Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects a high volume of 'MailItemsAccessed' operations within a short 15-minute window performed by a specific application ID. This behavior is often indicative of automated, bulk exfiltration of emails from a Microsoft 365 environment, potentially by a compromised application or an attacker using a malicious OAuth application.
This rule detects illicit OAuth consent grant events where an application is granted high-risk delegated permissions. This behavior is indicative of consent phishing or 'ConsentFix' tradecraft, where attackers trick users into granting permissions to a malicious application, thereby enabling unauthorized access to mail and file data.
This rule detects illicit OAuth consent grant events where an application is granted high-risk delegated permissions. This behavior is indicative of consent phishing or 'ConsentFix' tradecraft, where attackers trick users into granting permissions to a malicious application, thereby enabling unauthorized access to mail and file data.
This rule detects potential unauthorized access to Azure Key Vault secrets by correlating AI agent process execution on an endpoint with subsequent Key Vault API activity by the same user account. It tracks common AI coding/assistant tools running locally on a device and triggers an alert if the same identity accesses cloud-plane Key Vault operations within 5 minutes. Additionally, it identifies anomalous bulk secret enumeration patterns in Key Vault audit logs to detect potential automated exfiltration.
This rule detects potential unauthorized access to Azure Key Vault secrets by correlating AI agent process execution on an endpoint with subsequent Key Vault API activity by the same user account. It tracks common AI coding/assistant tools running locally on a device and triggers an alert if the same identity accesses cloud-plane Key Vault operations within 5 minutes. Additionally, it identifies anomalous bulk secret enumeration patterns in Key Vault audit logs to detect potential automated exfiltration.
This rule detects potential unauthorized access to Azure Key Vault secrets by correlating AI agent process execution on an endpoint with subsequent Key Vault API activity by the same user account. It tracks common AI coding/assistant tools running locally on a device and triggers an alert if the same identity accesses cloud-plane Key Vault operations within 5 minutes. Additionally, it identifies anomalous bulk secret enumeration patterns in Key Vault audit logs to detect potential automated exfiltration.
This rule detects potential unauthorized access to Azure Key Vault secrets by correlating AI agent process execution on an endpoint with subsequent Key Vault API activity by the same user account. It tracks common AI coding/assistant tools running locally on a device and triggers an alert if the same identity accesses cloud-plane Key Vault operations within 5 minutes. Additionally, it identifies anomalous bulk secret enumeration patterns in Key Vault audit logs to detect potential automated exfiltration.
Detects the use of specific malicious versions (v2.2.1) of GitHub Actions (actions-cool/issues-helper or actions-cool/maintain-one-comment) in workflow runs that also involve the exposure or referencing of the GITHUB_TOKEN, indicating potential credential theft and CI/CD pipeline compromise.
Detects the suspicious reactivation of 'actions-cool/issues-helper' or 'actions-cool/maintain-one-comment' repositories within a known malicious window (September 14-19, 2026). These repositories were involved in the 'Mini Shai-Hulud' supply-chain attack. This rule identifies when previously disabled repositories are re-enabled or modified to become active, potentially re-exposing malicious release tags to downstream users.
Detects the suspicious reactivation of 'actions-cool/issues-helper' or 'actions-cool/maintain-one-comment' repositories within a known malicious window (September 14-19, 2026). These repositories were involved in the 'Mini Shai-Hulud' supply-chain attack. This rule identifies when previously disabled repositories are re-enabled or modified to become active, potentially re-exposing malicious release tags to downstream users.
Detects sign-in events in Entra ID where the OAuth 2.0 device authorization grant (device code) flow is utilized. This flow is a common vector for phishing attacks designed to steal tokens by tricking users into authenticating on a secondary device, potentially bypassing MFA.
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 Cost Harvesting activity (AML.T0034) targeting ML inference services, characterized by a significant spike in request volume or compute consumption coupled with low payload uniqueness. This behavior suggests an adversary is intentionally driving up billing costs by submitting redundant or low-value requests to the API.
This rule detects potential cost harvesting or unauthorized resource abuse by monitoring API endpoints (typically AI model gateways) for high-volume or resource-intensive queries associated with a specific identity. It correlates these patterns with billing or autoscaling trigger events (e.g., cost spikes, quota exhaustion) and identifies agentic sessions that exhibit abnormally long reasoning loops, which suggests an attempt to waste computational budget.
Detects anomalous, high-frequency enumeration of AI/ML assets such as model registries, container image repositories, and training data buckets. This activity is indicative of reconnaissance for AI supply chain attacks, specifically tracking with MITRE ATLAS AML.T0007 (Discover AI Artifacts), where an actor probes infrastructure to identify targets for model theft, data poisoning, or unauthorized access.
Detects newly consented OAuth applications that immediately perform mass access or exfiltration of mail and file data across multiple mailboxes or drives within a short period, potentially indicating an attacker utilizing a malicious OAuth application.
Detects instances where a user grants consent to an unverified OAuth application requesting high-privilege scopes (e.g., Mail.Read, Files.ReadWrite.All). This behavior is characteristic of consent phishing attacks aimed at obtaining persistent access to sensitive data and mailbox contents.
Detects an anomalous volume of artificial intelligence model inference API calls (e.g., AWS Bedrock, Azure OpenAI, Google Vertex AI) initiated by a single IAM principal or API key within a short time window. This activity is indicative of 'LLMjacking', where compromised cloud credentials are leveraged by threat actors to perform unauthorized, high-cost AI model inference tasks.
Detects instances where an AI agent's persistent memory store is updated with suspicious content (e.g., prompt injection, role overrides, or embedded credentials) by a user other than the authorized agent owner, followed by subsequent tool invocations in an independent session. This behavior aligns with prompt injection attacks aimed at manipulating agent logic and unauthorized resource access via the AI agent.
Detects rapid mass-cloning or downloading of private GitHub repositories shortly after an OAuth token authorization, originating from IP addresses or countries not previously associated with the user account. This behavior is indicative of unauthorized bulk data extraction using compromised credentials, consistent with threat actor activity (e.g., TeamPCP/UNC6780).
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