Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,141 detections
Filters
Last updated
All Time
Detection languages
3,227
1,086
498
476
469
Contributors
1,824
1,607
872
505
300
Categories
2,978
2,471
1,062
1,026
910
Platforms
39,793
7,141
6,515
3,861
3,600
Products / Services
1,325
1,070
970
655
490
MITRE Techniques
2,230
1,319
1,319
875
873
CVEs
66
65
33
32
28
IDS Classtypes
81
79
56
27
10
IDS Protocols
149
98
6
5
4
Detects process command lines containing LLM tool-calling syntax (e.g., 'tool_call', 'function_call') in conjunction with keywords associated with offensive security capabilities (e.g., 'exploit', 'mimikatz', 'exfiltrate'). This pattern is consistent with the behavior of malicious LLM-based agents attempting to autonomously invoke and execute offensive tasks.
Detects instances where a single process on a device communicates with two or more distinct hosted LLM provider APIs (OpenAI, Anthropic, DeepSeek, Google) within a 24-hour window. This behavior is indicative of multi-model orchestration or 'consensus' techniques, which may be employed by sophisticated implants for automated tasking, data processing, or evasion.
Detects instances where a single process on a device communicates with two or more distinct hosted LLM provider APIs (OpenAI, Anthropic, DeepSeek, Google) within a 24-hour window. This behavior is indicative of multi-model orchestration or 'consensus' techniques, which may be employed by sophisticated implants for automated tasking, data processing, or evasion.
Detects process command lines containing LLM tool-calling syntax (e.g., 'tool_call', 'function_call') in conjunction with keywords associated with offensive security capabilities (e.g., 'exploit', 'mimikatz', 'exfiltrate'). This pattern is consistent with the behavior of malicious LLM-based agents attempting to autonomously invoke and execute offensive tasks.
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.
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.
Detects HTTP POST requests directed at Grav CMS endpoints where the __unique_form_id__ parameter contains directory traversal sequences (e.g., ../). This behavior is indicative of attempted exploitation of CVE-2026-42608, aimed at bypassing file system restrictions to write files to unauthorized locations outside the application's expected form data directory.
Detects instances where a Claude process accesses a 'SKILL.md' file, followed closely by a suspicious command execution on the same device. The rule specifically targets command-line activity that involves encoding, download-and-execute patterns, staging in sensitive directories, or communication with suspicious domains/IPs, which is indicative of a supply-chain or poisoned agent-skill file attack.
Detects instances where a Claude process accesses a 'SKILL.md' file, followed closely by a suspicious command execution on the same device. The rule specifically targets command-line activity that involves encoding, download-and-execute patterns, staging in sensitive directories, or communication with suspicious domains/IPs, which is indicative of a supply-chain or poisoned agent-skill file attack.
Detects the GitHub Actions runner executing the Bun runtime to run a hidden index.js payload, specifically associated with the compromised 'actions-cool/issues-helper' or 'actions-cool/maintain-one-comment' actions. The rule filters for process execution context tied to GitHub runner internals and validates that the Bun execution follows the pattern observed in these supply chain attacks.
Detects the GitHub Actions runner executing the Bun runtime to run a hidden index.js payload, specifically associated with the compromised 'actions-cool/issues-helper' or 'actions-cool/maintain-one-comment' actions. The rule filters for process execution context tied to GitHub runner internals and validates that the Bun execution follows the pattern observed in these supply chain attacks.
Detects the GitHub Actions runner executing the Bun runtime to run a hidden index.js payload, specifically associated with the compromised 'actions-cool/issues-helper' or 'actions-cool/maintain-one-comment' actions. The rule filters for process execution context tied to GitHub runner internals and validates that the Bun execution follows the pattern observed in these supply chain attacks.
Detects unauthorized execution of a Bun runtime executing an 'index.js' payload within a GitHub Actions workflow context, specifically targeting known compromised action paths (e.g., issues-helper, maintain-one-comment). The detection requires correlation with either sensitive environment/secret access or anomalous outbound network activity originating from the same process, indicating potential credential theft or C2 communication by a malicious CI/CD pipeline component.
Detects execution of netcat with the "-e" flag followed by common shells. This could be a sign of a potential reverse shell setup.
Detects JavaScript loaders associated with the Exvicy/ErrTraffic campaign. The rule identifies obfuscated code that utilizes atob() for Base64 decoding, single-byte XOR operations, TextDecoder for content processing, and dynamic code execution via 'new Function()'. This pattern is commonly used to inject malicious payloads into compromised WordPress sites.
Detects the presence of a shared client-side JavaScript framework used in 'ClickFix' MaaS (Malware-as-a-Service) panels, specifically associated with the Exvicy and ErrTraffic campaigns. The rule identifies specific function patterns (e.g., xdReq, xdDec, waitForToken) and state machine logic (tokens, deduplication hashing) commonly used for token interception and clipboard interaction.
Detects the initialization of known-malicious Terraform providers (dockerd) followed within one hour by the execution of a 'go run' process from the .terraform directory. This pattern is indicative of a supply chain compromise where an adversary leverages a typosquatted or malicious Terraform provider to facilitate the execution of a secondary payload.
Detects the initialization of known-malicious Terraform providers (dockerd) followed within one hour by the execution of a 'go run' process from the .terraform directory. This pattern is indicative of a supply chain compromise where an adversary leverages a typosquatted or malicious Terraform provider to facilitate the execution of a secondary payload.
Page 45 of 341