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
39,960
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
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects artifacts associated with the JWR phishing framework client engine, specifically focusing on the presence of self-referential .toString().search() anti-debugging checks, decoy variable naming conventions, and WebSocket communication components (JWRCID/JWRCVV, ws-worker.js) used for session establishment.
Detects anomalous behavioral patterns where an AI agent process (e.g., Claude Code, Cursor, Aider) performs multi-category discovery of its own runtime environment, plugins, and host configuration. The rule identifies agents that trigger processes spanning at least three distinct discovery categories (Network, File/Plugin, Software, or System) within a short timeframe, indicating potential reconnaissance of agent-authorized capabilities and tools rather than standard host exploration. Covers T1082, T1518, T1083, T1016
Detects the execution of shell interpreters (PowerShell, CMD, Bash, Zsh) with suspicious command-line arguments typically associated with malicious activity, such as encoded commands, hidden window styles, or network download requests, when initiated by common user-facing applications like web browsers or Windows Explorer.
This rule detects multiple suspicious file write operations on a Linux host, specifically targeting directories often used for persistence such as /etc/apt/apt.conf.d, /etc/profile.d, and /etc/cron.d. The rule flags hosts where at least two of these specific persistence techniques are utilized within a 24-hour window, which is indicative of an automated, redundant persistence strategy often employed by cryptocurrency miners like XMRig.
This rule detects suspicious activity associated with Redis server processes attempting to write to SSH 'authorized_keys' files or executing commands (such as BGREWRITEAOF or directory reconfiguration) commonly used by attackers to inject malicious SSH public keys into a Redis installation for persistent remote access.
Detects malicious bash scripts identified as part of the BYOB C2 framework infrastructure. These scripts are designed to download and install second-stage payloads such as Meterpreter or XMRig-style cryptominers. The rule identifies these scripts by their specific naming conventions, embedded C2 infrastructure indicators, and persistence mechanisms including crontab, systemd, and rc.local.
Detects the creation or modification of a file within the /etc/cron.d/ directory, followed shortly by a Bash process invocation using /dev/tcp redirection. This pattern is commonly used by attackers to achieve persistence via cron and establish reverse shells, bypassing potential limitations of default system shells that do not support /dev/tcp syntax.
Detects a post-compromise pattern on a WordPress site where an attacker, after an initial login, rapidly accesses multiple sensitive administrative pages (plugins.php, profile.php, etc.) within a short window, suggesting nonce harvesting. This activity is correlated with the subsequent creation of a suspected webshell file named 's.php'.
This rule detects the execution of common Node.js package managers (npm, yarn, pnpm) attempting to install specific dependencies that match known patterns of malicious or typosquatted packages often used in supply chain attacks. The detection focuses on suspicious package names that mimic common data structure library naming conventions.
Detects the presence of an XMRig cryptocurrency miner masquerading as the systemd-logind process. The rule identifies processes named 'systemd-logind' that either reside in an incorrect file path ('/usr/sbin/systemd-logind') or utilize command-line arguments characteristic of XMRig mining activity. This behavior is associated with the CARBONATO botnet.
This rule detects scenarios where an AI agent executes shell commands or system-level tools immediately after receiving a prompt injection attack from an untrusted source. It correlates agent audit logs—specifically identifying tool calls flagged for prompt injection—with actual endpoint process creation events. It flags process execution as 'High' severity if the command line matches common malicious patterns like encoded commands, IEX, or attempts to access credential material (e.g., lsass, mimikatz).
This rule detects known malicious activity including network connections to a specific C2 IP address (69.48.229.140), downloading files from specific C2 URLs, the presence of known malicious file hashes (SHA256), and communications involving specific actor-associated email addresses.
This rule detects a node.exe process initiating a network connection to a specific remote IP (69.48.229.140) on port 8080, followed by the termination of that same process within 45 seconds of the connection. This behavior is indicative of a short-lived beaconing process or an ephemeral network task associated with command-and-control activity.
This rule detects a node.exe process initiating a network connection to a specific remote IP (69.48.229.140) on port 8080, followed by the termination of that same process within 45 seconds of the connection. This behavior is indicative of a short-lived beaconing process or an ephemeral network task associated with command-and-control activity.
Detects execution of remote payloads by piping the output of network utilities like curl or wget directly into a shell interpreter (bash, sh, zsh). This pattern is a common technique used by attackers to execute malicious scripts directly from a remote server without saving a file to disk.
Detects the creation of a new local account that is immediately followed by (or associated with) execution from a process identified as an AI development or agent-based tool (e.g., Cursor, GitHub Copilot, Ollama). The rule correlates process creation events with Windows Security Event ID 4720 (User account created) and optionally checks for subsequent addition to the local Administrators group. This logic aims to surface potential unauthorized credential creation triggered by automated AI coding assistants or LLM-driven workflows.
This rule detects network activity and cloud API events indicative of an adversary utilizing the VAPI.ai platform to orchestrate automated voice-phishing (vishing) campaigns. It correlates outbound network connections to VAPI.ai endpoints with specific webhook callback patterns used to track call status for victim records.
Detects the Redis server process writing or modifying files within cron configuration directories. This is a common persistence technique used by attackers to gain recurring execution after exploiting vulnerable or misconfigured Redis instances.
Page 43 of 347