Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects potential lateral movement or compromised account use where a single user identity authenticates to multiple distinct cloud or administrative resources within a short time window (15 minutes). The rule uses a threshold of six or more unique applications or resources to identify anomalous patterns typical of reconnaissance or mass resource access often associated with stolen credentials. Administrative, service, and automation-related accounts are excluded to minimize noise.
Detects potential lateral movement or compromised account use where a single user identity authenticates to multiple distinct cloud or administrative resources within a short time window (15 minutes). The rule uses a threshold of six or more unique applications or resources to identify anomalous patterns typical of reconnaissance or mass resource access often associated with stolen credentials. Administrative, service, and automation-related accounts are excluded to minimize noise.
Detects potential lateral movement or compromised account use where a single user identity authenticates to multiple distinct cloud or administrative resources within a short time window (15 minutes). The rule uses a threshold of six or more unique applications or resources to identify anomalous patterns typical of reconnaissance or mass resource access often associated with stolen credentials. Administrative, service, and automation-related accounts are excluded to minimize noise.
Detects potential lateral movement or compromised account use where a single user identity authenticates to multiple distinct cloud or administrative resources within a short time window (15 minutes). The rule uses a threshold of six or more unique applications or resources to identify anomalous patterns typical of reconnaissance or mass resource access often associated with stolen credentials. Administrative, service, and automation-related accounts are excluded to minimize noise.
Flags MFA method deletions performed by an actor other than the account owner, especially when followed by registration of a new authenticator — the signature of a helpdesk agent socially engineered into swapping a victim's MFA factor.
Flags FIDO2/passkey registrations initiated or facilitated by a helpdesk/support identity for another user's account, matching Okta's advisory on threat actor O-UNC-066 ('Pink' leak site) and the ShinyHunters O-UNC-037 campaign that mimicked Microsoft's passkey enrollment flow to register attacker-controlled passkeys.
Detects the reuse of a session or refresh token from a geographic location that contradicts recent user activity (impossible travel) without a new interactive MFA challenge. This activity is indicative of Adversary-in-the-Middle (AiTM) phishing attacks, such as those leveraging Evilginx, where session cookies are captured and replayed from attacker infrastructure to bypass multi-factor authentication.
Detects when a user adds a new Multi-Factor Authentication (MFA) method such as a phone number, authenticator app, or FIDO2 key in Entra ID (Azure AD). Attackers often register their own MFA devices to maintain persistent access to a compromised account, particularly following a successful password reset or social engineering event.
This rule detects potential prompt injection attacks against AI agents by monitoring tool-invocation parameters. It identifies indicators such as common instruction-overriding phrases, large base64-encoded blobs, or embedded shell metacharacters within parameters when the agent's context is derived from external content (web, email, URLs, etc.).
This rule detects potential prompt injection attacks against AI agents by monitoring tool-invocation parameters. It identifies indicators such as common instruction-overriding phrases, large base64-encoded blobs, or embedded shell metacharacters within parameters when the agent's context is derived from external content (web, email, URLs, etc.).
Detects abnormal bulk API operations (upsert, batch updates, or deletions) targeting vector database or Retrieval-Augmented Generation (RAG) knowledge stores. This activity is flagged when performed by non-authorized service identities or when ingestion volume exceeds a specific threshold within a short timeframe, potentially indicating an attempt to poison the agent's memory store or retrieval corpus to facilitate indirect prompt injection.
Detects an abnormally high frequency of tool/function invocations by an AI agent within a one-minute timeframe. This pattern is indicative of a potential hijack where an attacker forces the agent into a runaway loop to perform resource exhaustion (DoS), repetitive brute-force operations, or automated exfiltration of sensitive data via manipulated tool execution.
Detects outbound HTTP POST requests from an AI agent runtime environment to known external paste-sites, webhook providers, or staging services. This behavior is indicative of a compromise where the AI agent is being manipulated to exfiltrate sensitive API keys, tokens, or configuration secrets to an attacker-controlled endpoint.
Detects unauthorized attempts to disable, reconfigure, or bypass AI safety guardrails and moderation services, particularly when followed by high-risk tool invocations such as file deletion, network egress, or credential access. This activity indicates an attacker attempting to impair an AI agent's defensive mechanisms to facilitate malicious actions.
Detects anomalous AI agent behavior where the agent ingests untrusted external content (e.g., tool outputs, RAG retrievals, or web fetches) and subsequently invokes high-risk or sensitive capabilities (such as shell execution, secret reading, or unauthorized network calls) within the same session. This pattern is indicative of an indirect prompt injection attack attempting to leverage the agent's internal tool-calling mechanisms for unauthorized actions.
Detects anomalous AI agent behavior where the agent ingests untrusted external content (e.g., tool outputs, RAG retrievals, or web fetches) and subsequently invokes high-risk or sensitive capabilities (such as shell execution, secret reading, or unauthorized network calls) within the same session. This pattern is indicative of an indirect prompt injection attack attempting to leverage the agent's internal tool-calling mechanisms for unauthorized actions.
Detects unauthorized or suspicious modifications to AI model artifacts, including fine-tuned weights and LoRA adapters. This rule identifies tampering attempts via unsigned uploads, bypassing CI/CD production deployment approvals, or mismatches in hash/checksum compared to known-good versions.
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