Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects forced pushes to the main branch of a GitHub repository where a specific file named 'indexe.cjs' is added or a specific commit ID (d1d55e13) is pushed. This behavior is indicative of a supply chain compromise where an attacker rewrites git history to inject malicious loaders or backdoors into the source code.
Detects ROOFDECK backdoor persistence via macOS Launch Agents, tightened to require the implant's distinguishing masquerade artifacts (fake Apple-service drop paths, --type=renderer flag, loginwindow.plist impersonation, or the literal 'persist add|remove|check' CLI syntax) rather than generic LaunchAgents plist creation or launchctl load activity, which legitimate macOS software triggers constantly.
Monitors GitHub Audit Logs for git pushes or workflow changes occurring after the September 16, 2026, reactivation window that reference the actions-cool/issues-helper or actions-cool/maintain-one-comment repositories. The rule excludes routine automated maintenance activity from trusted bots and common maintenance commit messages, aiming to identify unauthorized attempts to utilize or modify potentially compromised GitHub actions.
Monitors GitHub Audit Logs for git pushes or workflow changes occurring after the September 16, 2026, reactivation window that reference the actions-cool/issues-helper or actions-cool/maintain-one-comment repositories. The rule excludes routine automated maintenance activity from trusted bots and common maintenance commit messages, aiming to identify unauthorized attempts to utilize or modify potentially compromised GitHub actions.
Monitors GitHub Audit Logs for git pushes or workflow changes occurring after the September 16, 2026, reactivation window that reference the actions-cool/issues-helper or actions-cool/maintain-one-comment repositories. The rule excludes routine automated maintenance activity from trusted bots and common maintenance commit messages, aiming to identify unauthorized attempts to utilize or modify potentially compromised GitHub actions.
Monitors GitHub Audit Logs for git pushes or workflow changes occurring after the September 16, 2026, reactivation window that reference the actions-cool/issues-helper or actions-cool/maintain-one-comment repositories. The rule excludes routine automated maintenance activity from trusted bots and common maintenance commit messages, aiming to identify unauthorized attempts to utilize or modify potentially compromised GitHub actions.
Monitors GitHub Audit Logs for git pushes or workflow changes occurring after the September 16, 2026, reactivation window that reference the actions-cool/issues-helper or actions-cool/maintain-one-comment repositories. The rule excludes routine automated maintenance activity from trusted bots and common maintenance commit messages, aiming to identify unauthorized attempts to utilize or modify potentially compromised GitHub actions.
This rule detects GitHub Actions workflows that explicitly reference specific 'actions-cool' GitHub actions using a known-bad mutable version tag ('@v2.2.1'). This tag corresponds to malicious code introduced into these repositories, allowing for potential CI/CD pipeline poisoning and supply chain compromise.
This rule detects GitHub Actions workflows that explicitly reference specific 'actions-cool' GitHub actions using a known-bad mutable version tag ('@v2.2.1'). This tag corresponds to malicious code introduced into these repositories, allowing for potential CI/CD pipeline poisoning and supply chain compromise.
This rule detects GitHub Actions workflows that explicitly reference specific 'actions-cool' GitHub actions using a known-bad mutable version tag ('@v2.2.1'). This tag corresponds to malicious code introduced into these repositories, allowing for potential CI/CD pipeline poisoning and supply chain compromise.
This rule detects GitHub Actions workflows that explicitly reference specific 'actions-cool' GitHub actions using a known-bad mutable version tag ('@v2.2.1'). This tag corresponds to malicious code introduced into these repositories, allowing for potential CI/CD pipeline poisoning and supply chain compromise.
This rule detects a correlation between the creation or modification of a specific file name ('nloemfbihmhm') and the subsequent execution of processes from the 'Temp' directory involving related filenames ('kojuyn.ini', 'ogftogcyiblzjccmcbnw.exe', or 'nloemfbihmhm') within a 5-minute window. This behavior is indicative of a multi-stage execution chain, often used by malware to drop and execute secondary payloads.
This rule detects the installation or presence of ten identified malicious npm packages associated with the indexed-btree/sorted-btree typosquatting campaign. It monitors command-line activity for 'npm install' commands, package name resolution, and entries within 'package.json' files.
This rule detects potential credential theft by monitoring for file access (creation, modification, or renaming) to common web browser sensitive files (Login Data or Cookies) followed by an outbound network connection from the same process within a 10-minute window. This behavior often indicates an adversary attempting to exfiltrate browser-stored credentials or session cookies.
Detects the installation or service creation of 'gdrv.sys', a known vulnerable GIGABYTE driver often abused in Bring Your Own Vulnerable Driver (BYOVD) attacks to escalate privileges.
This rule detects the creation of Git branches or tags using names that match the 40-character or 64-character hexadecimal format of Git commit hashes. This technique, identified in the context of Plugin4Shell, is used to spoof pinned-commit references, potentially misleading automated systems or developers into using malicious code versions.
Detects potential model weight poisoning or unauthorized integrity compromise by identifying training jobs launched by non-authorized users, model file modifications in production paths that bypass CI/CD approval gates, or integrity mismatches between deployed model artifacts and their corresponding approved training outputs.
Detects potential model weight poisoning or unauthorized integrity compromise by identifying training jobs launched by non-authorized users, model file modifications in production paths that bypass CI/CD approval gates, or integrity mismatches between deployed model artifacts and their corresponding approved training outputs.
Detects a rapid, multi-stage intrusion sequence on a single host involving discovery, lateral movement, and high-volume file modifications. The rule identifies anomalous behavior where diverse reconnaissance and lateral movement commands are executed within a compressed timeframe, characteristic of automated or LLM-driven orchestration of ransomware operations.
Detects instances where an AI agent invokes sensitive administrative or system tools (shell execution, file writing, network communication, or email sending) shortly after ingesting untrusted external content, in the absence of explicit, logged user instructions for those specific actions. This behavior is a primary indicator of indirect prompt injection attacks aimed at achieving command execution or C2-like behavior.
Detects activity associated with the CARBONATO botnet, specifically the weaponization of the Hermes Agent framework. The detection monitors for the overwrite of the SOUL.md configuration file to inject a malicious GH0ST persona, execution of the hermes-agent binary from known malicious paths, and the presence of the CARBONATO_API_KEY environment variable or command-line parameter.
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