Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects potential data exfiltration by monitoring corporate user activities involving consumer generative AI services (e.g., ChatGPT, Gemini, Claude). It identifies suspicious patterns such as large volume submissions, repeated chunked prompts, and the presence of sensitive content (e.g., PII markers, classification labels) or obfuscated payloads (base64/rot13) that suggest an attempt to bypass DLP or detection controls.
This rule detects anomalous, high-volume activity from a single user account interacting with known public generative AI platforms (e.g., ChatGPT, Copilot, Claude). It uses a threshold-based approach on CloudAppEvents logs to identify potential reconnaissance or bulk information gathering activities which could signify unauthorized use or automated data scraping via AI interfaces.
Detects a suspicious sequence of events where a host performs multiple reconnaissance-style probes (health checks, documentation, or OpenAPI definitions) followed within 10 minutes by access to sensitive artifact-related URLs on the same remote host. This pattern is indicative of an attacker profiling a target server for metadata and then proceeding to exfiltrate files or sensitive artifacts.
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.
This rule detects potential quishing (QR code phishing) attempts by identifying emails with one or more image attachments and minimal URL content, followed by a risky user authentication event within a 3-hour window. This behavior is indicative of an attacker attempting to harvest credentials via a malicious QR code that redirects the user to a phishing site.
This rule detects inbound network traffic directed at NetScaler ADC or Gateway devices originating from IP addresses flagged as malicious by threat intelligence feeds. It correlates activity logs with active threat intelligence data (categorized as Malware, C2, Exploit, Scanning, or related to known NetScaler/Citrix vulnerabilities) to identify potential reconnaissance or exploitation attempts.
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 external reconnaissance activity targeting AI backend services. Adversaries scan for known AI infrastructure ports (such as TensorFlow Serving, Triton, or Ray) and common API paths (e.g., /v1/models, /predict) to identify potential targets. The detection utilizes proxy logs to correlate these suspicious network attributes, high request rates, or known scanning user agents.
Detects potential vishing activity associated with the Sauron Loader threat. The rule identifies a pattern of a mailbox receiving a high volume of inbound emails (spam or subscription bombs) within a short window, followed by the user launching remote assistance tools like Quick Assist or AnyDesk on the same endpoint, indicating an attacker-guided remote session.
Detects the Sauron Loader vishing chain: a mailbox hit by a high-volume, high-distinct-sender email bombing burst (raised from 20 to 50 emails/hour and now requiring 15+ distinct senders, to exclude single-sender retry loops or broken automation), followed within 2 hours by the same user launching Quick Assist or AnyDesk — consistent with an attacker posing as IT support after the flood. Also fixes a schema bug where EmailEvents was queried with TimeGenerated instead of Timestamp, and a post-join column reference bug.
Detects unauthorized GET requests to common default paths associated with AI agent developer consoles and infrastructure, such as '/playground', '/api/predict', or '/_stcore'. These requests are identified by the absence of expected 'Authorization' or 'Cookie' headers, suggesting an unauthenticated probe or discovery attempt by an external actor looking for misconfigured or exposed AI development interfaces.
Detects host and browser fingerprinting reconnaissance behavior associated with the Macfinger/ClickFix infection chain. The rule identifies multiple disparate indicators (ClickFix tracker domain, ipinfo.io geolocation lookups, and known AMOS C2 IP contact) occurring on the same device within a 15-minute window, effectively reducing noise from isolated or benign network lookups.
Detects host and browser fingerprinting reconnaissance behavior associated with the Macfinger/ClickFix infection chain. The rule identifies multiple disparate indicators (ClickFix tracker domain, ipinfo.io geolocation lookups, and known AMOS C2 IP contact) occurring on the same device within a 15-minute window, effectively reducing noise from isolated or benign network lookups.
Detects host and browser fingerprinting reconnaissance behavior associated with the Macfinger/ClickFix infection chain. The rule identifies multiple disparate indicators (ClickFix tracker domain, ipinfo.io geolocation lookups, and known AMOS C2 IP contact) occurring on the same device within a 15-minute window, effectively reducing noise from isolated or benign network lookups.
Detects host and browser fingerprinting reconnaissance behavior associated with the Macfinger/ClickFix infection chain. The rule identifies multiple disparate indicators (ClickFix tracker domain, ipinfo.io geolocation lookups, and known AMOS C2 IP contact) occurring on the same device within a 15-minute window, effectively reducing noise from isolated or benign network lookups.
Detects anomalous, high-frequency, multi-channel probing of AI agent input surfaces. The rule identifies external actors sending instruction-injection style keywords across different communication channels (e.g., email, Teams, calendar) within a short window, which may indicate an attempt to identify and manipulate an organization's autonomous agents. Covers T1595.002
This rule detects reconnaissance activity targeting Azure App Service instances, specifically looking for attempts to access web-shell-like files, WordPress administration paths, PHP-CGI endpoints, or LangFlow code validation paths. It identifies potential Storm-3168 actor infrastructure through known IOCs or through patterns of high-volume, path-diverse HTTP requests that deviate from typical noise, helping to distinguish targeted probing from common internet background scanning.
This rule detects reconnaissance activity targeting Azure App Service instances, specifically looking for attempts to access web-shell-like files, WordPress administration paths, PHP-CGI endpoints, or LangFlow code validation paths. It identifies potential Storm-3168 actor infrastructure through known IOCs or through patterns of high-volume, path-diverse HTTP requests that deviate from typical noise, helping to distinguish targeted probing from common internet background scanning.
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