Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
40,063 detections
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Detects outbound HTTP requests to identified scam infrastructure (e.g., herokuapp domains) containing platform-specific query parameters (win or mac). This behavior is indicative of a user interacting with a malicious site that is delivering an OS-tailored locker or malware payload.
Detects the use of PowerShell cmdlets (Add-MpPreference or Set-MpPreference) to modify Microsoft Defender exclusions (paths, processes, or extensions). This behavior is often associated with adversaries attempting to evade security detection by excluding malicious files or processes from being scanned by Microsoft Defender.
Detects Node.js or Python processes exhibiting suspicious behavior consistent with information-stealing malware (such as BeaverTail or InvisibleFerret). The rule monitors for these processes accessing sensitive files, including browser credentials, cookies, browser extension wallet settings, cryptocurrency wallet files, and various identification document image types, followed by the execution of archiving utilities (e.g., zip, rar, 7z) to stage the collected data for exfiltration.
Detects the execution of known remote-access or screen-sharing software (e.g., TeamViewer, AnyDesk, RDP) within 30 minutes of launching common video conferencing applications (e.g., Zoom, Teams, Google Meet). This pattern is consistent with an adversary remotely assisting an unauthorized user during a live coding interview or similar assessment process.
Detects anomalous patterns associated with potential WordPress comment moderation bypass. The rule identifies suspicious rapid sequences of comment submissions, use of unapproved comment preview endpoints with moderation hashes, or reuse of approved-commenter cookies. These behaviors can be indicative of an attacker attempting to preview or force-render stored XSS payloads to administrators or visitors before the comment has been officially approved.
Detects a specific behavioral chain associated with the CHOSEN BRICK implant. This rule identifies when a suspected malicious process writes image/screenshot files (.png, .jpg, .bmp) to disk, followed by a network connection to the Telegram Bot API within a 15-minute window, suggesting potential data exfiltration.
Detects the creation of multiple suspicious scheduled tasks using schtasks.exe initiated by scripting engines (wscript.exe, cscript.exe). The rule filters for specific task names associated with known malicious patterns and XML definition files located in a specific directory path, identifying potential automated persistence or malware installation behavior.
Detects a chained sequence of suspicious activity initiated by agentic AI development tools (e.g., Claude Code, Aider, AutoGen). The rule identifies the execution of these tools followed by local reconnaissance commands, lateral movement attempts, and outbound network connections originating from the same host, indicating potential automated exploitation or credential abuse.
Detects anomalous, chained execution patterns initiated by AI agents integrated with Model Context Protocol (MCP) servers. The rule identifies a multi-stage sequence involving the invocation of browser automation tools followed by shell execution and subsequent outbound network activity, potentially indicating an AI-driven attack chaining filesystem, shell, or cloud-API access in an unauthorized manner.
Detects outbound HTTPS connections to major generative AI inference endpoints originating from non-browser, non-development tool processes at regular intervals. This activity is indicative of beaconing behavior, where malware (specifically families like LAMEHUG) leverages LLM APIs for dynamic command generation, payload obfuscation, or C2 instruction retrieval, bypassing traditional security controls.
Detects anomalous remote access patterns on a single endpoint, specifically involving AnyDesk or TeamViewer sessions originating from multiple distinct geographical locations combined with usage by multiple distinct user accounts. This pattern is consistent with DPRK IT worker fraud (Wagemole) involving shared laptop farms.
Detects coordinated activity patterns across multiple distinct tenant environments within a one-hour window. The rule looks for shared infrastructure (C2 domains), malicious tool hashes, or specific agentic-CLI command line signatures (e.g., related to orchestration, reconnaissance, or exfiltration) that suggest an AI-orchestrated actor or automated attack campaign affecting multiple organizations simultaneously.
Detects anomalous child process spawning from common web browsers or productivity applications (e.g., Office, Acrobat) on hosts that have recently communicated with known LLM or code-generation APIs. This behavioral correlation is intended to identify the potential execution of AI-generated exploit code.
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.
Detects the stopping or modification of critical security and backup services on Windows endpoints. This behavior often indicates an attempt by an adversary to impair defensive capabilities or disable logging to facilitate malicious activity.
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.
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.
This rule detects the execution of known lateral movement tools, including Impacket suite components and NetExec (formerly CrackMapExec), by monitoring process command lines. It specifically flags tools such as psexec.py, wmiexec.py, and secretsdump.py, which are frequently used for remote execution and credential dumping. The rule also joins these process events with network events targeting SMB port 445 to correlate tool usage with potential lateral movement activity.
Detects attempts to disable or tamper with Microsoft Windows Defender via registry modifications or malicious PowerShell/reg.exe commands, specifically targeting security settings like DisableAntiSpyware, DisableRealtimeMonitoring, and exclusion paths.
Detects an suspicious progression of activities originating from identified AI assistant/agent processes. The rule monitors for a chain of behaviors occurring within a 15-minute window: execution of an AI agent/tool, followed by host discovery (network/system enumeration), access to sensitive local credential files, archival of data, and outbound network communication to common file-sharing/exfiltration platforms.
Detects an suspicious progression of activities originating from identified AI assistant/agent processes. The rule monitors for a chain of behaviors occurring within a 15-minute window: execution of an AI agent/tool, followed by host discovery (network/system enumeration), access to sensitive local credential files, archival of data, and outbound network communication to common file-sharing/exfiltration platforms.
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