Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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 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 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 modifications to Group Policy Objects (GPOs) that are either explicitly marked as malicious based on known indicators (GUIDs or 'PAYLOAD' strings) or involve unauthorized modifications to domain-root or organizational unit GPO links. It monitors Active Directory security event logs for object changes.
This rule detects modifications to Group Policy Objects (GPOs) that are either explicitly marked as malicious based on known indicators (GUIDs or 'PAYLOAD' strings) or involve unauthorized modifications to domain-root or organizational unit GPO links. It monitors Active Directory security event logs for object changes.
Detects suspicious file create or write operations within the domain SYSVOL Policies directory. This pattern is commonly associated with attackers attempting to deploy malicious payloads, such as ransom notes or configuration changes, via Group Policy Objects (GPO). The rule monitors Windows Event ID 4663 to identify unauthorized modification attempts by non-system accounts.
This rule detects potential execution of Vidar Stealer by identifying specific diagnostic loader strings (e.g., 'Loader: write failed') within file modification and process execution events. The presence of these strings in file metadata or command-line arguments indicates stage-specific activity related to the malware's loading process.
Detects PowerShell processes using the .NET System.IO.File methods (SetLastWriteTime, SetCreationTime, SetLastAccessTime) to modify file timestamps, a common anti-forensic technique to obfuscate file creation or modification times.
This rule detects potential Training Data Poisoning (AML.T0020) by monitoring for anomalous activities in ML data pipelines. It triggers when ingestion jobs utilize untrusted sources, unregistered source paths, unauthorized accounts write to datasets, or when significant data volume bursts or schema/checksum drifts occur immediately prior to a scheduled training run.
Detects potential Business Email Compromise (BEC) fraud involving AI voice-cloning. The rule correlates external or unrecognized Microsoft Teams activity (calls/chats) directed at finance users with high-value financial transactions or vendor bank detail updates occurring on the same day, specifically when these transactions lack evidence of legitimate out-of-band or second-approver verification.
Detects bulk insertions or content updates into vector databases, document stores, or knowledge bases that contain suspected instruction-override, jailbreak, or role-hijacking phrases, particularly when performed by unauthenticated or unknown identities.
Detects bulk insertions or content updates into vector databases, document stores, or knowledge bases that contain suspected instruction-override, jailbreak, or role-hijacking phrases, particularly when performed by unauthenticated or unknown identities.
Detects unauthorized modifications to AI agent configuration files, including system prompts, tool allow-lists, and MCP server registration files. Such changes are often performed by processes outside of approved configuration management or infrastructure-as-code pipelines, potentially allowing for persistent manipulation of AI model behavior and capabilities.
Detects bulk insertions or content updates into vector databases, document stores, or knowledge bases that contain suspected instruction-override, jailbreak, or role-hijacking phrases, particularly when performed by unauthenticated or unknown identities.
Detects anomalous activity from IDEs or AI coding assistant extension host processes (e.g., VS Code, Cursor, JetBrains). The rule alerts when these processes spawn children that access sensitive local credential files, perform network connections to non-standard/unexpected domains, or modify critical CI/CD build script configuration files, which is indicative of a supply chain compromise within the developer environment.
Detects potential runtime polymorphic malware that modifies its own executable or script content while simultaneously communicating with generative AI or LLM API endpoints. This behavior indicates an adversary using LLMs to regenerate obfuscated code or mutate malware signatures dynamically at runtime.
Detects various persistence mechanisms used by the CARBONATO botnet on Linux systems, including registration of cron jobs, systemd services/timers, and the usage of 'chattr +i' to make persistence files immutable.
Detects various persistence mechanisms used by the CARBONATO botnet on Linux systems, including registration of cron jobs, systemd services/timers, and the usage of 'chattr +i' to make persistence files immutable.
Detects various persistence mechanisms used by the CARBONATO botnet on Linux systems, including registration of cron jobs, systemd services/timers, and the usage of 'chattr +i' to make persistence files immutable.
Detects activity associated with the CARBONATO botnet utilizing the GH0ST RAT agent to locate, stage, and exfiltrate sensitive AI API keys. The detection monitors for file write operations within specific hidden directories ('/root/.hermes/loot/') and the use of malicious command-line patterns or process context associated with Hermes Agent or GH0ST malware to search for configuration files (e.g., .env) containing sensitive API credentials.
Detects activity associated with the CARBONATO botnet utilizing the GH0ST RAT agent to locate, stage, and exfiltrate sensitive AI API keys. The detection monitors for file write operations within specific hidden directories ('/root/.hermes/loot/') and the use of malicious command-line patterns or process context associated with Hermes Agent or GH0ST malware to search for configuration files (e.g., .env) containing sensitive API credentials.
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