Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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 anomalous, high-velocity enumeration of IAM entities and cloud infrastructure resources by a single principal within a 5-minute window. This behavior is consistent with automated reconnaissance performed by agentic AI or scripted tools, often used to map cloud environments prior to exploitation.
Single-source Sigma proxy for the authentication-persistence stage of the multi-stage device-code-phishing-to-Graph-collection chain (see the companion KQL correlation rule "Passkey Persistence: Device-Code Phishing to MFA Takeover and Graph Collection" for the full, threshold-gated, cross-table detection). Sigma has no join, temporal-windowing, or aggregation support, so this rule fires on the authentication-persistence event alone and does NOT enforce the device-code sign-in precursor or the subsequent Graph-activity burst - both required by the full correlation. Scoped to the two highest-signal operations only (new security-info registration, and any admin-initiated registration on another user's account) to cut noise from routine self-service profile edits.
Detects potential post-authentication persistent actions (e.g., OAuth app grants, MFA changes, mailbox rule updates) performed shortly after a successful Google Workspace login, specifically flagging actions originating from IP addresses different from the login session to identify session hijacking or token replay (AiTM) activity. Includes suppression for known corporate IP ranges, trusted OAuth application consents, and recurring user behavior.
Detects high-privilege IAM roles (roles/owner, roles/resourcemanager.organizationAdmin) being granted at the GCP organization level. This activity is indicative of the ConfigConfusion attack, where a low-privilege user exploits a Kubernetes ConfigConnector (KCC) controller's high-privilege service account to perform unauthorized IAM modifications.
Detects malicious tampering with GitHub Actions workflows by monitoring for two specific patterns within an hour: the addition of automatic push triggers for the main branch alongside the removal of manual approval gates, or rapid sequential commits that rewrite workflows to facilitate an unattended publish of a package. This rule also integrates with npm registry publish events to identify potential malicious distribution of compromised packages like @dforge-core/dforge-mcp.
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