Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects a precursor pattern identified as JADEPUFFER, where a failed 'ListKeys' operation (indicating an attempt to discover or access keys for a non-existent storage account) is immediately followed by a burst of destructive delete operations on critical cloud resources (e.g., storage, databases, key vaults) by the same actor.
Detects anomalous service principal activity characterized by multiple, rapidly issued, and overlapping OAuth tokens within a short time window. This pattern is associated with automated/agentic token orchestration (e.g., JADEPUFFER/Storm-3168 campaign) rather than standard manual operator activity, specifically when leveraging the python-requests user agent.
This rule detects successful authentication events for remote access applications (VPN, RDP, Azure AD App Proxy) where the source IP address or ASN has not been observed in the user's login history within the preceding 14 days. This behavior is indicative of potential account takeover using compromised credentials sourced from infostealers or other credential-harvesting activities.
The EntraIdSignInEvents table exposes the JA4 fingerprint in the GatewayJA4 field. But looking at the complete fingerprint alone does not tell us much. The interesting part starts when we break it down. With a simple KQL query, we can separate the TLS version, SNI, number of cipher suites, extensions and ALPN, together with the cipher and extension hashes. Then we can see how many sign-ins, users, IPs and countries are behind each JA4. The idea is simple: understand what is normal first, then look for what is different.
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 Microsoft Entra ID sign-in events where a user authenticates from a known residential proxy ASN, matching the user's historical geography but using a new, unrecognized device fingerprint. This behavior is indicative of session token replay attacks, where an adversary uses stolen session cookies to bypass standard authentication.
Detects instances where an identity with no prior compute-provisioning history in the last 14 days successfully provisions high-performance GPU-enabled virtual machines in Azure. This behavior is indicative of potential resource hijacking, such as LLMjacking, where compromised identities are leveraged to deploy infrastructure for unauthorized resource-intensive workloads.
Detects high-volume file download, access, or preview activity originating from known anonymous proxy services. This behavior is indicative of potential data exfiltration or unauthorized mass data collection from cloud storage applications.
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 high-volume file download, access, or preview activity originating from known anonymous proxy services. This behavior is indicative of potential data exfiltration or unauthorized mass data collection from cloud storage applications.
Detects instances where a user grants high-privilege permissions (Mail.Read, Files.ReadWrite.All, offline_access) to an OAuth application within 24 hours of a risky sign-in event. This pattern is commonly observed in AiTM (Adversary-in-the-Middle) or consent-phishing attacks designed to establish persistent access to cloud resources.
Detects instances where an administrative user performs a password or MFA method reset on a target account, followed within 24 hours by a sign-in event from that same target account flagged with a high or medium risk level. This sequence is characteristic of Adversary-in-the-Middle (AiTM) attacks, where an attacker utilizes stolen session tokens and subsequently coerces helpdesk or administrative intervention to reset MFA requirements to maintain unauthorized access.
This rule detects potential account compromise by identifying scenarios where a user's security information (MFA methods) is modified, deleted, or updated, followed shortly after (within 24 hours) by a successful sign-in using a less secure authentication method (SMS, Voice, OATH, or Mobile App notification) instead of a stronger method like FIDO2 or Passkey.
Detects MFA request abuse (T1621) by identifying a user receiving 5 or more MFA push, SMS, or call denials/timeouts within a 10-minute window across Entra ID, Okta, or Duo authentication logs. This behavior is indicative of an MFA fatigue attack, where an adversary continuously triggers MFA prompts until the user eventually approves the request out of frustration or confusion.
Detects the reuse of session identifiers (SessionId, DeviceId, or RefreshTokenJti) across different device fingerprints (e.g., changes in User-Agent, OS, or Browser) within a short timeframe. This behavior is indicative of session hijacking or adversary-in-the-middle (AiTM) attacks, where stolen session tokens or cookies are replayed by an attacker from a different endpoint than the original user.
Detects instances where a user account successfully registers or joins a device to Entra ID (Azure AD) immediately after performing a sign-in operation that was flagged with a medium or high-risk level. This pattern may indicate an adversary is attempting to enroll a malicious device to bypass conditional access policies or establish persistence in the environment using compromised credentials.
Detects a suspicious volume of cloud resource deletions occurring within a short 15-minute window, specifically targeting multiple storage accounts and SQL database/server instances. This behavior is indicative of a malicious actor or an automated script attempting to disrupt business operations or destroy data.
Detects anomalous bursts of deletion activity targeting Azure Site Recovery and Backup resources, including management locks and backup policies. This behavior is consistent with an adversary attempting to inhibit system recovery capabilities, often a precursor to destructive activities such as ransomware deployment. The rule also correlates this activity with the deletion of other critical infrastructure components like databases or storage accounts.
Detects instances where a user account registers a new Multi-Factor Authentication (MFA) method (such as TOTP or Authenticator app) shortly (within 30 minutes) after a high-risk or potentially anomalous sign-in event. This pattern is commonly associated with adversary-in-the-middle (AiTM) phishing attacks where an attacker captures an authenticated session and uses it to enroll a secondary, attacker-controlled MFA device for persistent account access.
This rule monitors cloud application events for high-frequency, automated API calls directed at AI/ML inference endpoints. A high volume of queries (over 1000) accessing a diverse range of objects (over 50) within a short window (60 minutes) by a single identity or IP address is flagged. Such behavior is characteristic of model extraction or distillation attacks, where an adversary probes an AI model to replicate its capabilities or internal parameters.
Detects outbound HTTP/HTTPS POST requests where the URI, query string, or path contains evidence of sensitive credential material, such as AWS access keys, private key blocks, or common environment variable patterns, indicative of potential credential harvesting and exfiltration to an external destination.
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