Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
9,759 detections
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Detects the use of PowerShell processes that employ command-line encoding (e.g., -enc, -EncodedCommand) in conjunction with download-related cmdlets (e.g., DownloadString, IEX, Invoke-WebRequest), or instances of PowerShell spawned as a child process of another PowerShell process. This pattern is indicative of a loader or stage-one script fetching additional malicious payloads.
Detects the anomalous behavior of a Node.js process acting as a typosquatting agent. The detection identifies a node.exe process initiating a network connection to a known malicious C2 IP and port, followed by the termination of that same process within 45 seconds. This pattern is consistent with the agent crashing due to an unhandled exception (e.g., os.userInfo() failure) after beaconing.
Detects the anomalous behavior of a Node.js process acting as a typosquatting agent. The detection identifies a node.exe process initiating a network connection to a known malicious C2 IP and port, followed by the termination of that same process within 45 seconds. This pattern is consistent with the agent crashing due to an unhandled exception (e.g., os.userInfo() failure) after beaconing.
Detects the anomalous behavior of a Node.js process acting as a typosquatting agent. The detection identifies a node.exe process initiating a network connection to a known malicious C2 IP and port, followed by the termination of that same process within 45 seconds. This pattern is consistent with the agent crashing due to an unhandled exception (e.g., os.userInfo() failure) after beaconing.
Detects unauthorized processes requesting memory access to lsass.exe with read permissions commonly associated with credential dumping techniques like Mimikatz. It identifies remote handle requests to lsass.exe, which are frequently used by tools deployed via network services or remote execution methods to extract credentials from memory.
Detects the execution of TruffleHog, an automated secret-scanning CLI tool, often utilized by threat actors to harvest sensitive credentials from local filesystems, repositories, environment variables, or CI/CD configuration files following unauthorized access.
Detects network connections to known ChatGPT Custom GPT ClickFix campaign infrastructure (IPs and URLs) as reported by Huntress. This rule covers the network_connection logsource only. Domain/DNS matching is covered by a separate dns_query rule, and file-hash matching is covered by a separate process_creation rule, as Sigma only supports one logsource per rule.
Detects network connections to known ChatGPT Custom GPT ClickFix campaign infrastructure (IPs and URLs) as reported by Huntress. This rule covers the network_connection logsource only. Domain/DNS matching is covered by a separate dns_query rule, and file-hash matching is covered by a separate process_creation rule, as Sigma only supports one logsource per rule.
Detects network connections to known ChatGPT Custom GPT ClickFix campaign infrastructure (IPs and URLs) as reported by Huntress. This rule covers the network_connection logsource only. Domain/DNS matching is covered by a separate dns_query rule, and file-hash matching is covered by a separate process_creation rule, as Sigma only supports one logsource per rule.
Detects the deletion of a specific file named 'Evernote.zip' located within the 'C:\Users\Public\' directory when executed by Windows Script Host (wscript.exe or cscript.exe). This activity is often associated with the cleanup of second-stage archives used during malicious operations.
Detects execution of shell commands (cmd, powershell) by processes associated with AvisLoader (e.g., 78324.exe, VLCAssistant.exe, auto.exe), followed by the creation or modification of files in suspicious user-writable directories (e.g., appdata, temp) within a 10-minute window, suggesting potential C2 activity and secondary payload staging.
Detects evidence of the Rapuncel infostealer attempting to compromise Chrome or Edge browsers. The rule identifies suspicious image loads into browser processes linked to known malicious hashes or the creation of specific browser decryption log files used by the malware to defeat App-Bound encryption.
Detects evidence of the Rapuncel infostealer attempting to compromise Chrome or Edge browsers. The rule identifies suspicious image loads into browser processes linked to known malicious hashes or the creation of specific browser decryption log files used by the malware to defeat App-Bound encryption.
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 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 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 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 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.
Detects a sequence of events where a potential multimedia lure (PDF, image, or media file) is created or downloaded via a web browser or communication client, followed by an AI agent (e.g., ChatGPT, Claude) accessing the file, and subsequently initiating a shell execution process (PowerShell, CMD, etc.) involving suspicious command-line patterns or references to the original file. This behavior is indicative of an AI-assisted prompt injection attack where malicious content is processed and executed through agent tooling.
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