Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
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 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.
Detects anomalous AI-assisted activity where multiple processes (specifically AI developer tools) appear to be chaining operations by writing to and reading from shared local directories (e.g., 'handoff', 'artifacts') followed by network requests to known AI service APIs. This pattern may indicate automated task chaining or unauthorized exfiltration of context/code to AI platforms.
Detects a multi-stage infection chain associated with RedFlick, involving the delivery of VHDX files or PDF attachments. The rule correlates malicious activities such as SSH-based tool download (using PermitLocalCommand), PowerShell-based PDF decoding/extraction, PowerShell VHDX disk mounting, and silent MSI installation from remote URLs. It monitors for these activities within user-writable directories like Downloads or Temp.
Detects a multi-stage infection chain associated with RedFlick, involving the delivery of VHDX files or PDF attachments. The rule correlates malicious activities such as SSH-based tool download (using PermitLocalCommand), PowerShell-based PDF decoding/extraction, PowerShell VHDX disk mounting, and silent MSI installation from remote URLs. It monitors for these activities within user-writable directories like Downloads or Temp.
This rule detects potential persistence via scheduled tasks that masquerade as legitimate network components (e.g., 'System Health Monitor'), as well as abnormal use of WebDav via rundll32.exe or net.exe in the context of the Star Blizzard (COLDRIVER) campaign. It monitors for task creation/updates, schtasks command lines, registry artifacts related to task scheduling, and suspicious WebDav network utility usage, while excluding common security tool processes.
Detects msiexec.exe being executed with a remote HTTP(S) URL argument that includes a token-based query parameter. This pattern is characteristic of ClickFix-style social engineering attacks where a user is tricked into manually executing a command to download and run a malicious MSI file.
Detects AI agent tooling (e.g., Copilot, Cursor, Ollama) that makes outbound network calls to known AI model providers followed by the execution of suspicious shell commands within a short duration. The rule flags high-severity activities such as download-cradles, credential reconnaissance, LOLBin usage, and persistence mechanisms. Informational alerts are generated for standard CLI activity within the same timeframe.
Detects instances where a browser or agentic-browser process initiates a file download and subsequently executes that same file within a five-minute window, without the intervention of a user-driven process like explorer.exe. This pattern is indicative of automated 'agentic' browser activity, potentially signifying hijacked web instructions or malicious automated tool execution flows.
This rule detects processes command lines containing specific strings ('EmbedHtml', 'EmbedHtml::GetUrl', 'GetUrl()') associated with suspicious NSIS (Nullsoft Scriptable Install System) script behavior often linked to the OpenSUpdater malware downloader, which uses these calls to fetch additional malicious payloads.
Detects the execution of PowerShell or PowerShell Core (pwsh) triggered by common Windows shell applications (explorer, mshta, wscript, cscript) where the command line contains indicators associated with the downloading of VelvetCake stager files, including specific GitHub raw content URLs or AppData paths referencing known stager filenames.
Detects the VelvetCake malware installation and operational pattern, which utilizes a scheduled task named 'OneDriveUpdateScheduler' to execute PowerShell scripts ('update1.ps1' or 'update2.ps1'). These scripts periodically retrieve modules or payloads from external infrastructure, specifically hardcoded C2 IPs or a dedicated GitHub repository.
Detects instances where a command-line utility or scripting interpreter references a 'SKILL.md' configuration file, typically associated with agent-skill payloads, followed shortly by network-based file download activity. This behavior is indicative of a secondary payload retrieval triggered by an adversary-controlled or poisoned configuration file.
This rule monitors for network connections to known malicious domains and IP addresses, as well as the presence or execution of files with specific SHA256 hashes known to be associated with threat activity. The indicators focus on Vercel-hosted domains and specific file hashes linked to recent campaign activity.
This rule detects the suspicious combination of PowerShell scripts executing download commands (Invoke-WebRequest, etc.) while initiated by scripting hosts (wscript.exe, cscript.exe), followed by activity associated with remote support tools (LogMeIn, ConnectWise, etc.). It also identifies network connections associated with these remote management platforms, suggesting potential unauthorized remote access or tool staging.
This rule detects a suspicious process chain where wscript.exe or cscript.exe (executing a VBScript file) initiates a PowerShell process with arguments typically used for downloading external content (e.g., Invoke-WebRequest, Net.WebClient), followed by subsequent actions involving remote management tools or installation packages (e.g., MSI files, ConnectWise, ScreenConnect, GoToResolve). This behavior is characteristic of initial access or secondary payload delivery via malicious scripts.
This rule detects the Kothamine dropper chain by correlating network connections or command-line activity fetching payloads from a specific GitHub repository ('cphc811-ui/new-tails') with the subsequent creation of associated malicious files (e.g., tailscale-related binaries, injector DLLs) on the same host within a one-hour window.
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