ブログ
/
Cloud
/
March 27, 2025

Python-based Triton RAT Targeting Roblox Credentials

Cado Security Labs (now part of Darktrace) identified Triton RAT, a Python-based open-source tool controlled via Telegram.
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
Tara Gould
Malware Research Lead
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
27
Mar 2025

Introduction

Researchers from Cado Security Labs (now part of Darktrace) have identified a Python Remote Access Tool (RAT) named Triton RAT. The open-source RAT is available on GitHub and allows users to remotely access and control a system using Telegram. 

Technical analysis

In the version of the Triton RAT Pastebin. 

Telegram token and chat ID encoded in Base64
Figure 1: Telegram token and chat ID encoded in Base64

Features of Triton RAT:

  • Keylogging
  • Remote commands
  • Steal saved passwords
  • Steal Roblox security cookies
  • Change wallpaper
  • Screen recording
  • Webcam access
  • Gather Wifi Information
  • Download/upload file
  • Execute shell commands
  • Steal clipboard data
  • Anti-Analysis
  • Gather system information
  • Data exfiltrated to Telegram Bot

The TritonRAT code contains many functions including the function “sendmessage” which iterates over password stores in AppData, Google, Chrome, User Data, Local, and Local State, decrypts them and saves the passwords in a text file. Additionally, the RAT searches for Roblox security cookies (.ROBLOSECURITY) in Opera, Chrome, Edge, Chromium, Firefox and Brave, if found the cookies are stored in a text file and exfiltrated. A Roblox security cookie is a browser cookie that stores the users’ session and can be used to gain access to the Roblox account bypassing 2FA. 

Function to search for and exfiltrate Roblox security cookies
Figure 2: Function used to search for and exfiltrate Roblox security cookies
Function that gathers and exfiltrates system information 
Figure 3: Function that gathers and exfiltrates system information 
Secondary payload retrieved from DropBox 
Figure 4: Secondary payload retrieved from DropBox 

The Python script also contains code to create a VBScript and a BAT script which are executed with Powershell. The VBScript “updateagent.vbs” disables Windows Defender, creates backups and scheduled tasks for persistence and monitors specified processes. The BAT script “check.bat” retrieves a binary named “ProtonDrive.exe” from DropBox, stores it in a hidden folder and executes it with admin privileges. ProtonDrive is a pyinstaller compiled version of TritonRAT. Presumably the binary is retrieved to set up persistence. Once retrieved, ProtonDrive is stored in a created folder structure “C:\Users\user\AppData\Local\Programs\Proton\Drive”. Three scheduled tasks are created to start on logon of any user.

Tasks created
Figure 5: Three tasks created to start on logon of any user

For anti-analysis, Triton RAT contains a function that checks for “blacklisted” processes which include popular tools such as xdbg, ollydbg, FakeNet, and antivirus products. Additionally, the same Git user offers a file resizer as defense evasion as some anti-virus will not check a file over a certain amount of MB.  All the exfiltrated data is sent to Telegram via a Telegram bot, where the user can send commands to the affected machine. At the time of analysis, the Telegram channel/bot had 4549 messages, although it is unknown if these are indicative of the number of infections.  

[related-resource]

Conclusion

The emergence of the Python-based Triton RAT highlights how quickly cybercriminals are evolving their tactics to target platforms with large user bases like Roblox. Its persistence mechanisms and reliance on Telegram for data exfiltration make it both resilient and easy for attackers to operate at scale. As threats like this continue to surface, it’s critical for organizations and individuals to reinforce endpoint protection, and promote strong credential security practices to reduce exposure to such attacks.

Indicators of compromise (IoCs)

ProtonDrive.exe

Ea04f1c4016383e0846aba71ac0b0c9c

Related samples:

076dccb222d0869870444fea760c7f2b564481faea80604c02abf74f1963c265

0975fdadbbd60d90afdcb5cc59ad58a22bfdb2c2b00a5da6bb1e09ae702b95e7

1f4e1aa937e81e517bccc3bd8a981553a2ef134c11471195f88f3799720eaa9c

200fdb4f94f93ec042a16a409df383afeedbbc73282ef3c30a91d5f521481f24

29d2a70eeedbe496515c71640771f1f9b71c4af5f5698e2068c6adcac28cc3e0

2b05494926b4b1c79ee0a12a4e7f6c07e04c084a953a4ba980ed7cb9b8bf6bc2

2d1b6bd0b945ddd8261efbd85851656a7351fd892be0fa62cc3346883a8f917e

2dce8fc1584e660a0cba4db2cacdf5ff705b1b3ba75611de0900ebaeaa420bf9

2f27b8987638b813285595762fa3e56fff2213086e9ba4439942cd470fa5669a

3f9ce4d12e0303faa59a307bcfc4366d02ba73e423dbf5bcf1da5178253db64d

4309e6a9abdfedc914df3393110a68bd4acfe922e9cd9f5f24abf23df7022af7

48231f2cf5bda35634fca2f98dc6e8581e8a65a2819d62bc375376fcd501ba2d

49b2ca4c1bd4405aa724ffaef266395be4b4581f1ff38b1fc092eab71e1adb6a

4b32dbd7a6ca7f91e75bacf055f4132be0952385d4d4fcbaf0970913876d64a1

566fc3f32633ce0b9a7154102bc1620a906473d5944dca8dea122cb63cb1bcaa

59793de10ed2d3684d0206f5f69cbebbba61d1f90a79dbd720d26bbf54226695

61a2c53390498716494ffa0b586aa6dc6c67baf03855845e2e3f2539f1f56563

6707ba64cccab61d3a658b23b28b232b1f601e3608b7d9e4767a1c0751bccd05

71fabe5022f613dc8e06d6dfda1327989e67be4e291f3761e84e3a988751caf8

78573a4c23f6ccdcbfce3a467fa93d2a1a49cf2f8dc7b595c0185e16b84828cb

78b246cbd9b1106d01659dd0ab65dc367486855b6b37869673bd98c560b6ff52

7bfdbceded56029bc32d89249e0195ebf47309fecded2b6578b035c52c43460b

7cb501e819fc98a55b9d19ad0f325084f6c4753785e30479502457ac7cb6289c

7fa70e18c414ae523e84c4a01d73e49f86ab816d129e8d7001fb778531adf3a7

8bc29a873b6144b6384a5535df5fc762c0c65e47a2caf0e845382c72f9d6671f

8c1db376bafcd071ffb59130d58ffcde45b2fa8e79dcc44c0a14574b9de55b43

a99ebd095d2ccda69855f2c700048658b8e425c90c916d5880f91c8aba634a2e

b656b7189925b043770a9738d8ae003d7401ac65a58e78c643937f4b44a3bc2c

b8dc2c5921f668f6cf8a355fd1cb79020b6752330be5e0db4bf96ae904d76249

b90af78927c6cb2d767f777d36031c9160aeb6fcd30090c3db3735b71274eb4e

bc1e211206c69fe399505e18380fb0068356d205c7929e2cb3d2fe0b4107d4e0

bf3c84a955f49c02a7f4fbf94dbbf089f26137fc75f5b36ac0b1bace9373d17a

c11d186e6d1600212565786ed481fbe401af598e1f689cf1ce6ff83b5a3b4371

cd42ae47c330c68cc8fd94cf5d91992f55992292b186991605b262ba1f776e8e

e1e2587ae2170d9c4533a6267f9179dff67d03f7adbb6d1fb4f43468d8f42c24

f389a8cbb88dae49559eaa572fc9288c253ed1825b1ce2a61e3d8ae998625e18

fc55895bb7d08e6ab770a05e55a037b533de809196f3019fbff0f1f58e688e5f

MITRE ATT&CK

T1053.005 Scheduled Task/Job: Scheduled Task

T1059.006 Command and Scripting Interpreter: Python

T1082 System Information Discovery

T1016 System Network Configuration Discovery

T1105 Ingress Tool Transfer

T1562.001 Impair Defenses: Disable or Modify Tools

T1132 Data Encoding

T1021 Remote Services

T1056.001 Input Capture: Keylogging

T1555 Credentials from Password Stores

T1539 Steal Web Session Cookie

T1546.015 Event Triggered Execution: Screensaver

T1113 Screen Capture

T1125 Video Capture

T1016 System Network Configuration Discovery

T1105 Ingress Tool Transfer

T1059 Command and Scripting Interpreter

T1115 Clipboard Data

T1497 Virtualization/Sandbox Evasion

T1020 Automated Exfiltration

YARA rule

rule Triton_RAT { 
   meta: 
       description = "Detects Python-based Triton RAT" 
       author = "[email protected]" 
       date = "2025-03-06" 
   strings: 
       $telegram = "telebot.TeleBot" ascii 
       $extract_data = "def extract_data" ascii 
       $bot_token = "bot_token" ascii 
       $chat_id = "chat_id" ascii 
       $keylogger = "/keylogger" ascii 
       $stop_keylogger = "/stopkeylogger" ascii 
       $passwords = "/passwords" ascii 
       $clipboard = "/clipboard" ascii 
       $roblox_cookie = "/robloxcookie" ascii 
       $wifi_pass = "/wifipass" ascii 
       $sys_commands = "/(shutdown|restart|sleep|altf4|tasklist|taskkill|screenshot|mic|wallpaper|block|unblock)" ascii 
       $win_cmds = /(taskkill \/f \/im|wmic|schtasks \/create|attrib \+h|powershell\.exe -Command|reg add|netsh wlan show profile|net user|whoami|curl ipinfo\.io)/ ascii 
       $startup = "/addstartup" ascii 
       $winblocker = "/winblocker" ascii 
       $startup_scripts = /(C:\\Windows\\System32\\updateagent\.vbs|check\.bat|watchdog\.vbs)/ ascii 
   condition: 
       any of ($telegram, $extract_data, $bot_token, $chat_id) and 
       4 of ($keylogger, $stop_keylogger, $passwords, $clipboard, $roblox_cookie, $wifi_pass, 
             $sys_commands, $win_cmds, $startup, $winblocker, $startup_scripts) 
} 

Get the latest insights on emerging cyber threats

This report explores the latest trends shaping the cybersecurity landscape and what defenders need to know in 2026.

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
Tara Gould
Malware Research Lead

More in this series

No items found.

Blog

/

Network

/

March 26, 2026

Phantom Footprints: Tracking GhostSocks Malware

Default blog imageDefault blog image

Why are attackers using residential proxies?

In today's threat landscape, blending in to normal activity is the key to success for attackers and the growing reliance on residential proxies shows a significant shift in how threat actors are attempting to bypass IP detection tools.

The increasing dependency on residential proxies has exposed how prevalent proxy services are and how reliant a diverse range of threat actors are on them. From cybercriminal groups to state‑sponsored actors, the need to bypass IP detection tools is fundamental to the success of these groups. One malware that has quietly become notorious for its ability to avoid anomaly detection is GhostSocks, a malware that turns compromised devices into residential proxies.

What is GhostSocks?

Originally marketed on the Russian underground forum xss[.]is as a Malware‑as‑a‑Service (MaaS), GhostSocks enables threat actors to turn compromised devices into residential proxies, leveraging the victim's internet bandwidth to route malicious traffic through it.

How does Ghostsocks malware work? 

The malware offers the threat actor a “clean” IP address, making it look like it is coming from a household user. This enables the bypassing of geographic restrictions and IP detection tools, a perfect tool for avoiding anomaly detection. It wasn’t until 2024, when a partnership was announced with the infamous information stealer Lumma Stealer, that GhostSocks surged into widespread adoption and alluded to who may be the author of the proxy malware.

Written in GoLang, GhostSocks utilizes the SOCKS5 proxy protocol, creating a SOCKS5 connection on infected devices. It uses a relay‑based C2 implementation, where an intermediary server sits in between the real command-and-control (C2) server and the infected device.

How does Ghostsocks malware evade detection?

To further increase evasion, the Ghostsocks malware wraps its SOCKS5 tunnels in TLS encryption, allowing its malicious traffic to blend into normal network traffic.

Early variants of GhostSocks do not implement a persistence mechanism; however, later versions achieve persistence via registry run keys, ensuring sustained proxy operational time [1].

While proxying is its primary purpose, GhostSocks also incorporates backdoor functionality, enabling malicious actors to run arbitrary commands and download and deploy additional malicious payloads. This was evident with the well‑known ransomware group Black Basta, which reportedly used GhostSocks as a way of maintaining long‑term access to victims’ networks [1].

Darktrace’s detection of GhostSocks Malware

Darktrace observed a steady increase in GhostSocks activity across its customer base from late 2025, with its Threat Research team identifying multiple incidents involving the malware. In one notable case from December 2025, Darktrace detected GhostSocks operating alongside Lumma Stealer, reinforcing that the partnership between Lumma and GhostSocks remains active despite recent attempts to disrupt Lumma’s infrastructure.

Darktrace’s first detection of GhostSocks‑related activity came when a device on the network of a customer in the education sector began making connections to an endpoint with a suspicious self‑signed certificate that had never been seen on the network before.

The endpoint in question, 159.89.46[.]92 with the hostname retreaw[.]click, has been flagged by multiple open‑source intelligence (OSINT) sources as being associated with Lumma Stealer’s C2 infrastructure [2], indicating its likely role in the delivery of malicious payloads.

Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.
Figure 1: Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.

Less than two minutes later, Darktrace observed the same device downloading the executable (.exe) file “Renewable.exe” from the IP 86.54.24[.]29, which Darktrace recognized as 100% rare for this network.

Darktrace’s detection of a device downloading the unusual executable file “Renewable.exe”.
Figure 2: Darktrace’s detection of a device downloading the unusual executable file “Renewable.exe”.

Both the file MD5 hash and the executable itself have been identified by multiple OSINT vendors as being associated with the GhostSocks malware [3], with the executable likely the backdoor component of the GhostSocks malware, facilitating the distribution of additional malicious payloads [4].

Following this detection, Darktrace’s Autonomous Response capability recommended a blocking action for the device in an early attempt to stop the malicious file download. In this instance, Darktrace was configured in Human Confirmation Mode, meaning the customer’s security team was required to manually apply any mitigative response actions. Had Autonomous Response been fully enabled at the time of the attack, the connections to 86.54.24[.]29 would have been blocked, rendering the malware ineffective at reaching its C2 infrastructure and halting any further malicious communication.

 Darktrace’s Autonomous Response capability suggesting blocking the suspicious connections to the unusual endpoint from which the malicious executable was downloaded.
Figure 3: Darktrace’s Autonomous Response capability suggesting blocking the suspicious connections to the unusual endpoint from which the malicious executable was downloaded.

As the attack was able to progress, two days later the device was detected downloading additional payloads from the endpoint www.lbfs[.]site (23.106.58[.]48), including “Setup.exe”, “,.exe”, and “/vp6c63yoz.exe”.

Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.
Figure 4: Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.

Once again, Darktrace recognized the anomalous nature of these downloads and suggested that a “group pattern of life” be enforced on the offending device in an attempt to contain the activity. By enforcing a pattern of life on a device, Darktrace restricts its activity to connections and behaviors similar to those performed by peer devices within the same group, while still allowing it to carry out its expected activity, effectively preventing deviations indicative of compromise while minimizing disruption. As mentioned earlier, these mitigative actions required manual implementation, so the activity was able to continue. Darktrace proceeded to suggest further actions to contain subsequent malicious downloads, including an attempt to block all outbound traffic to stop the attack from progressing.

An overview of download activity and the Autonomous Response actions recommended by Darktrace to block the downloads.
Figure 5: An overview of download activity and the Autonomous Response actions recommended by Darktrace to block the downloads.

Around the same time, a third executable download was detected, this time from the hostname hxxp[://]d2ihv8ymzp14lr.cloudfront.net/2021-08-19/udppump[.]exe, along with the file “udppump.exe”.While GhostSocks may have been present only to facilitate the delivery of additional payloads, there is no indication that these CloudFront endpoints or files are functionally linked to GhostSocks. Rather, the evidence points to broader malicious file‑download activity.

Shortly after the multiple executable files had been downloaded, Darktrace observed the device initiating a series of repeated successful connections to several rare external endpoints, behavior consistent with early-stage C2 beaconing activity.

Cyber AI Analyst’s investigation

Darktrace’s detection of additional malicious file downloads from malicious CloudFront endpoints.
Figure 7: Darktrace’s detection of additional malicious file downloads from malicious CloudFront endpoints.

Throughout the course of this attack, Darktrace’s Cyber AI Analyst carried out its own autonomous investigation, piecing together seemingly separate events into one wider incident encompassing the first suspicious downloads beginning on December 4, the unusual connectivity to many suspicious IPs that followed, and the successful beaconing activity observed two days later. By analyzing these events in real-time and viewing them as part of the bigger picture, Cyber AI Analyst was able to construct an in‑depth breakdown of the attack to aid the customer’s investigation and remediation efforts.

Cyber AI Analyst investigation detailing the sequence of events on the compromised device, highlighting its extensive connectivity to rare endpoints, the related malicious file‑download activity, and finally the emergence of C2 beaconing behavior.
Figure 8: Cyber AI Analyst investigation detailing the sequence of events on the compromised device, highlighting its extensive connectivity to rare endpoints, the related malicious file‑download activity, and finally the emergence of C2 beaconing behavior.

Conclusion

The versatility offered by GhostSocks is far from new, but its ability to convert compromised devices into residential proxy nodes, while enabling long‑term, covert network access—illustrates how threat actors continue to maximise the value of their victims’ infrastructure. Its growing popularity, coupled with its ongoing partnership with Lumma, demonstrates that infrastructure takedowns alone are insufficient; as long as threat actors remain committed to maintaining anonymity and can rapidly rebuild their ecosystems, related malware activity is likely to persist in some form.

Credit to Isabel Evans (Cyber Analyst), Gernice Lee (Associate Principal Analyst & Regional Consultancy Lead – APJ)
Edited by Ryan Traill (Content Manager)

Appendices

References

1.    https://bloo.io/research/malware/ghostsocks

2.    https://www.virustotal.com/gui/domain/retreaw.click/community

3.    https://synthient.com/blog/ghostsocks-from-initial-access-to-residential-proxy

4.    https://www.joesandbox.com/analysis/1810568/0/html

5. https://www.virustotal.com/gui/url/fab6525bf6e77249b74736cb74501a9491109dc7950688b3ae898354eb920413

Darktrace Model Detections

Real-time Detection Models

Anomalous Connection / Suspicious Self-Signed SSL

Anomalous Connection / Rare External SSL Self-Signed

Anomalous File / EXE from Rare External Location

Anomalous File / Multiple EXE from Rare External Locations

Compromise / Possible Fast Flux C2 Activity

Compromise / Large Number of Suspicious Successful Connections

Compromise / Large Number of Suspicious Failed Connections

Compromise / Sustained SSL or HTTP Increase

Autonomous Response Models

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

Antigena / Network / External Threat / Antigena File then New Outbound Block

Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique

Resource Development – T1588 - Malware

Initial Access - T1189 - Drive-by Compromise

Persistence – T1112 – Modify Registry

Command and Control – T1071 – Application Layer Protocol

Command and Control – T1095 – Non-application Layer Protocol

Command and Control – T1071 – Web Protocols

Command and Control – T1571 – Non-Standard Port

Command and Control – T1102 – One-Way Communication

List of Indicators of Compromise (IoCs)

86.54.24[.]29 - IP - Likely GhostSocks C2

http[://]86.54.24[.]29/Renewable[.]exe - Hostname - GhostSocks Distribution Endpoint

http[://]d2ihv8ymzp14lr.cloudfront[.]net/2021-08-19/udppump[.]exe - CDN - Payload Distribution Endpoint

www.lbfs[.]site - Hostname - Likely C2 Endpoint

retreaw[.]click - Hostname - Lumma C2 Endpoint

alltipi[.]com - Hostname - Possible C2 Endpoint

w2.bruggebogeyed[.]site - Hostname - Possible C2 Endpoint

9b90c62299d4bed2e0752e2e1fc777ac50308534 - SHA1 file hash – Likely GhostSocks payload

3d9d7a7905e46a3e39a45405cb010c1baa735f9e - SHA1 file hash - Likely follow-up payload

10f928e00a1ed0181992a1e4771673566a02f4e3 - SHA1 file hash - Likely follow-up payload

Continue reading
About the author
Gernice Lee
Associate Principal Analyst & Regional Consultancy Lead

Blog

/

Email

/

March 24, 2026

Darktrace Unites Human Behavior and Threat Detection Across Email, Slack, Teams, and Zoom

Default blog imageDefault blog image

The communication attack surface is expanding

Modern attackers no longer focus solely on inboxes, they target people and the productivity systems where work actually happens. Meanwhile, the boundary between internal and external usage of tools is becoming blurrier everyday – turning the entire workplace into the attack surface. In 2025, identity compromise emerged as the single most consistent threat across the global threat landscape, as observed by Darktrace research across our entire customer base. Over 70% of incidents in the US involved SaaS/M365 account compromise and phishing or email-based social engineering, making credential abuse the single most effective initial access vector.

Despite this upward trend, investment in existing security awareness training (SAT) isn’t moving the needle on reducing risk. 84% of organizations still measure success through completion rates1, even though completion of standard training correlates with less than 2% real improvement in risky behavior.2 By prioritizing completion, organizations reward time spent rather than meaningful engagement, yet time in training doesn’t translate to retention or real-world decision-making. This compliance-first approach has left the workforce unprepared for the threats they actually face.

At the same time, attacks have evolved. Highly personalized, AI-generated campaigns now move fluidly across email, Slack, Teams, Zoom, and beyond, blending channels and even targeting systems directly through techniques like prompt injection. This new reality demands a different approach: one that treats people and the tools they use as a single ecosystem, where behavior and detection continuously inform and strengthen each other.

Only an adaptive communication security system can keep pace with the speed, creativity, and cross channel nature of today’s threats. 

Ushering in the adaptive era of workplace security

With this release, Darktrace brings together our new behavior-driven training solution with email detection, cross-channel visibility, and platform-level insights. Powered by Self-Learning AI, it delivers protection across both people and the communication tools they rely on every day, including email, Slack, Teams, and Zoom.

Each component learns from the others – training adapts to real user behavior, detection evolves across channels, and response is continuously refined – creating a powerful feedback loop that strengthens resilience and improves accuracy against today’s AI-driven threats.

Introducing: Unified training and email security for a self-improving email defense

Our brand new product, Darktrace / Adaptive Human Defense, closes the gap between human behavior and email security to continuously strengthen both people and defenses. Each user receives personalized training that adapts to their own inbox activity and skill level, with learning delivered directly within the flow of their day-to-day email interactions.

By learning from each user’s interactions with security training, it adapts security responses, creating a closed-loop system where training reinforces detection and detection informs training. Let’s look at some of the benefits.

  • Reduce successful phishing at the source with contextual Just in Time coaching: Contextual coaching appears directly in real email threads the moment risky behavior is detected, so habits change where mistakes actually happen. Configurable triggers and group policies target the right users, reducing repeated errors and administrative overhead.
  • Adaptive phishing simulations that progress automatically with each user: Embedded simulations vary in their degree of realism, from generic phishing to generative AI-enabled spear phishing. Users progress through the difficulty levels based on their performance to give an accurate picture of their phishing preparedness.  
  • Native email security integration turns human behavior into quantified risk: The native email security integration allows engagement, links clicked, and question success signals to flow back into / EMAIL recipes and models, so detection and response adapt automatically as users learn.  
  • Actionable risk and trend analytics beyond completion rates: Analytics that surface repeat offenders, high-value targets, and measurable exposure, moving beyond completion metrics to give leaders actionable insights tied to real behavior.

Learn more about / Adaptive Human Defense in the product solution brief.

Industry-first cross-channel full-message analysis for email, Slack, Teams, and Zoom

Darktrace now brings full-message analysis to Email, Slack, Teams, Zoom, and even generative AI prompts. The same leading behavioral analysis from EMAIL extends to every message, tracing intent, tone, relationships, and conversation flow across all communication activity for a complete understanding of every user interaction.

By correlating messaging and collaboration activity with email and account environments, cross-channel analysis reveals multi-domain attack paths and follows both users and threats as a single, continuous narrative – delivering better context to improve detection across the entire organization.

  • Eliminate cross-channel blind spots: Detect phishing, malware, account takeovers, and conversational manipulation across email and collaboration platforms, so attackers can’t exploit Slack, Teams, or Zoom as a new entry point. Unified behavioral analysis gives security teams a coherent, single view, for no more fragmented, channel-specific gaps.
  • Spot generative AI prompt injection attacks before they manipulate assistants: Dedicated models surface threats targeting corporate AI assistants – like ShadowLeak and Hashjack – before they can silently manipulate workflows, reducing risk before static filters catch up.

Learn more about Darktrace’s messaging security offering in the product solution brief.

Industry-first DMARC with bi-directional ASM and email security integration

Darktrace transforms domain protection by linking DMARC, attack surface intelligence, and email security into a single, continuously evolving workflow. Instead of treating domain authentication and exposure as separate tasks, this unified approach shows not just where domains are vulnerable, but how attackers are actively exploiting them.

  • Fix authentication weaknesses faster: SPF, DKIM, DMARC configurations, and external exposure data are analyzed together, giving teams clear guidance to correct weaknesses before they can be abused. Deep bidirectional integration with attack surface intelligence reduces impersonation risk at the source.
  • Accelerate email investigations: DMARC context is embedded directly into email workflows, enriching triage with authentication posture, internal/external sender lists, and seamless pivots between email and domain intelligence for faster, more accurate investigations.

Committed to innovation

These updates are part of a broader Darktrace release, which also includes:

Join our Live Launch Event on April 14, 2026.

Join us for an exclusive announcement event where Darktrace, the leader in AI-native cybersecurity, will be announcing our latest innovations, including  a demo of our new product / Adaptive Human Defense, an exclusive conversation with a Darktrace customer, and a deep dive into the Darktrace ActiveAI Security Portal.  

Register here.

References

[1] 84% of organizations still measure security awareness training success through completion rates, a vanity metric with no correlation to behavior change. (Source:  NIST Awareness Effectiveness Study, Forrester 2025)

[2] 'Limited benefit from embedded phishing training. Using randomized controlled trials and statistical modeling, embedded training provides a statistically-significant reduction in average failure rate, but of only 2%.' Ho, G., Mirian, A., Luo, E., Tong, K., Lee, E., Liu, L., Longhurst, C. A., Dameff, C., Savage, S., & Voelker, G. M. (2025). Understanding the Efficacy of Phishing Training in Practice. Proceedings of the 2025 IEEE Symposium on Security and Privacy.

Continue reading
About the author
Carlos Gray
Senior Product Marketing Manager, Email
あなたのデータ × DarktraceのAI
唯一無二のDarktrace AIで、ネットワークセキュリティを次の次元へ