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March 20, 2023

Multi-Factor Authentication: Not the Silver Bullet

Multi-Factor Authentication (MFA) is a widely used security measure, but it's not bulletproof. See how threat actors can exploit MFA to access your information.
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
Tegbir Singh
Cyber Analyst
Written by
Emma Foulger
Global Threat Research Operations Lead
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20
Mar 2023

Multi-Factor Authentication (MFA) is a long-established component of the identity and access management (IAM) framework that requires users to provide multiple verification factors to access Software as a Service (SaaS) and application environments, rather than simply relying on account credentials. MFA has been widely, although not universally, adopted as a security measure against common account takeover methods, such as brute-force attacks and exploiting passwords found in data leaks. Despite the adoption of MFA, account takeover methods are still prevalent across the threat landscape. However, the industry is seeing more and more examples of MFA compromise wherein threat actors exploit the security tool itself to gain account access.

Although having a security measure like MFA is a crucial first step in safeguarding a network, relying on a single method will always lead to gaps. MFA is a generic term for a broad range of products and services with varying degrees of efficacy; however, it is often used in the same way as Zero Trust, as a tick box or one size fits all solution. Knowing the gaps in security that are still present, even when utilizing effective MFA tools, is essential to mitigating the evolving threats of account compromise.

Figure 1: The standard flow process of MFA for any individual application. 

Bypassing MFA & Attack Details

Instances of threat actors’ bypassing MFA typically involve an element of social engineering, such as spamming authentication requests to the victim’s email or phone. This takes advantage of the victim’s fatigue of receiving numerous notifications, leading them to validate the request to silence the notifications. Microsoft research data published in September 2022 shows a clear trend of MFA fatigue attacks becoming increasingly popular last year [1]. Notably, the Uber hack occurred after attackers exploited this method [2]. This trend seems likely to continue as MFA progresses towards universal adoption and attackers continue to focus on social engineering as a means to bypass it. The following example details how Darktrace not only identifies and warns customers about unusual MFA activity for hijacked accounts, but also how its suite of products can take appropriate actions to prevent further compromise.

On January 5, 2023, a SaaS account belonging to a customer based in Australia was observed successfully logging in from a rare external endpoint, following two previous failed attempts. Darktrace identified that the login IP address was in the United States, which it recognized as unusual compared to the user’s expected login location, and the successful login followed multiple failed MFA authentication requests.

Figure 2: A screenshot of the SaaS console, showcasing the login activity of the SaaS user with the reason for the failed logins highlighted. 

No further suspicious activity was detected on the device, likely a tactic employed by the threat actor to remain undetected by security tools. However, a Darktrace model breach was triggered two days later following another usual login location, this time in Germany. Once again, a successful authentication request was observed, suggesting the attacker was able to consistently bypass the MFA security and access the account. 

Following this login, multiple unusual activities were observed including the access of multiple sensitive internal files and initiating updates to email folders, namely \Sent Items, \Deleted Items, and \WIISE. This type of activity is indicative of a victim’s mailbox being modified to enable attackers to send malicious spam to contacts in the organization, allowing them to escalate their privileges and move laterally throughout the network.

Figure 3: A screenshot of the SaaS console showing some of the suspicious files that were previewed by the user. 

Darktrace continued to report suspicious activity from this user with similar activity occurring again on January 8, when the user was observed logging in from another highly anomalous location and accessing similar files. The activity escalated on January 13 when, alongside an unusual login and further email updates, the user created a new email rule suspiciously named “.”.

Figure 4: A screenshot showcasing the details of the email rule that was created by the malicious actor. 

The rule appears to have targeted emails received from a specific internal user, marking them as read and moving them to a different folder; it was likely that the attacker intended to use these emails to help socially engineer third-parties and compromise the organization’s network further. Additional suspicious activity was observed from the user, including an update to an email containing a potentially sensitive attachment.

Figure 5: A screenshot showing details of the attachment observed.

Due to the combination of an unusual login and new email rule, Darktrace RESPOND/Network™ took swift autonomous action, forcing the user in question to log out and disabling the account, preventing further compromise. With the implementation of these actions the malicious actor was unable to engage in any further activity on the compromised account.

Figure 6: The above screenshot of the SaaS UI shows some of the actions initiated by Darktrace RESPOND/Network.

Conclusion

Having MFA in place is an important first step towards hardening an organization’s SaaS environment and safeguarding against less sophisticated methods of attack, however defense in depth is key to ensuring a network is truly secure. Any one security measure will always have weaknesses, and only with multiple layers of varying protection can gaps in security be effectively closed. 

Using Self-Learning AI™, Darktrace DETECT™ can quickly identify unexpected behavior on a device, even if it occurs with legitimate credentials and successfully passes MFA, to bring it to the attention of the security team. Darktrace RESPOND™ is then able to take immediate action, implementing precise actions to prevent more serious compromise. 

Pairing the two products together provides customers with an around-the-clock AI decision maker capable of detecting emerging threats, even those that would evade other traditional security measures, and interrupting attacks at machine speed with surgical precision.

Resources

[1] https://techcommunity.microsoft.com/t5/microsoft-entra-azure-ad-blog/defend-your-users-from-mfa-fatigue-attacks/ba-p/2365677 

[2] https://www.forbes.com/sites/daveywinder/2022/09/18/has-uber-been-hacked-company-investigates-cybersecurity-incident-as-law-enforcement-alerted/?sh=4d3495796056 

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
Tegbir Singh
Cyber Analyst
Written by
Emma Foulger
Global Threat Research Operations Lead

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December 18, 2025

Why organizations are moving to label-free, behavioral DLP for outbound email

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Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

  • Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
  • Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

  • No labeling burden; no regex/rule maintenance
  • Works when tags are missing, wrong, or stale
  • Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

  • Flags risk without predefined rules or IOCs
  • Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
  • Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

  • Low-latency, on-send enforcement
  • Non-generative for predictable, explainable outcomes
  • Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.  

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

  • Can it operate label free when tags are missing or wrong? 
  • Does it truly learn per user behavior (no shortcuts)? 
  • Is there a domain specific model behind the content understanding (not a generic LLM)? 
  • Does it explain decisions to both analysts and end users? 
  • Will it integrate with your label program and SOC workflows rather than duplicate them? 

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email

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December 17, 2025

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with Darktrace

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What is an Adversary-in-the-middle (AiTM) attack?

Adversary-in-the-Middle (AiTM) attacks are a sophisticated technique often paired with phishing campaigns to steal user credentials. Unlike traditional phishing, which multi-factor authentication (MFA) increasingly mitigates, AiTM attacks leverage reverse proxy servers to intercept authentication tokens and session cookies. This allows attackers to bypass MFA entirely and hijack active sessions, stealthily maintaining access without repeated logins.

This blog examines a real-world incident detected during a Darktrace customer trial, highlighting how Darktrace / EMAILTM and Darktrace / IDENTITYTM identified the emerging compromise in a customer’s email and software-as-a-service (SaaS) environment, tracked its progression, and could have intervened at critical moments to contain the threat had Darktrace’s Autonomous Response capability been enabled.

What does an AiTM attack look like?

Inbound phishing email

Attacks typically begin with a phishing email, often originating from the compromised account of a known contact like a vendor or business partner. These emails will often contain malicious links or attachments leading to fake login pages designed to spoof legitimate login platforms, like Microsoft 365, designed to harvest user credentials.

Proxy-based credential theft and session hijacking

When a user clicks on a malicious link, they are redirected through an attacker-controlled proxy that impersonates legitimate services.  This proxy forwards login requests to Microsoft, making the login page appear legitimate. After the user successfully completes MFA, the attacker captures credentials and session tokens, enabling full account takeover without the need for reauthentication.

Follow-on attacks

Once inside, attackers will typically establish persistence through the creation of email rules or registering OAuth applications. From there, they often act on their objectives, exfiltrating sensitive data and launching additional business email compromise (BEC) campaigns. These campaigns can include fraudulent payment requests to external contacts or internal phishing designed to compromise more accounts and enable lateral movement across the organization.

Darktrace’s detection of an AiTM attack

At the end of September 2025, Darktrace detected one such example of an AiTM attack on the network of a customer trialling Darktrace / EMAIL and Darktrace / IDENTITY.

In this instance, the first indicator of compromise observed by Darktrace was the creation of a malicious email rule on one of the customer’s Office 365 accounts, suggesting the account had likely already been compromised before Darktrace was deployed for the trial.

Darktrace / IDENTITY observed the account creating a new email rule with a randomly generated name, likely to hide its presence from the legitimate account owner. The rule marked all inbound emails as read and deleted them, while ignoring any existing mail rules on the account. This rule was likely intended to conceal any replies to malicious emails the attacker had sent from the legitimate account owner and to facilitate further phishing attempts.

Darktrace’s detection of the anomalous email rule creation.
Figure 1: Darktrace’s detection of the anomalous email rule creation.

Internal and external phishing

Following the creation of the email rule, Darktrace / EMAIL observed a surge of suspicious activity on the user’s account. The account sent emails with subject lines referencing payment information to over 9,000 different external recipients within just one hour. Darktrace also identified that these emails contained a link to an unusual Google Drive endpoint, embedded in the text “download order and invoice”.

Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Figure 2: Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.
Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.
Figure 3: Darktrace / EMAIL’s detection of the compromised account sending over 9,000 external phishing emails, containing an unusual Google Drive link.

As Darktrace / EMAIL flagged the message with the ‘Compromise Indicators’ tag (Figure 2), it would have been held automatically if the customer had enabled default Data Loss Prevention (DLP) Action Flows in their email environment, preventing any external phishing attempts.

Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.
Figure 4: Darktrace / EMAIL’s preview of the email sent by the offending account.

Darktrace analysis revealed that, after clicking the malicious link in the email, recipients would be redirected to a convincing landing page that closely mimicked the customer’s legitimate branding, including authentic imagery and logos, where prompted to download with a PDF named “invoice”.

Figure 5: Download and login prompts presented to recipients after following the malicious email link, shown here in safe view.

After clicking the “Download” button, users would be prompted to enter their company credentials on a page that was likely a credential-harvesting tool, designed to steal corporate login details and enable further compromise of SaaS and email accounts.

Darktrace’s Response

In this case, Darktrace’s Autonomous Response was not fully enabled across the customer’s email or SaaS environments, allowing the compromise to progress,  as observed by Darktrace here.

Despite this, Darktrace / EMAIL’s successful detection of the malicious Google Drive link in the internal phishing emails prompted it to suggest ‘Lock Link’, as a recommended action for the customer’s security team to manually apply. This action would have automatically placed the malicious link behind a warning or screening page blocking users from visiting it.

Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.
Figure 6: Autonomous Response suggesting locking the malicious Google Drive link sent in internal phishing emails.

Furthermore, if active in the customer’s SaaS environment, Darktrace would likely have been able to mitigate the threat even earlier, at the point of the first unusual activity: the creation of a new email rule. Mitigative actions would have included forcing the user to log out, terminating any active sessions, and disabling the account.

Conclusion

AiTM attacks represent a significant evolution in credential theft techniques, enabling attackers to bypass MFA and hijack active sessions through reverse proxy infrastructure. In the real-world case we explored, Darktrace’s AI-driven detection identified multiple stages of the attack, from anomalous email rule creation to suspicious internal email activity, demonstrating how Autonomous Response could have contained the threat before escalation.

MFA is a critical security measure, but it is no longer a silver bullet. Attackers are increasingly targeting session tokens rather than passwords, exploiting trusted SaaS environments and internal communications to remain undetected. Behavioral AI provides a vital layer of defense by spotting subtle anomalies that traditional tools often miss

Security teams must move beyond static defenses and embrace adaptive, AI-driven solutions that can detect and respond in real time. Regularly review SaaS configurations, enforce conditional access policies, and deploy technologies that understand “normal” behavior to stop attackers before they succeed.

Credit to David Ison (Cyber Analyst), Bertille Pierron (Solutions Engineer), Ryan Traill (Analyst Content Lead)

Appendices

Models

SaaS / Anomalous New Email Rule

Tactic – Technique – Sub-Technique  

Phishing - T1566

Adversary-in-the-Middle - T1557

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