Blog
/
Network
/
December 14, 2021

Log4Shell Vulnerability Detection & Response With Darktrace

Learn how Darktrace's AI detects and responds to Log4Shell attacks. Explore real-world examples and see how Darktrace identified and mitigated cyber threats.
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
Max Heinemeyer
Global Field CISO
Written by
Justin Fier
SVP, Red Team Operations
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
14
Dec 2021

In this blog, we’ll take a look at the Log4Shell vulnerability and provide real-world examples of how Darktrace detects and responds to attacks attempting to leverage Log4Shell in the wild.

Log4Shell is now the well-known name for CVE-2021-44228 – a severity 10 zero-day exploiting a well-known Java logging utility known as Log4j. Vulnerabilities are discovered daily, and some are more severe than others, but the fact that this open source utility is nested into nearly everything, including the Mars Ingenuity drone, makes this that much more menacing. Details and further updates about Log4Shell are still emerging at the publication date of this blog.

Typically, zero-days with the power to reach this many systems are held close to the chest and only used by nation states for high value targets or operations. This one, however, was first discovered being used against Minecraft gaming servers, shared in chat amongst gamers.

While all steps should be taken to deploy mitigations to the Log4Shell vulnerability, these can take time. As evidenced here, behavioral detection can be used to look for signs of post-exploitation activity such as scanning, coin mining, lateral movement, and other activities.

Darktrace initially detected the Log4Shell vulnerability targeting one of our customers’ Internet-facing servers, as you will see in detail in an actual anonymized threat investigation below. This was highlighted and reported using Cyber AI Analyst, unpacked here by our SOC team. Please take note that this was using pre-existing algorithms without retraining classifiers or adjusting response mechanisms in reaction to Log4Shell cyber-attacks.

How Log4Shell works

The vulnerability works by taking advantage of improper input validation by the Java Naming and Directory Interface (JNDI). A command comes in from an HTTP user-agent, encrypted HTTPS connection, or even a chat room message, and the JNDI sends that to the target system in which it gets executed. Most libraries and applications have checks and protections in place to prevent this from happening, but as seen here, they get missed at times.

Various threat actors have started to leverage the vulnerability in attacks, ranging from indiscriminate crypto-mining campaigns to targeted, more sophisticated attacks.

Real-world example 1: Log4Shell exploited on CVE ID release date

Darktrace saw this first example on December 10, the same day the CVE ID was released. We often see publicly documented vulnerabilities being weaponized within days by threat actors. This attack hit an Internet-facing device in an organization’s demilitarized zone (DMZ). Darktrace had automatically classified the server as an Internet-facing device based on its behavior.

The organization had deployed Darktrace in the on-prem network as one of many coverage areas that include cloud, email and SaaS. In this deployment, Darktrace had good visibility of the DMZ traffic. Antigena was not active in this environment, and Darktrace was in detection-mode only. Despite this fact, the client in question was able to identify and remediate this incident within hours of the initial alert. The attack was automated and had the goal of deploying a crypto-miner known as Kinsing.

In this attack, the attacker made it harder to detect the compromise by encrypting the initial command injection using HTTPS over the more common HTTP seen in the wild. Despite this method being able to bypass traditional rules and signature-based systems Darktrace was able to spot multiple unusual behaviors seconds after the initial connection.

Initial compromise details

Through peer analysis Darktrace had previously learned what this specific DMZ device and its peer group normally do in the environment. During the initial exploitation, Darktrace detected various subtle anomalies that taken together made the attack obvious.

  1. 15:45:32 Inbound HTTPS connection to DMZ server from rare Russian IP — 45.155.205[.]233;
  2. 15:45:38 DMZ server makes new outbound connection to the same rare Russian IP using two new user agents: Java user agent and curl over a port that is unusual to serve HTTP compared to previous behavior;
  3. 15:45:39 DMZ server uses an HTTP connection with another new curl user agent (‘curl/7.47.0’) to the same Russian IP. The URI contains reconnaissance information from the DMZ server.

All this activity was detected not because Darktrace had seen it before, but because it strongly deviated from the regular ‘pattern of life’ for this and similar servers in this specific organization.

This server never reached out to rare IP addresses on the Internet, using user agents it never used before, over protocol and port combinations it never uses. Every point-in-time anomaly itself may have presented slightly unusual behavior – but taken together and analyzed in the context of this particular device and environment, the detections clearly tell a bigger story of an ongoing cyber-attack.

Darktrace detected this activity with various models, for example:

  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous Connection / Callback on Web Facing Device

Further tooling and crypto-miner download

Less than 90 minutes after the initial compromise, the infected server started downloading malicious scripts and executables from a rare Ukrainian IP 80.71.158[.]12.

The following payloads were subsequently downloaded from the Ukrainian IP in order:

  • hXXp://80.71.158[.]12//lh.sh
  • hXXp://80.71.158[.]12/Expl[REDACTED].class
  • hXXp://80.71.158[.]12/kinsing
  • hXXp://80.71.158[.]12//libsystem.so
  • hXXp://80.71.158[.]12/Expl[REDACTED].class

Using no threat intelligence or detections based on static indicators of compromise (IoC) such as IPs, domain names or file hashes, Darktrace detected this next step in the attack in real time.

The DMZ server in question never communicated with this Ukrainian IP address in the past over these uncommon ports. It is also highly unusual for this device and its peers to download scripts or executable files from this type of external destination, in this fashion. Shortly after these downloads, the DMZ server started to conduct crypto-mining.

Darktrace detected this activity with various models, for example:

  • Anomalous File / Script from Rare External Location
  • Anomalous File / Internet Facing System File Download
  • Device / Internet Facing System with High Priority Alert

Surfacing the Log4Shell incident immediately

In addition to Darktrace detecting each individual step of this attack in real time, Darktrace Cyber AI Analyst also surfaced the overarching security incident, containing a cohesive narrative for the overall attack, as the most high-priority incident within a week’s worth of incidents and alerts in Darktrace. This means that this incident was the most obvious and immediate item highlighted to human security teams as it unfolded. Darktrace’s Cyber AI Analyst found each stage of this incident and asked the very questions you would expect of your human SOC analysts. From the natural language report generated by the Cyber AI Analyst, a summary of each stage of the incident followed by the vital data points human analysts need, is presented in an easy to digest format. Each tab signifies a different part of this incident outlining the actual steps taken during each investigative process.

The result of this is no sifting through low-level alerts, no need to triage point-in-time detections, no putting the detections into a bigger incident context, no need to write a report. All of this was automatically completed by the AI Analyst saving human teams valuable time.

The below incident report was automatically created and could be downloaded as a PDF in various languages.

Figure 1: Darktrace’s Cyber AI Analyst surfaces multiple stages of the attack and explains its investigation process

Real-world example 2: Responding to a different attack using Log4Shell

On December 12, another organization’s Internet-facing server was initially compromised via Log4Shell. While the details of the compromise are different – other IoCs are involved – Darktrace detected and surfaced the attack similarly to the first example.

Interestingly, this organization had Darktrace Antigena in autonomous mode on their server, meaning the AI can take autonomous actions to respond to ongoing cyber-attacks. These responses can be delivered via a variety of mechanisms, for instance, API interactions with firewalls, other security tools, or native responses issued by Darktrace.

In this attack the rare external IP 164.52.212[.]196 was used for command and control (C2) communication and malware delivery, using HTTP over port 88, which was highly unusual for this device, peer group and organization.

Antigena reacted in real time in this organization, based on the specific context of the attack, without any human in the loop. Antigena interacted with the organization’s firewall in this case to block any connections to or from the malicious IP address – in this case 164.52.212[.]196 – over port 88 for 2 hours with the option of escalating the block and duration if the attack appears to persist. This is seen in the illustration below:

Figure 2: Antigena’s response

Here comes the trick: thanks to Self-Learning AI, Darktrace knows exactly what the Internet-facing server usually does and does not do, down to each individual data point. Based on the various anomalies, Darktrace is certain that this represents a major cyber-attack.

Antigena now steps in and enforces the regular pattern of life for this server in the DMZ. This means the server can continue doing whatever it normally does – but all the highly anomalous actions are interrupted as they occur in real time, such as speaking to a rare external IP over port 88 serving HTTP to download executables.

Of course the human can change or lift the block at any given time. Antigena can also be configured to be in human confirmation mode, having the human in the loop at certain times during the day (e.g. office hours) or at all times, depending on an organization’s needs and requirements.

Conclusion

This blog illustrates further aspects of cyber-attacks leveraging the Log4Shell vulnerability. It also demonstrates how Darktrace detects and responds to zero-day attacks if Darktrace has visibility of the attacked entities.

While Log4Shell is dominating the IT and security news, similar vulnerabilities have surfaced in the past and will appear in the future. We’ve spoken about our approach to detecting and responding to similar vulnerabilities and surrounding cyber-attacks before, for instance:

As always, companies should aim for a defense-in-depth strategy combining preventative security controls with detection and response mechanisms, as well as strong patch management.

Thanks to Brianna Leddy (Darktrace’s Director of Analysis) for her insights on the above threat find.

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
Max Heinemeyer
Global Field CISO
Written by
Justin Fier
SVP, Red Team Operations

More in this series

No items found.

Blog

/

Email

/

December 18, 2025

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

Man at laptopDefault blog imageDefault blog image

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.

[related-resource]

Continue reading
About the author
Carlos Gray
Senior Product Marketing Manager, Email

Blog

/

Email

/

December 17, 2025

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

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with DarktraceDefault blog imageDefault blog image

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

Continue reading
About the author
Your data. Our AI.
Elevate your network security with Darktrace AI