Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the sharedLoad.min.js obfuscated first-stage loader dropped by the malicious npm indexed-btree package, triggered via BTree.prototype.set to evade static/taint-analysis scanners
Detects the sharedLoad.min.js obfuscated first-stage loader dropped by the malicious npm indexed-btree package, triggered via BTree.prototype.set to evade static/taint-analysis scanners
Detects the indexed-btree npm malware loader embedded in BTree.prototype.set that spawns a detached hidden Node.js child process and references sharedLoad.min.js
Detects the execution of npm commands to install specific, potentially malicious or typo-squatted npm packages. The rule filters for common npm install command line arguments and matches against a predefined list of package names that are often associated with supply chain compromise attempts.
Detects high-frequency anonymous comment submissions to 'wp-comments-post.php' from a single IP address within a 10-minute window, characterized by minimal variations in User-Agent. This behavior is indicative of an automated script attempting to bypass comment moderation settings, potentially exploiting known vulnerabilities (e.g., CVE-2026-93485) to inject content without administrative approval.
Detects high-frequency anonymous comment submissions to 'wp-comments-post.php' from a single IP address within a 10-minute window, characterized by minimal variations in User-Agent. This behavior is indicative of an automated script attempting to bypass comment moderation settings, potentially exploiting known vulnerabilities (e.g., CVE-2026-93485) to inject content without administrative approval.
Detects high-frequency anonymous comment submissions to 'wp-comments-post.php' from a single IP address within a 10-minute window, characterized by minimal variations in User-Agent. This behavior is indicative of an automated script attempting to bypass comment moderation settings, potentially exploiting known vulnerabilities (e.g., CVE-2026-93485) to inject content without administrative approval.
Detects anomalous patterns associated with potential WordPress comment moderation bypass. The rule identifies suspicious rapid sequences of comment submissions, use of unapproved comment preview endpoints with moderation hashes, or reuse of approved-commenter cookies. These behaviors can be indicative of an attacker attempting to preview or force-render stored XSS payloads to administrators or visitors before the comment has been officially approved.
Detects anomalous patterns associated with potential WordPress comment moderation bypass. The rule identifies suspicious rapid sequences of comment submissions, use of unapproved comment preview endpoints with moderation hashes, or reuse of approved-commenter cookies. These behaviors can be indicative of an attacker attempting to preview or force-render stored XSS payloads to administrators or visitors before the comment has been officially approved.
Detects anomalous patterns associated with potential WordPress comment moderation bypass. The rule identifies suspicious rapid sequences of comment submissions, use of unapproved comment preview endpoints with moderation hashes, or reuse of approved-commenter cookies. These behaviors can be indicative of an attacker attempting to preview or force-render stored XSS payloads to administrators or visitors before the comment has been officially approved.
Detects anomalous patterns associated with potential WordPress comment moderation bypass. The rule identifies suspicious rapid sequences of comment submissions, use of unapproved comment preview endpoints with moderation hashes, or reuse of approved-commenter cookies. These behaviors can be indicative of an attacker attempting to preview or force-render stored XSS payloads to administrators or visitors before the comment has been officially approved.
Detects potential zero-click administrator session hijacking by correlating a logged-in administrator accessing a page containing user-generated content (e.g., comments) followed immediately by a sensitive administrative action (e.g., plugin installation, user creation) within a 5-second window, suggesting automated script execution via an autofocus/onfocus handler triggered by the admin's browser.
Detects potential exploitation of CVE-2025-3248 in Langflow by identifying suspicious inbound network traffic targeting the /api/v1/validate/code endpoint, correlated with subsequent python child processes containing base64 decoding and execution primitives such as eval, exec, or subprocess.
Detects AI agent tooling (e.g., Copilot, Cursor, Ollama) that makes outbound network calls to known AI model providers followed by the execution of suspicious shell commands within a short duration. The rule flags high-severity activities such as download-cradles, credential reconnaissance, LOLBin usage, and persistence mechanisms. Informational alerts are generated for standard CLI activity within the same timeframe.
Detects AI agent tooling (e.g., Copilot, Cursor, Ollama) that makes outbound network calls to known AI model providers followed by the execution of suspicious shell commands within a short duration. The rule flags high-severity activities such as download-cradles, credential reconnaissance, LOLBin usage, and persistence mechanisms. Informational alerts are generated for standard CLI activity within the same timeframe.
Detects AI agent tooling (e.g., Copilot, Cursor, Ollama) that makes outbound network calls to known AI model providers followed by the execution of suspicious shell commands within a short duration. The rule flags high-severity activities such as download-cradles, credential reconnaissance, LOLBin usage, and persistence mechanisms. Informational alerts are generated for standard CLI activity within the same timeframe.
Detects instances where AI-based coding or agent-driven tools (e.g., Cursor, Claude Code, AutoGPT) perform suspicious file archiving/packaging activities followed by network egress to unauthorized or known exfiltration-prone services. This helps identify potential data exfiltration by AI agents or compromised development environments.
Detects instances where AI-based coding or agent-driven tools (e.g., Cursor, Claude Code, AutoGPT) perform suspicious file archiving/packaging activities followed by network egress to unauthorized or known exfiltration-prone services. This helps identify potential data exfiltration by AI agents or compromised development environments.
Detects AI coding assistants and LLM agent frameworks attempting to access, read, or export sensitive credential files, registry hives, or application secrets. The rule monitors both file system operations on known secret locations and process execution patterns indicative of credential dumping or API token retrieval by these tools.
Detects AI coding assistants and LLM agent frameworks attempting to access, read, or export sensitive credential files, registry hives, or application secrets. The rule monitors both file system operations on known secret locations and process execution patterns indicative of credential dumping or API token retrieval by these tools.
Detects AI coding assistants and LLM agent frameworks spawning shell processes or interpreters to execute potentially malicious commands. The rule monitors for suspicious activity often associated with download cradles, credential access, reconnaissance, or persistence, while excluding standard interactive terminal usage.
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