Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects scenarios where a user registers a new WebAuthn/Passkey credential and subsequently performs a sign-in from a different network or device shortly after, preceded by a sign-in from a risky or non-passkey-based method. This pattern is often associated with credential abuse or MFA registration hijacking.
Detects instances where an Entra ID application or service principal has new secrets or certificates added by a user who is not a registered owner of that application. This behavior is a common technique for establishing stealthy persistence in a cloud environment.
Detects a burst of MFA push/approval prompts (5+ in 10 minutes) to one user culminating in an eventual approval after repeated denials/timeouts — the push-bombing technique Scattered Spider and Mutant Spider pair with helpdesk vishing to exhaust remaining MFA resistance.
Correlates new OAuth application consent grants with a preceding helpdesk-driven password/MFA reset on the same user, catching attacker-installed OAuth persistence — echoes the 2026 Vercel/Context.ai OAuth-abuse incident that followed a similar trust-granting pattern.
Detects anomalous, high-frequency MFA method resets, deletions, or updates performed by a single administrative account within a 15-minute window. This activity is indicative of a compromised support or helpdesk account being leveraged to bypass multi-factor authentication across multiple user accounts for unauthorized access or account takeover.
Detects modifications to Entra ID (Azure AD) Conditional Access policies where a user or group is explicitly added to an exclusion list. This behavior can be used by adversaries to bypass mandatory Multi-Factor Authentication (MFA) or device compliance requirements, facilitating persistence on a compromised account.
Detects a suspicious pattern where a user account experiences a burst of multiple MFA failures followed by an anomalous successful authentication within a short 15-minute window. This behavior is indicative of automated MFA fatigue attacks or attempts to exploit MFA bypass vulnerabilities.
Detects anomalous database activity involving the creation of 'ZQ'-prefixed staging tables followed by a high-volume drop operation across multiple tables within a short timeframe. This behavior is indicative of data staging for potential exfiltration followed by an attempt to delete evidence or clear tracks.
Detects anomalous mass consent grants to OAuth applications by users within the environment. This rule monitors for high-risk permissions such as Mail.Read and Files.ReadWrite.All, which are commonly used in consent phishing and application-based persistence campaigns to gain unauthorized access to mailbox and file resources.
This rule detects potential MFA fatigue attacks (Push Bombing) by monitoring sign-in logs for a high volume of authentication prompts directed at a single user within a one-hour window. It specifically identifies patterns where a user receives multiple denied MFA push requests followed by at least one successful approval. The rule also accounts for suspicious behavior such as requests originating from multiple IP addresses or geographic locations.
Detects potential Adversary-in-the-Middle (AiTM) phishing attacks by identifying an initial legitimate sign-in followed by a second sign-in from a different IP address within a short timeframe (indicative of session token replay), immediately succeeded by high-risk post-compromise actions such as OAuth application consent or the creation of malicious mailbox rules.
Detects potential N0va-style phishing or token-exchange abuse where a user completes a device-code authentication flow, followed immediately by an access token usage from a different IP address or device. This sequence indicates an adversary has captured the resulting token/session and is attempting to bypass multi-factor authentication (MFA) to gain unauthorized access to cloud resources.
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 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
This rule detects interaction with specific infrastructure identified as deceptive dating applications. It monitors network communication, sign-in attempts, email interactions, and endpoint execution associated with known malicious domains (e.g., archat.us, heyhru.com, sitin.ai), specific IP addresses (38.129.138.244), and unique package identifiers (com.cavalier.nalo, com.qiga.vio). The detection logic spans across DeviceNetworkEvents, EntraIdSignInEvents, EmailUrlInfo, EmailEvents, DeviceEvents, and DeviceProcessEvents to identify exposure to these Indicators of Compromise (IOCs) across both network and endpoint layers.
This rule detects internal users submitting potentially sensitive data to public generative AI applications (e.g., ChatGPT, Claude, Gemini) where Microsoft Purview DLP or a comparable inspection service has triggered a policy match. It correlates cloud application outbound signals with content inspection logs to identify high-risk exfiltration events while excluding sanctioned enterprise-licensed AI instances.
This rule detects internal users submitting potentially sensitive data to public generative AI applications (e.g., ChatGPT, Claude, Gemini) where Microsoft Purview DLP or a comparable inspection service has triggered a policy match. It correlates cloud application outbound signals with content inspection logs to identify high-risk exfiltration events while excluding sanctioned enterprise-licensed AI instances.
This rule detects internal users submitting potentially sensitive data to public generative AI applications (e.g., ChatGPT, Claude, Gemini) where Microsoft Purview DLP or a comparable inspection service has triggered a policy match. It correlates cloud application outbound signals with content inspection logs to identify high-risk exfiltration events while excluding sanctioned enterprise-licensed AI instances.
Detects anomalous, high-volume reconnaissance activity against multiple Microsoft Graph API endpoints (users, groups, roles, authentication methods, service principals, sites, and drives) by a single identity within a one-hour window. This behavior is indicative of post-compromise enumeration, often associated with threats like UNC6671/Storm-3121.
Detects anomalous, high-volume reconnaissance activity against multiple Microsoft Graph API endpoints (users, groups, roles, authentication methods, service principals, sites, and drives) by a single identity within a one-hour window. This behavior is indicative of post-compromise enumeration, often associated with threats like UNC6671/Storm-3121.
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