Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
1,588 detections
Filters
Last updated
All Time
Detection languages
830
199
132
116
107
Contributors
608
289
249
88
63
Categories
20,440
11,814
5,871
5,145
4,864
1,588
Platforms
1,255
461
295
76
68
Products / Services
530
382
139
113
85
MITRE Techniques
629
565
504
437
297
CVEs
5
4
3
3
3
IDS Classtypes
5
2
2
1
IDS Protocols
5
3
2
Detects a two-stage activity: first, the potential unauthorized reading or access of LLM provider API keys from files, environment variables, or command-line arguments; and second, the subsequent use of those same credentials to authenticate to LLM provider APIs (OpenAI, Anthropic, Azure, Bedrock) from a different, unrecognized source IP address, indicating potential credential theft and session hijacking.
Detects anomalous, high-volume file access by a single process, followed by local file compression or archiving, and subsequent outbound data transfer to a non-internal IP address. This pattern is characteristic of automated agents (like those used in the GTG-1002/Anthropic AI-orchestrated campaigns) performing rapid data triage and staging for exfiltration.
Detects Node.js or ProfileQuickHost processes performing file operations (read, write, rename) on sensitive browser directories, such as 'Local Extension Settings' or wallet-related folders. This pattern is commonly observed in credential-stealing malware attempting to extract browser-stored secrets or cryptocurrency wallet data.
Detects unauthorized processes (excluding firefox.exe and explorer.exe) accessing or modifying sensitive Firefox browser profile files, specifically places.sqlite (history/bookmarks) and cookies.sqlite (session cookies). This activity is often indicative of credential or session hijacking attempts by malware or malicious scripts.
Detects unauthorized processes attempting to access Discord's local storage (LevelDB) where authentication tokens are stored, followed by a network request to the Discord API user validation endpoint, which is a pattern indicative of token extraction and liveness testing by malware.
This rule detects the use of the macOS 'security' utility to unlock the login keychain and modify its access-control list, a technique observed in PamStealer malware to facilitate subsequent unauthorized access to the keychain database.
This rule detects non-standard processes attempting to access sensitive web browser files (e.g., 'Login Data', 'Cookies', 'key4.db') that are typically only accessed by legitimate web browsers. It excludes known browser processes and trusted security or sync applications to identify potential credential theft attempts.
Detects non-browser processes performing file operations (create, modify, rename) on known web browser credential storage, configuration files, and cryptocurrency wallet artifacts in a short duration. This behavior is indicative of credential harvesting or information stealing activity often associated with malware like Vidar, which stages these sensitive files for exfiltration.
Detects non-browser processes performing file operations (create, modify, rename) on known web browser credential storage, configuration files, and cryptocurrency wallet artifacts in a short duration. This behavior is indicative of credential harvesting or information stealing activity often associated with malware like Vidar, which stages these sensitive files for exfiltration.
Detects non-browser processes performing file operations (create, modify, rename) on known web browser credential storage, configuration files, and cryptocurrency wallet artifacts in a short duration. This behavior is indicative of credential harvesting or information stealing activity often associated with malware like Vidar, which stages these sensitive files for exfiltration.
Detects the loading of the 'WbElevation.dll' module associated with SectopRAT, followed by suspicious access to browser, email, or cryptocurrency wallet credential stores within a 10-minute window on the same device.
Detects the loading of the 'WbElevation.dll' module associated with SectopRAT, followed by suspicious access to browser, email, or cryptocurrency wallet credential stores within a 10-minute window on the same device.
This rule detects potential SectopRAT loader activity by identifying non-.NET-native processes accessing 'pool.db' in the ProgramData directory followed by the immediate loading of .NET CLR libraries (clr.dll or mscoreei.dll). This pattern is indicative of reflective loading of a decrypted .NET payload into memory within a target process.
Detects potential activity related to the SectopRAT loader by monitoring the execution sequence of 'ReportDump.exe'. The rule identifies the side-loading of a specific DLL ('sdkcra.dll') followed by the process reading a known configuration or database file ('pool.db') within a short time window, which is indicative of the loader's secondary decryption phase.
Detects potential activity related to the SectopRAT loader by monitoring the execution sequence of 'ReportDump.exe'. The rule identifies the side-loading of a specific DLL ('sdkcra.dll') followed by the process reading a known configuration or database file ('pool.db') within a short time window, which is indicative of the loader's secondary decryption phase.
Detects potential activity related to the SectopRAT loader by monitoring the execution sequence of 'ReportDump.exe'. The rule identifies the side-loading of a specific DLL ('sdkcra.dll') followed by the process reading a known configuration or database file ('pool.db') within a short time window, which is indicative of the loader's secondary decryption phase.
Detects anomalous process activity that reads sensitive developer secrets (such as Anthropic, OpenAI, Gemini, or Hugging Face API keys) from common configuration or environment files, followed by an outbound network connection to a non-private IP address. This behavior is consistent with credential harvesting and exfiltration tactics used in supply chain attacks to hijack AI services.
Detects anomalous, high-volume file access by a single process, followed by local file compression or archiving, and subsequent outbound data transfer to a non-internal IP address. This pattern is characteristic of automated agents (like those used in the GTG-1002/Anthropic AI-orchestrated campaigns) performing rapid data triage and staging for exfiltration.
Detects instances of common interpreter processes, specifically node.exe or python variants, accessing known cryptocurrency wallet files or mnemonic storage files on a Windows system. This behavior is highly indicative of malicious activity where an attacker uses lightweight script-based tools to perform credential harvesting or theft of cryptocurrency assets.
This rule detects potential credential theft by correlating access to local git credential files (e.g., .git-credentials, .netrc) followed by a git push event from the same user account within 24 hours. The rule flags these pushes if they originate from an unrecognized host or an IP address outside of the trusted internal network range.
Detects the use of legitimate data transfer utilities (s5cmd, rclone) configured to exfiltrate sensitive document types (.doc, .pdf, .sql, etc.) to S3-compatible cloud storage. The rule correlates this activity with recent access to files containing sensitive credentials or configuration data, a pattern observed in double-extortion campaigns.
Page 13 of 76