Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
1,310 detections
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Detects potential account compromise by identifying a specific sequence of behavior: reconnaissance of sensitive users (HR/Payroll) via Microsoft Graph API calls, followed by the creation of inbox rules (e.g., forwarding) by the same user account within a 12-hour window.
Detects internal spearphishing emails containing subjects and body text commonly associated with payroll fraud, specifically attempts to solicit changes to employee direct deposit accounts by requesting voided checks or information about upcoming payments.
Detects reconnaissance activity targeting sensitive personnel groups (payroll, HR, finance) via Microsoft Graph API. The rule identifies suspicious URI patterns combined with the use of the 'axios' User-Agent library, potentially indicating automated enumeration by an adversary to map high-value targets for future business email compromise or credential theft.
Detects network activity associated with the FlowerStorm/Storm-1167 phishing campaign. The rule monitors for credential exfiltration to a specific malicious URI (e.g., /google.php) containing login parameters, as well as the delivery of spoofed Microsoft sign-in pages from the associated phishing infrastructure.
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 a potential vishing attack by correlating suspicious IT helpdesk requests (e.g., password or MFA reset without manager approval or verified callback) with subsequent sensitive Entra ID identity changes (e.g., self-service password resets, MFA device registration, or new device registration) occurring for the same user within a short time window. This activity is indicative of an attacker attempting to social-engineer helpdesk staff to bypass authentication controls and compromise a user account.
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 a sequence of events where a user's MFA configuration is modified (typically via a help-desk impersonation vishing attack) followed closely by a successful sign-in from a new IP address. This pattern is indicative of attackers hijacking sessions or stealing credentials by coercing victims into resetting MFA settings.
This rule detects network connections or DNS queries made to suspicious domains that utilize MFA or credential-themed keywords (e.g., 'mfa', 'sso', 'passkey', 'register'). These domains are often used in phishing campaigns to impersonate authentication portals and harvest user credentials or MFA tokens.
Detects a multi-stage attack pattern against publicly exposed AI service endpoints. The detection correlates: (1) Enumeration of sensitive AI/MCP-related API endpoints (minimum 3 distinct hits) in a 10-minute window; (2) Subsequent POST requests to AI service endpoints within 15 minutes of the enumeration; and (3) A spike of 5 or more server errors (HTTP 5xx) within 10 minutes following the POST activity. This pattern suggests automated reconnaissance followed by attempted exploitation of exposed AI model interfaces or orchestration services. Covers T1595.002, T1190
Detects a multi-stage attack pattern against publicly exposed AI service endpoints. The detection correlates: (1) Enumeration of sensitive AI/MCP-related API endpoints (minimum 3 distinct hits) in a 10-minute window; (2) Subsequent POST requests to AI service endpoints within 15 minutes of the enumeration; and (3) A spike of 5 or more server errors (HTTP 5xx) within 10 minutes following the POST activity. This pattern suggests automated reconnaissance followed by attempted exploitation of exposed AI model interfaces or orchestration services. Covers T1595.002, T1190
Detects potential embedding inversion or extraction attempts by identifying callers exhibiting abnormally high volumes of similarity or vector search queries against RAG-enabled vector stores or cognitive search APIs within a short timeframe.
Detects potential embedding inversion or extraction attempts by identifying callers exhibiting abnormally high volumes of similarity or vector search queries against RAG-enabled vector stores or cognitive search APIs within a short timeframe.
Detects network requests originating from mobile device user-agents to suspected phishing or OAuth-consent URLs. This rule identifies patterns common in 'quishing' (QR-code phishing) campaigns where a mobile device is used to interact with a malicious redirect chain or credential-harvesting page. It excludes known legitimate OAuth authorization flows to reduce noise.
Detects a multi-stage Telephone-Oriented Attack Delivery (TOAD) campaign. The first stage identifies phishing emails containing billing or security-themed subjects and phone numbers without links or attachments to evade automated analysis. The second stage monitors for the subsequent installation or execution of common remote-support tools (e.g., AnyDesk, TeamViewer, ScreenConnect). A final correlative rule detects these events occurring sequentially on the same host, indicating a high-confidence indicator of a successful callback phishing social-engineering attempt.
Detects a suspicious pattern of activity where a single source IP performs network reconnaissance followed by rapid-fire failed authentication attempts against multiple distinct hosts within a short time window. This behavior is indicative of automated, agentic AI-driven offensive tooling rather than manual activity.
Detects a suspicious pattern of activity where a single source IP performs network reconnaissance followed by rapid-fire failed authentication attempts against multiple distinct hosts within a short time window. This behavior is indicative of automated, agentic AI-driven offensive tooling rather than manual activity.
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 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 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 anomalous, high-frequency requests from a single source IP targeting sensitive NetScaler/Citrix ADC Gateway paths. This behavior is indicative of automated reconnaissance or vulnerability scanning often preceding exploitation attempts.
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