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April 12, 2023

P2Pinfect - New Variant Targets MIPS Devices

A new P2Pinfect variant compiled for the Microprocessor without Interlocked Pipelined Stages (MIPS) architecture has been discovered. This demonstrates increased targeting of routers, Internet of Things (IoT) and other embedded devices by those behind P2Pinfect.
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Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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12
Apr 2023

Introduction: P2PInfect

Since July 2023, researchers at Cado Security Labs (now part of Darktrace) have been monitoring and reporting on the rapid growth of a cross-platform botnet, named “P2Pinfect”. As the name suggests, the malware - written in Rust - acts as a botnet agent, connecting infected hosts in a peer-to-peer topology. In early samples, the malware exploited Redis for initial access - a relatively common technique in cloud environments. 

There are a number of methods for exploiting Redis servers, several of which appear to be utilized by P2Pinfect. These include exploitation of CVE-2022-0543[1] - a sandbox escape vulnerability in the LUA scripting language (reported by Unit42 [2]), and, as reported previously by Cado Security Labs, an unauthorized replication attack resulting in the loading of a malicious Redis module.  

Researchers have since encountered a new variant of the malware, specifically targeting embedded devices based on 32-bit MIPS processors, and attempting to brute force SSH access to these devices. It’s highly likely that by targeting MIPS, the P2Pinfect developers intend to infect routers and IoT devices with the malware. Use of MIPS processors is common for embedded devices and the architecture has been previously targeted by botnet malware, including high-profile families like Mirai [3], and its variants/derivatives.

Not only is this an interesting development in that it demonstrates a widening of scope for the developers behind P2Pinfect (more supported processor architectures equals more nodes in the botnet itself), but the MIPS32 sample includes some notable defense evasion techniques. 

This, combined with the malware’s utilization of Rust (aiding cross-platform development) and rapid growth of the botnet itself, reinforces previous suggestions that this campaign is being conducted by a sophisticated threat actor.

Initial access

Cado researchers encountered the MIPS variant of P2Pinfect after triaging files uploaded via SFTP and SCP to a SSH honeypot. Although earlier variants had been observed scanning for SSH servers, and attempting to propagate the malware via SSH as part of its worming procedure, researchers had yet to observe successful implantation of a P2Pinfect sample using this method - until now.

In keeping with similar botnet families, P2Pinfect includes a number of common username/password pairs embedded within the MIPS binary itself. The malware will then iterate through these pairs, initiating a SSH connection with servers identified during the scanning phase to conduct a brute force attack. 

It was assumed that SSH would be the primary method of propagation for the MIPS variant, due to routers and other embedded devices being more likely to utilize SSH. However, additional research shows that it is in fact possible to run the Redis server on MIPS. This is achievable via an OpenWRT package named redis-server. [4]

It is unclear what use-case running Redis on an embedded MIPS device solves, or whether it is commonly encountered in the wild. If such a device is compromised by P2Pinfect and has the Redis-server package installed, it is perfectly feasible for that node to then be used to compromise new peers via one of the reported P2Pinfect attack patterns, involving exploitation of Redis or SSH brute-forcing.

Static analysis

The MIPS variant of P2Pinfect is a 32-bit, statically-linked, ELF binary with stripped debug information. Basic static analysis revealed the presence of an additional ELF executable, along with a 32-bit Windows DLL in the PE32 format - more on this later. 

This piqued the interest of Cado analysts, as it is unusual to encounter a compiled ELF with an embedded DLL. Consequently, it was a defining feature of the original P2Pinfect samples.

Embedded Windows PE32 executable
Figure 1: Embedded Windows PE32 executable

Further analysis of the host executable revealed a structure named “BotnetConf” with members consistent in naming with the original P2Pinfect samples. 

Example of a partially populated version of the BotnetConf struct 
Figure 2: Example of a partially populated version of the BotnetConf struct 

As the name suggests, this structure defines the configuration of the malware itself, whilst also storing the IP addresses of nodes identified during the SSH and Redis scans. This, in combination with the embedded ELF and DLL, along with the use of the Rust programming language allowed for positive attribution of this sample to the P2Pinfect family.

Updated evasion - consulting tracerpid

One of the more interesting aspects of the MIPS sample was the inclusion of a new evasion technique. Shortly after execution, the sample calls fork() to spawn a child process. 

The child process then proceeds to access /proc using openat(), determines its own Process Identifier (PID) using the Linux getpid() syscall, and then uses this PID to consult the relevant /proc subdirectory and read the status file within that. Note that this is likely achieved in the source code by resolving the symbolic link at /proc/self/status.

Example contents of /proc/pid/status when process not being traced
Figure 3: Example contents of /proc/pid/status when process not being traced

/proc/<pid>/status contains human-readable metadata and other information about the process itself, including memory usage and the name of the command currently being run. Importantly, the status file also contains a field TracerPID:. This field is assigned a value of 0 if the current process is not being traced by dynamic analysis tools, such as strace and ltrace.

Example MIPS disassembly showing reading of /proc/pid/status file
Figure 4: Example MIPS disassembly showing reading of /proc/pid/status file

If this value is non-zero, the MIPS variant of P2Pinfect determines that it is being analyzed and will immediately terminate both the child process and its parent. 

read(5, "Name:\tmips_embedded_p\nUmask:\t002", 32) = 32 
read(5, "2\nState:\tR (running)\nTgid:\t975\nN", 32) = 32 
read(5, "gid:\t0\nPid:\t975\nPPid:\t1\nTracerPid:\t971\nUid:\t0\t0\t0\t0\nGid:\t0\t0\t0\t0", 64) = 64 
read(5, "\nFDSize:\t32\nGroups:\t0 \nNStgid:\t975\nNSpid:\t975\nNSpgid:\t975\nNSsid:\t975\nVmPeak:\t    3200 kB\nVmSize:\t    3192 kB\nVmLck:\t       0 kB\n", 128) = 128 
read(5, "VmPin:\t       0 kB\nVmHWM:\t    1564 kB\nVmRSS:\t    1560 kB\nRssAnon:\t      60 kB\nRssFile:\t    1500 kB\nRssShmem:\t       0 kB\nVmData:\t     108 kB\nVmStk:\t     132 kB\nVmExe:\t    2932 kB\nVmLib:\t       8 kB\nVmPTE:\t      16 kB\nVmSwap:\t       0 kB\nCoreDumping:\t0\nThre", 256) = 256 
mmap2(NULL, 4096, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x77ff1000 
read(5, "ads:\t1\nSigQ:\t0/1749\nSigPnd:\t00000000000000000000000000000000\nShdPnd:\t00000000000000000000000000000000\nSigBlk:\t00000000000000000000000000000000\nSigIgn:\t00000000000000000000000000001000\nSigCgt:\t00000000000000000000000000000600\nCapInh:\t0000000000000000\nCapPrm:\t0000003fffffffff\nCapEff:\t0000003fffffffff\nCapBnd:\t0000003fffffffff\nCapAmb:\t0000000000000000\nNoNewPrivs:\t0\nSeccomp:\t0\nSpeculation_Store_Bypass:\tunknown\nCpus_allowed:\t1\nCpus_allowed_list:\t0\nMems_allowed:\t1\nMems_allowed_list:\t0\nvoluntary_ctxt_switches:\t92\nn", 512) = 512 
mmap2(NULL, 8192, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x77fef000 
munmap(0x77ff1000, 4096)                = 0 
read(5, "onvoluntary_ctxt_switches:\t0\n", 1024) = 29 
read(5, "", 995)                        = 0 
close(5)                                = 0 
munmap(0x77fef000, 8192)                = 0 
sigaltstack({ss_sp=NULL, ss_flags=SS_DISABLE, ss_size=8192}, NULL) = 0 
munmap(0x77ff4000, 12288)               = 0 
exit_group(-101)                        = ? 
+++ exited with 155 +++ 

Strace output demonstrating TracerPid evasion technique

Updated evasion - disabling core dumps

Interestingly, the sample will also attempt to disable Linux core dumps. This is likely used as an anti-forensics procedure as the memory regions written to disk as part of the core dump can often contain internal information about the malware itself. In the case of P2Pinfect, this would likely include information such as IP addresses of connected peers and the populated BotnetConf structure mentioned previously. 

It is also possible that the sample prevents core dumps from being created to protect the availability of the MIPS device itself. Low-powered embedded devices are unlikely to have much local storage available and core dumps could quickly fill what little storage they do have, affecting performance of the device itself.

A screen shot of a computer codeAI-generated content may be incorrect.
Image 5

This procedure can be observed during dynamic analysis, with the binary utilising the prctl() syscall and passing the parameters PR_SET_DUMPABLE, SUID_DUMP_DISABLE.

munmap(0x77ff1000, 4096)                = 0 
prctl(PR_SET_DUMPABLE, SUID_DUMP_DISABLE) = 0 
prlimit64(0, RLIMIT_CORE, {rlim_cur=0, rlim_max=0}, NULL) = 0 

Example strace output demonstrating disabling of core dumps

Embedded DLL

As mentioned in the Static Analysis section, the MIPS variant of P2Pinfect includes an embedded 64-bit Windows DLL. This DLL acts as a malicious loadable module for Redis, implementing the system.exec functionality to allow the running of shell commands on a compromised host.

Disassembly of the Redis module entrypoint
Figure 6: Disassembly of the Redis module entrypoint, mapping the system.exec command to a handler

This is consistent with the previous examples of P2Pinfect, and demonstrates that the intention is to utilize MIPS devices for the Redis-specific initial access attack patterns mentioned throughout this blog. 

Interestingly, this embedded DLL also includes a Virtual Machine (VM) evasion function, demonstrating the lengths that the P2Pinfect developers have taken to hinder the analysis process. In the DLLs main function, a call can be observed to a function helpfully labelled anti_vm by IDAs Lumina feature.

Decompiler output showing call to anti_vm function
Figure 7: Decompiler output showing call to anti_vm function

Viewing the function itself, it can be seen that researchers Christopher Gardner and Moritz Raabe have identified it as a known VM evasion method in other malware samples.

IDA’s graph view for the anti_vm function showing Lumina annotations
Figure 8: IDA’s graph view for the anti_vm function showing Lumina annotations

Conclusion

P2Pinfect’s continued evolution and broadened targeting appear to be the utilization of a variety of evasion techniques demonstrate an above-average level of sophistication when it comes to malware development. This is a botnet that will continue to grow until it’s properly utilized by its operators. 

While much of the functionality of the MIPS variant is consistent with the previous variants of this malware, the developer’s efforts in making both the host and embedded executables as evasive as possible show a continued commitment to complicating the analysis procedure. The use of anti-forensics measures such as the disabling of core dumps on Linux systems also supports this.

Indicators of compromise (IoCs)

Files SHA256

MIPS ELF 8b704d6334e59475a578d627ae4bcb9c1d6987635089790350c92eafc28f5a6c

Embedded DLL Redis Module  d75d2c560126080f138b9c78ac1038ff2e7147d156d1728541501bc801b6662f

References:

[1] https://nvd.nist.gov/vuln/detail/CVE-2022-0543

[2] https://unit42.paloaltonetworks.com/peer-to-peer-worm-p2pinfect/

[3] https://unit42.paloaltonetworks.com/mirai-variant-iz1h9/

[4] https://openwrt.org/packages/pkgdata/redis-server

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
The Darktrace Community

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March 26, 2026

Phantom Footprints: Tracking GhostSocks Malware

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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

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

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March 24, 2026

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

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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.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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