Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,270 detections
Filters
Last updated
All Time
Detection languages
3,342
1,086
505
478
472
Contributors
1,824
1,622
874
505
300
Categories
3,031
2,542
1,076
1,049
929
Platforms
39,961
7,270
6,537
3,868
3,651
Products / Services
1,331
1,109
991
658
494
MITRE Techniques
2,271
1,336
1,318
904
885
CVEs
66
65
33
32
28
IDS Classtypes
81
80
56
27
10
IDS Protocols
149
98
6
5
4
This rule detects potential web shell activity by identifying instances where a web server process creates or modifies a PHP file within a 'wp-content/themes' directory, followed closely (within 15 minutes) by that same web server process spawning a shell process (e.g., sh, bash, cmd.exe).
This rule detects potential web shell activity by identifying instances where a web server process creates or modifies a PHP file within a 'wp-content/themes' directory, followed closely (within 15 minutes) by that same web server process spawning a shell process (e.g., sh, bash, cmd.exe).
This rule detects processes accessing sensitive credential, configuration, or environment files typically stored in locations like .aws, .ssh, .kube, or application user directories. It alerts when a non-standard or unexpected process touches multiple sensitive folders, or when specific suspicious process names or known malicious tools (such as 'NeedleStealer') are observed interacting with these files. This is indicative of potential credential harvesting or reconnaissance activities.
Detects a suspected exploitation attempt of CVE-2026-81000, known as TUNderflow. The detection looks for a sequence of events where a process initializes a user namespace (unshare) followed by a kernel panic or error report from the tun/tap driver originating from the same process lineage within a short timeframe, which is indicative of an integer overflow exploit leading to kernel instability.
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 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 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 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 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 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.
Page 62 of 347