Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects potential exploitation of an out-of-bounds write vulnerability in Python's Pillow library (ImageCms module). It identifies Python processes initialized with indicators related to known exploit research (exploitarium) that subsequently terminate with error signals indicative of a crash, such as heap-buffer-overflows or memory management errors, suggesting successful or attempted exploitation.
Detects the execution of Python-based proof-of-concept scripts related to the Pillow ImageCms out-of-bounds write vulnerability. The rule monitors for Python process execution involving specific file paths associated with the PoC, and correlates this with subsequent process crashes or abort signals which are indicative of an exploit attempt resulting in process instability.
Detects the execution of the 'curl' utility with parameters configured to interact with an SMTP server using 'EXPN' or 'VRFY' commands while loading a configuration file via the '-K' flag. This pattern is indicative of an attempt to exploit CVEs or software vulnerabilities that are susceptible to CRLF injection within SMTP recipient fields, potentially leading to unauthorized mail relaying or protocol manipulation.
Detects suspicious usage of the Bun runtime, specifically when it is spawned by common Node.js development processes (like npm or node-gyp) or downloaded from non-official sources using command-line tools or browsers. This behavior is associated with the Miasma malware campaign, which abuses the Bun runtime for malicious operations.
This rule detects potentially malicious activity related to the 'node-gyp' build tool used by npm. It monitors for two patterns: 1) npm spawning node-gyp, which can be an automated trigger for malicious binding.gyp files, and 2) node-gyp spawning node.exe with command-line arguments indicative of obfuscated JavaScript (e.g., base64 encoding, eval, AES-GCM patterns), a technique often seen in malicious Node.js packages.
This rule detects the presence and execution of files associated with the proof-of-concept (PoC) exploit for CVE-2026-55200, a remote code execution vulnerability in libssh2. The rule monitors for specific exploit binary names and directory structures commonly used by security researchers or attackers to test or exploit this vulnerability.
Detects the exploitation of CVE-2026-20253, where the Flowise application fails to correctly sanitize or validate environment variables. By using a case-insensitive bypass (e.g., lowercase 'node_options'), an attacker can cause a spawned Node.js child process to honor the variable and execute unauthorized JavaScript files via the '--require' command-line argument.
Detects the use of 'docker cp' to copy files out of a container to the host filesystem. This rule identifies suspicious patterns that may exploit the path traversal vulnerability CVE-2026-43503, which allows an attacker to escape the intended destination directory on the host.
This rule monitors web server access logs for suspicious activity targeting sensitive Flowise API endpoints (credentials, variables, secretvariable, apikey). It flags two types of threats: 1) Case-bypass evasion attempts, where canonical endpoints are bypassed via non-standard casing while requesting sensitive data (e.g., matching a status 200 on non-canonical casing for sensitive paths), and 2) High-frequency requests indicative of bulk enumeration or directory/wordlist scanning.
Detects the installation of Visual Studio Code extensions from sources other than the official Marketplace. This activity is monitored by checking process command line arguments for the --install-extension flag combined with external URLs or local file paths, which may indicate an attempt to install malicious extensions.
Detects execution of persistence-related commands such as cron job modifications or SSH key additions originating from development-related parent processes or installation commands like npm/pip. This behavior is indicative of potential supply chain compromises or malicious actors abusing development environments to maintain access.
This rule monitors for processes attempting to enumerate sensitive environment variables related to cloud services (AWS) and AI platforms (Anthropic), or access specific configuration and credential files (e.g., .aws/credentials, .config/anthropic). It specifically correlates these behaviors with potentially suspicious parent processes like bash, node, or python interpreters, which may indicate an AI agent or a reverse shell performing reconnaissance or credential exfiltration.
Detects suspicious child processes spawned by AI coding agents that are potentially indicative of an indirect prompt injection attack. The rule specifically looks for the execution of 'axiom init' or 'pip install' commands, which are often used by attackers to execute malicious code within an environment when tricked by instructions embedded in repository files.
Detects network connections attempting to access Kubernetes API endpoints related to secrets. This could indicate an adversary trying to enumerate or exfiltrate sensitive credentials from a Kubernetes cluster.
Detects phishing links delivered through Microsoft Teams or Slack messages
Detects the initialization of unprivileged user and network namespaces combined with specific IPsec tunnel configurations (xfrm/esp/veth). This sequence of operations is a known prerequisite for exploiting the DirtyClone (CVE-2026-43503) vulnerability, a Local Privilege Escalation (LPE) in the Linux kernel.
Detects indicators of SSH-based lateral movement and credential access, including suspicious SSH command-line flags (e.g., BatchMode, StrictHostKeyChecking), manual enumeration of SSH keys via ssh-add, inspection of agent socket variables, and unauthorized access to critical SSH configuration and identity files by non-standard processes.
Detects exploitation attempts against CVE-2026-33017, an unauthenticated remote code execution vulnerability in Langflow. The rule identifies suspicious HTTP POST requests directed at specific vulnerable API endpoints, looking for common Python code injection payloads (e.g., 'os.system', '__import__', 'subprocess') or the presence of a known dropper payload ('isp.sh') within the URI query. It also flags requests originating from common automation user agents like 'python-requests'.
Identifies network connections to known ShinyHunters IP addresses or domains associated with the recent Oracle PeopleSoft exploitation campaign. This rule monitors both endpoint network events and perimeter firewall/proxy logs for outbound communication to the specified malicious infrastructure.
Detects a highly relevant Antivirus alert that reports password dumpers and stealers.
This event must not be ignored just because the AV has blocked the malware but investigate, how it came there in the first place and check if passwords need to be reset.
This event must not be ignored just because the AV has blocked the malware but investigate, how it came there in the first place and check if passwords need to be reset.
Detects a highly relevant Antivirus alert that reports a web shell.
It's highly recommended to tune this rule to the specific strings used by your anti virus solution by downloading a big WebShell repository from e.g. github and checking the matches.
This event must not be ignored just because the AV has blocked the malware but investigate, how it came there in the first place.
It's highly recommended to tune this rule to the specific strings used by your anti virus solution by downloading a big WebShell repository from e.g. github and checking the matches.
This event must not be ignored just because the AV has blocked the malware but investigate, how it came there in the first place.
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