humans to parse through. Thus, teams accept the risk of leaving many alerts uninvestigated, rather than finding a solution to eliminate that risk altogether.

Darktrace / NETWORK is designed for your Security Operations Center to eliminate alert triage with AI-led investigations , and rapidly detect and respond to known and unknown threats. This includes the ability to scale into other environments in your infrastructure including cloud, OT, and more.

Beyond global threat intelligence: Self-Learning AI enables novel threat detection & response

Darktrace does not rely on known malware signatures, external threat intelligence, historical attack data, nor does it rely on threat trained machine learning to identify threats.

Darktrace’s unique Self-learning AI deeply understands your business environment by analyzing trillions of real-time events that understands your normal ‘pattern of life’, unique to your business. By connecting isolated incidents across your business, including third party alerts and telemetry, Darktrace / NETWORK uses anomaly chains to identify deviations from normal activity.

The benefit to this is that when we are not predefining what we are looking for, we can spot new threats, allowing end users to identify both known threats and subtle, never-before-seen indicators of malicious activity that traditional solutions may miss if they are only looking at historical attack data.

AI-led investigations empower your SOC to prioritize what matters

Anomaly detection is often criticized for yielding high false positives, as it flags deviations from expected patterns that may not necessarily indicate a real threat or issues. However, Darktrace applies an investigation engine to automate alert triage and address alert fatigue.

Darktrace’s Cyber AI Analyst revolutionizes security operations by conducting continuous, full investigations across Darktrace and third-party alerts, transforming the alert triage process. Instead of addressing only a fraction of the thousands of daily alerts, Cyber AI Analyst automatically investigates every relevant alert, freeing up your team to focus on high-priority incidents and close security gaps.

Powered by advanced machine-learning techniques, including unsupervised learning, models trained by expert analysts, and tailored security language models, Cyber AI Analyst emulates human investigation skills, testing hypotheses, analyzing data, and drawing conclusions. According to Darktrace Internal Research, Cyber AI Analyst typically provides a SOC with up to  50,000 additional hours of Level 2 analysis and written reporting annually, enriching security operations by producing high level incident alerts with full details so that human analysts can focus on Level 3 tasks.

Containing threats with Autonomous Response

Simply quarantining a device is rarely the best course of action - organizations need to be able to maintain normal operations in the face of threats and choose the right course of action. Different organizations also require tailored response functions because they have different standards and protocols across a variety of unique devices. Ultimately, a ‘one size fits all’ approach to automated response actions puts organizations at risk of disrupting business operations.

Darktrace’s Autonomous Response tailors its actions to contain abnormal behavior across users and digital assets by understanding what is normal and stopping only what is not. Unlike blanket quarantines, it delivers a bespoke approach, blocking malicious activities that deviate from regular patterns while ensuring legitimate business operations remain uninterrupted.

Darktrace offers fully customizable response actions, seamlessly integrating with your workflows through hundreds of native integrations and an open API. It eliminates the need for costly development, natively disarming threats in seconds while extending capabilities with third-party tools like firewalls, EDR, SOAR, and ITSM solutions.

Unlocking a proactive state of security

Securing the network isn’t just about responding to incidents — it’s about being proactive, adaptive, and prepared for the unexpected. The NIST Cybersecurity Framework (CSF 2.0) emphasizes this by highlighting the need for focused risk management, continuous incident response (IR) refinement, and seamless integration of these processes with your detection and response capabilities.

Despite advancements in security technology, achieving a proactive posture is still a challenge to overcome because SOC teams face inefficiencies from reliance on pattern-matching tools, which generate excessive false positives and leave many alerts unaddressed, while novel threats go undetected. If SOC teams are spending all their time investigating alerts then there is no time spent getting ahead of attacks.

Achieving proactive network resilience — a state where organizations can confidently address challenges at every stage of their security posture — requires strategically aligned solutions that work seamlessly together across the attack lifecycle.

References

1.       Market Guide for Extended Detection and Response, Gartner, 17thAugust 2023 - ID G00761828

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