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.
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 processes attempting to enumerate Active Directory objects (users, groups, trusts) while simultaneously establishing a network connection to Active Directory Web Services (ADWS) on port 9389. This behavior is characteristic of tools like SharpADWS, which performs AD enumeration via the ADWS protocol rather than traditional LDAP, potentially bypassing legacy detection mechanisms.
Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
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.
This rule detects potential AI prompt injection attacks delivered via email. It identifies users clicking external links directed at AI platforms (like OpenAI, Claude, or Perplexity) that contain suspicious URL parameters or instructions designed to manipulate or override the AI's standard behavior. It correlates the web request with a subsequent process execution on the host that includes suspicious keywords in the command line, suggesting an attempt to automate or exploit the AI assistant through local system interaction.
Detects potential supply chain or local configuration tampering involving the Model Context Protocol (MCP). The rule identifies modification of common MCP configuration files, followed by the execution of a tool runner (node, python, npx) that spawns a scripting shell, which subsequently initiates an unusual outbound network connection.
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 unauthorized processes reading Chromium or Firefox cookie and login stores that specifically reference Claude.ai or Anthropic domains. This rule is designed to identify infostealer malware attempting to hijack active Claude sessions by analyzing process file access and command-line arguments, filtered against known-legitimate security, sync, and development tools. The detection logic mandates corroborating evidence of suspicious execution paths or subsequent outbound network activity.
Detects two-stage phishing activity involving an initial visit to a Vercel-hosted lure page followed by the exfiltration or relay of fingerprinting and sandbox-evasion results to api.telegram.org.
This rule performs a retrospective hunt for indicators of compromise (IOCs) associated with the ClosedQuorum malware. It identifies suspicious activity by matching against known file hashes, specific filenames, and network traffic directed towards services (such as DeepSeek, OpenRouter, Mistral, and Discord) which the malware uses for C2 or data exfiltration. The detection logic aggregates results from file system, process, and network telemetry.
Detects DNS queries and TLS SNI traffic directed at known actor-controlled domains and subdomains associated with ScreenConnect remote management software usage in malicious RMM chains.
Detects DNS queries and TLS SNI traffic directed at known actor-controlled domains and subdomains associated with ScreenConnect remote management software usage in malicious RMM chains.
Detects network activity associated with the AMOS (Atomic macOS Stealer) malware, specifically identifying outbound HTTP POST requests to the '/api/t' URI path occurring on non-standard ports. The detection utilizes a threshold-based trigger to identify potential C2 communication heartbeats.
Detects anomalous, high-frequency outbound network connections to port 443 initiated by the binary 'rnpkeys.exe'. This behavior is characteristic of the Sauron Loader, which uses this masqueraded process to exfiltrate collected data, such as screenshots, via an encrypted C2 channel.
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