Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects user authentication events where the MFA redirect URL points to an unrecognized or potentially malicious domain. This behavior is indicative of an Adversary-in-the-Middle (AiTM) attack, such as the TrustSink credential capture technique, where the user is diverted from the legitimate identity provider to an attacker-controlled OIDC endpoint to capture credentials or session tokens.
Detects the modification of critical Entra ID authentication method policies, such as the registration or configuration of external MFA/OIDC providers, by a user account shortly after an anomalous or high-risk login event. This behavior is indicative of an attacker attempting to establish persistent backdoor access via rogue MFA provider registration, consistent with techniques used to subvert cloud authentication policies.
Detects successful OAuth token grant events for specific third-party SaaS integrations (Salesloft, Drift, Gainsight) where requested scopes include broad permissions such as 'api', 'refresh_token', or 'full'. This pattern is indicative of potential consent phishing or the abuse of a compromised OAuth application to maintain persistent, high-privileged access to SaaS resources.
Detects inbound email or ticketing messages containing extortion language (such as payment deadlines, leak-site references, and data-shredding threats) specifically directed at high-value internal targets like executives, security teams, and legal departments. This pattern is consistent with the ShinyHunters 'pay-or-leak' extortion model.
This rule detects potential adversarial reconnaissance activities targeting AI agents via multiple communication channels (e.g., email, collaboration tools). It identifies patterns where a sender transmits messages containing common 'jailbreak' or 'instruction-override' phrases (e.g., 'ignore previous instructions', 'bypass approval') across at least three distinct channels within a 24-hour window, indicating a concerted effort to manipulate or probe agent logic.
This rule detects potential adversarial reconnaissance activities targeting AI agents via multiple communication channels (e.g., email, collaboration tools). It identifies patterns where a sender transmits messages containing common 'jailbreak' or 'instruction-override' phrases (e.g., 'ignore previous instructions', 'bypass approval') across at least three distinct channels within a 24-hour window, indicating a concerted effort to manipulate or probe agent logic.
This rule detects potential adversarial reconnaissance activities targeting AI agents via multiple communication channels (e.g., email, collaboration tools). It identifies patterns where a sender transmits messages containing common 'jailbreak' or 'instruction-override' phrases (e.g., 'ignore previous instructions', 'bypass approval') across at least three distinct channels within a 24-hour window, indicating a concerted effort to manipulate or probe agent logic.
This rule detects potential data exfiltration by correlating access to files labeled as sensitive (confidential, restricted, etc.) with a subsequent upload to a personal cloud storage provider or transmission via email to a personal domain within a two-hour window.
Detects evidence of the SCKit supply chain worm within GitHub Actions CI pipelines. The rule identifies attempts to poison the build process by injecting a BASH_ENV path into GITHUB_ENV, which forces the execution of a malicious script (sckit-publish-bridge.sh) during subsequent shell steps. This script is designed to exfiltrate npm publishing credentials (NODE_AUTH_TOKEN) to external infrastructure. The detection logic covers both the injection mechanism and diagnostic output indicators from the bridge script.
Detects malicious instruction-override payloads, hidden HTML comments, or obscured text (e.g., zero-font size) in third-party content ingested into LLM/RAG systems, commonly used for indirect prompt injection.
Detects potential ML model-stealing attacks against inference API endpoints. The rule identifies anomalous behavior characterized by an extremely high volume of diverse queries from a single API key over a 24-hour period. Such patterns are consistent with systematic decision-boundary probing or input-space coverage attempts used for training a replica model (distillation or knockoff attacks) without direct access to model weights.
Detects systematic and high-frequency probing of internet-exposed LLM chatbot or API gateway endpoints. The rule monitors for sequences of requests, use of known prompt-infiltration or jailbreak testing toolkits, and varied path access indicative of automated reconnaissance or adversarial prompt engineering attempts.
This rule detects modifications to critical AI agent configurations—such as system prompts, tool allowlists, permission scopes, or model parameters—by users who are not part of the authorized 'agent-ops' group. It flags potentially high-risk changes, particularly when those changes fall outside established maintenance windows or involve an expansion of agent permissions.
This rule detects unauthorized or malicious agentic AI behavior where prompt injection or poisoned content causes an AI agent to execute excessive tool calls or enter an infinite loop of tool invocations. This behavior can lead to compute resource exhaustion, increased operational costs, and potential data exfiltration via excessive querying.
Detects adversarial AI cost harvesting (AML.T0034.000) by identifying anomalous volumes of low-complexity queries against an AI inference API that result in a significant, unexplained spike in billed costs. This behavior mimics denial-of-service tactics aimed at exhausting compute capacity and financial resources.
Detects ingestion of documents containing potential LLM prompt injection payloads (using zero-width characters or HTML comments to hide imperative instructions) into a RAG system, followed by anomalous LLM behavior such as unauthorized tool execution or unintended data disclosure.
Detects ingestion of documents containing potential LLM prompt injection payloads (using zero-width characters or HTML comments to hide imperative instructions) into a RAG system, followed by anomalous LLM behavior such as unauthorized tool execution or unintended data disclosure.
Detects ingestion of documents containing potential LLM prompt injection payloads (using zero-width characters or HTML comments to hide imperative instructions) into a RAG system, followed by anomalous LLM behavior such as unauthorized tool execution or unintended data disclosure.
Detects anomalous AI model inference activity where a caller submits a high volume of near-duplicate queries while frequently requesting raw confidence scores. This behavior is indicative of a membership-inference privacy attack, where an adversary attempts to determine if specific data points were included in the model's training set by analyzing query response patterns.
Detects LLM jailbreak attempts using a 'crescendo' multi-turn strategy. The rule monitors for sequences where an initial prompt contains known jailbreak-intent keywords leading to an AI refusal, followed shortly by a compliant AI response to a rephrased request within the same conversation session.
Detects adversaries attempting to override LLM system instructions by submitting crafted prompts containing known injection patterns (e.g., 'ignore previous instructions', role-override). The rule tracks anomalous interaction patterns identified by multiple occurrences or a variety of override phrases within a short timeframe.
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