Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects potential misuse of AI agentic frameworks (e.g., Semantic Kernel) where the host process executes destructive commands (e.g., file deletion, wiping, shadow copy deletion) or performs large-scale file deletion, which may indicate unauthorized tool invocation or compromised AI agents.
Detects persistence and execution abuse within hidden IDE/AI assistant workspace directories (e.g., .vscode, .cursor, .claude). The rule monitors for the creation or modification of configuration files used for automated task execution and identifies process execution patterns originating from these directories that invoke downloaders (curl, wget) or decoders (base64) to fetch or execute payloads.
This rule detects the creation, modification, or renaming of .lnk shortcut files within the Windows Startup folders (both current user and roaming appdata). It then correlates this activity with the execution of AutoIt3.exe or processes identified as 'AutoIt' that are running scripts with .a3x or .au3 extensions, which are commonly used for persistence and execution of malicious automation scripts.
Detects persistence modification via Registry Run/RunOnce keys or the addition of suspicious files to the Windows Startup folder. The rule specifically monitors for registry values pointing to common volatile directories, script extensions, or suspicious command-line interpreters. It also flags new executables or scripts being written directly into startup directory paths. This rule covers both direct registry manipulation and startup folder placement, often associated with malware or adversary persistence mechanisms.
Detects unauthorized processes attempting to read or modify Discord's local storage leveldb files, which store user authentication tokens, and correlates this with suspicious network activity to Discord's API or the presence of token-stealing keywords in command-line arguments.
This rule detects suspicious activity originating from the PaperCut 'pc-app.exe' process. It monitors for the creation or execution of a file named 'mscopilot.exe' within the non-standard directory 'C:\Microsoft.Office365\'. The rule matches based on a known malicious SHA1 hash or the specific file-write-then-execute behavior within a one-hour window, indicating potential AdaptixC2 loader activity.
Detects potential poisoning of an AI agent's RAG knowledge base or vector store through suspicious bulk ingestion or injection of imperative instruction-style content. The rule correlates these ingestion events with subsequent anomalous tool usage patterns by the same agent session, indicating a possible prompt injection or data manipulation attack aimed at altering agent behavior.
Detects potential poisoning of an AI agent's RAG knowledge base or vector store through suspicious bulk ingestion or injection of imperative instruction-style content. The rule correlates these ingestion events with subsequent anomalous tool usage patterns by the same agent session, indicating a possible prompt injection or data manipulation attack aimed at altering agent behavior.
Detects unauthorized processes attempting to read or copy sensitive browser data files (e.g., Cookies, Login Data, Local Storage) often targeted by info-stealing malware such as Vidar, LummaC2, or RedLine. The rule filters out legitimate browser-related processes and adds security context by requiring evidence of unsigned code, execution from suspicious locations (Temp/Downloads), or associated outbound network activity to reduce false positives.
Detects unauthorized processes attempting to read or copy sensitive browser data files (e.g., Cookies, Login Data, Local Storage) often targeted by info-stealing malware such as Vidar, LummaC2, or RedLine. The rule filters out legitimate browser-related processes and adds security context by requiring evidence of unsigned code, execution from suspicious locations (Temp/Downloads), or associated outbound network activity to reduce false positives.
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 potentially malicious activity involving LNK files being modified, created, or renamed in common locations (Desktop, Quick Launch, Start Menu) correlated with the creation of files ending in '.backup' within 30 minutes. It also independently detects the presence or execution of 'VLCAssistant.exe', which may indicate unauthorized launcher activity or masquerading.
Detects a suspicious sequence of events where a process modifies a Windows Registry Run key to ensure execution at logon, followed shortly by the same executable file being copied into the current user's startup folder. This pattern is often used by malware to establish persistence.
Detects patterns associated with the TeamFiltration backdoor module, specifically unauthorized OneDrive/SharePoint file modifications occurring shortly after an authentication from a previously unseen device/IP. The rule flags file changes (modifications, deletions, or renames) involving potential executable or script extensions, or those performed by the OneDrive SyncEngine app from a new source.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects unauthorized or suspicious modifications to AI model artifacts, including fine-tuned weights and LoRA adapters. This rule identifies tampering attempts via unsigned uploads, bypassing CI/CD production deployment approvals, or mismatches in hash/checksum compared to known-good versions.
Detects potential poisoning of an AI agent's RAG knowledge base or vector store through suspicious bulk ingestion or injection of imperative instruction-style content. The rule correlates these ingestion events with subsequent anomalous tool usage patterns by the same agent session, indicating a possible prompt injection or data manipulation attack aimed at altering agent behavior.
Detects malicious JavaScript injection on e-commerce checkout or payment pages designed to harvest sensitive payment card information (PAN, CVV, expiry) and exfiltrate the stolen data to an external endpoint. This behavior is indicative of a digital credit card skimmer (e.g., Magecart style) deployed following a compromise of the web application.
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