Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects various activities associated with the Charon ransomware, including DLL sideloading, ransomware deployment, payload reading, ransom note creation, shadow copy deletion, and service creation for persistence. It leverages specific filenames, process behaviors, and registry modifications to identify the threat.
Detects file creation events for the TeamPCP systemd persistence mechanism, specifically looking for 'sysmon.py' or 'sysmon.service' dropped in hidden user config directories. This persistence mechanism ensures the attacker maintains access and can download further stages via C2 even if the original vulnerable litellm process terminates.
This rule detects the presence or execution of files identified by specific SHA256 hashes or file names associated with PAM-based backdoors on Linux systems. It monitors device events, file events, process events, and image load events for these indicators. The rule aims to identify the 'Plague' PAM-based backdoor or similar malicious PAM modules.
Detects the creation or modification of files within the misspelled 'Photoes' directory (e.g., \ProgramData\Photoes\). This directory is utilized by the BRUSHWORM backdoor for its main installation and by the BRUSHLOGGER keylogger for storing keystroke log files.
Detects the creation or modification of files within the misspelled 'Photoes' directory (e.g., \ProgramData\Photoes\). This directory is utilized by the BRUSHWORM backdoor for its main installation and by the BRUSHLOGGER keylogger for storing keystroke log files.
Detects the creation of the ValleyRAT configuration file 'bb.jpg' in the public download directory. This activity is actively associated with Silver Fox (Void Arachne) campaigns targeting South Asia.
Detects the creation of specific file artifacts (WhatsAppData.zip, whatsapp_backup.lock) associated with a Python-based WhatsApp stealer deployed by the Silver Fox (Void Arachne) intrusion set. This malware is designed to collect credentials and host data.
Detects log files or unpacked binaries associated with the .NET infostealer 'AI Theater' that uses ChatGPT for illusory evasion techniques. Identifies specific log markers such as LLM verification and evasion generation.
Detects the creation or modification of .NET application configuration files (`.exe.config`) in sensitive framework directories. This is a technique known as AppDomain Hijacking, used by the Silver Dragon APT group to hijack legitimate Windows services like `dfsvc.exe` and `tzsync.exe` for persistence and to load their malware (MonikerLoader).
This rule detects the execution chain associated with MicroStealer, specifically looking for processes named 'RocobeSetup.exe', 'Game Launcher.exe', or 'miicrosoft.exe' that are involved in actions related to a file named 'soft.jar'. This indicates the potential presence and activity of the MicroStealer malware.
This rule detects the presence of MicroStealer malware by matching known MD5, SHA1, or SHA256 hashes of its binaries against file events on monitored devices. MicroStealer is a credential stealer that targets various applications to exfiltrate sensitive information.
Detects known malicious invoice PDFs and email samples used in Crimson Kingsnake campaigns.
This rule detects the presence of files on endpoints that match a list of known malicious SHA256 hashes associated with the Nuso malware. It queries DeviceFileEvents to identify any file with a matching hash.
Detects LampoRAT malware based on known file hash execution.
Description- Detects PDF invoice files associated with Crimson Kingsnake using consistent metadata author “hpins”.
Detects GhostBackdoor malware execution via known file hash.
This rule detects the presence of Phoenix malware by identifying known SHA256 hashes of its components or network connections to its command and control (C2) infrastructure. It correlates file events with known malware hashes and network events with known C2 IP addresses.
Attackers do not only use emojis in the subject. Sometimes they also use them in the file name itself to make the file look more attractive or legitimate. Based on my experience, I am not expecting legitimate files names with icons so it can be an interesting case to easily convert the hunting into a detection. For example:
📄Invoice.pdf
🔐Reset_Password.html
📦Delivery_Document.zip
This can help find:
- Suspicious files dropped on disk
- Files downloaded from phishing emails
- User-downloaded scam files
- Payloads with social engineering names
📄Invoice.pdf
🔐Reset_Password.html
📦Delivery_Document.zip
This can help find:
- Suspicious files dropped on disk
- Files downloaded from phishing emails
- User-downloaded scam files
- Payloads with social engineering names
This rule detects the presence of Blackbeard malware by identifying known SHA256 hashes of its components or network connections to its known C2 infrastructure. It correlates file events with specific hashes and network events with specific remote IP addresses.
This query detects multiple TTPs associated with the Storm-2561 campaign that uses SEO poisoning to distribute fake VPN clients for credential theft. It looks for known file hashes, a specific code-signing certificate, malicious DLL side-loading and C2 communication patterns.
Detects malicious/suspicious systemd generator files for persistence mechanisms
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