Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects msiexec.exe initiating network connections to or executing packages from endpoints containing 'capher.php' and token parameters. This behavior is associated with the 'ClickFix' delivery chain, where adversaries use malicious websites or scripts to trick users into downloading and executing installer files.
Detects a suspected ClickFix phishing sequence where a user navigates to a brand-impersonating GitHub organization or repository, followed by a redirection within a short timeframe to a GitHub Pages portal hosting malicious content. The rule also monitors for direct navigation to a known common lure-hosting domain.
This rule monitors for network communication with a specific known malicious IP address and URL path, as well as the execution or presence of associated malicious file hashes. Additionally, it flags emails involving specific indicators identified as potentially associated with malicious activity.
This rule monitors for network communication with a specific known malicious IP address and URL path, as well as the execution or presence of associated malicious file hashes. Additionally, it flags emails involving specific indicators identified as potentially associated with malicious activity.
This rule monitors for network communication with a specific known malicious IP address and URL path, as well as the execution or presence of associated malicious file hashes. Additionally, it flags emails involving specific indicators identified as potentially associated with malicious activity.
Detects a specific multi-stage exfiltration pattern characterized by initial reconnaissance/fingerprinting probes against sensitive service endpoints (/health, /docs, /openapi.json) followed by unauthorized access to artifact storage endpoints (/files or /file?name=) from the same device against the same host within a short timeframe.
Detects anomalous HTTP traffic directed to githubusercontent.com containing a custom, non-standard request header 'aohvcoehfg'. This pattern is associated with Kimsuky threat actor activity, specifically related to the 'GitPower' operation involving C2 communication via GitHub repositories.
Detects network activity (DNS queries, TLS SNI, and HTTP Host headers) associated with known RedFlick command-and-control (C2) infrastructure domains: etia.ca, groy.cc, and secure-dns-hub.com.
Detects the presence of self-signed TLS certificates with the common name 'JDYFJ' in the certificate subject or issuer fields. This pattern has been associated with the KV-Botnet, which has been utilized by the Volt Typhoon threat actor to obfuscate command and control traffic through compromised SOHO routers.
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.
This rule detects potential adversarial reconnaissance activities targeting AI agents via multiple communication channels (e.g., email, collaboration tools). It identifies patterns where a sender transmits messages containing common 'jailbreak' or 'instruction-override' phrases (e.g., 'ignore previous instructions', 'bypass approval') across at least three distinct channels within a 24-hour window, indicating a concerted effort to manipulate or probe agent logic.
This rule detects potential adversarial reconnaissance activities targeting AI agents via multiple communication channels (e.g., email, collaboration tools). It identifies patterns where a sender transmits messages containing common 'jailbreak' or 'instruction-override' phrases (e.g., 'ignore previous instructions', 'bypass approval') across at least three distinct channels within a 24-hour window, indicating a concerted effort to manipulate or probe agent logic.
This rule detects potential adversarial reconnaissance activities targeting AI agents via multiple communication channels (e.g., email, collaboration tools). It identifies patterns where a sender transmits messages containing common 'jailbreak' or 'instruction-override' phrases (e.g., 'ignore previous instructions', 'bypass approval') across at least three distinct channels within a 24-hour window, indicating a concerted effort to manipulate or probe agent logic.
Detects a sequence of potentially malicious activity targeting LLM-related API endpoints. The rule identifies a client IP performing rapid, distinct endpoint discovery, followed by a POST request to sensitive paths, and concluding with a spike in 5xx server-side errors, indicating potential exploitation attempts or fuzzing against Large Language Model interfaces.
Detects web servers serving significantly different content sizes to AI crawlers (e.g., ChatGPT-User, ClaudeBot) compared to human web browsers for the same URL, which is a technique used to serve benign content to automated scrapers while delivering malicious or deceptive content to human users.
Detects web servers serving significantly different content sizes to AI crawlers (e.g., ChatGPT-User, ClaudeBot) compared to human web browsers for the same URL, which is a technique used to serve benign content to automated scrapers while delivering malicious or deceptive content to human users.
Detects web servers serving significantly different content sizes to AI crawlers (e.g., ChatGPT-User, ClaudeBot) compared to human web browsers for the same URL, which is a technique used to serve benign content to automated scrapers while delivering malicious or deceptive content to human users.
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.
This rule detects potentially compromised accounts by correlating risky Entra ID sign-ins to Citrix NetScaler with subsequent VPN session establishment followed by rapid, high-volume outbound connection activity from the source IP address. This pattern is indicative of a threat actor using compromised credentials to gain access via VPN and immediately perform internal network scanning or data staging.
Detects HTTP responses containing a file named 'ClaudeDesktop.exe' served from paths containing '/artifacts/' on the 'claude.ai' host where the request referrer is not 'claude.ai' or 'anthropic.com', suggesting a malvertising or supply chain delivery attempt of a malicious installer.
This rule detects activity involving known malicious indicators of compromise (IOCs), including specific IP addresses, domains, and file hashes. It monitors network connections, file system activity, process execution, and email attachments, filtering out common security tools to reduce noise.
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