Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects potential session hijacking or malicious OAuth consent grants within Okta. This rule triggers either when an authenticated session start is associated with risky flags (impossible travel, anomalous device/location) or when an OAuth consent grant for 'recall' or cross-tenant functionality occurs shortly after a high-risk sign-in event.
Detects the registration of a new authentication method (e.g., FIDO2 key, authenticator app) shortly after a user account experiences a risky sign-in event. This pattern is indicative of an adversary attempting to establish persistent access and bypass MFA requirements, often following a successful AiTM (Adversary-in-the-Middle) phishing attack.
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.
Detects a multi-stage exfiltration sequence consisting of: (1) bulk file archival/compression using common utilities, (2) suspicious OAuth grant or service principal authorization, and (3) abnormal network egress volume to common ports. This pattern suggests an adversary is preparing data, establishing or leveraging an identity/application for cloud access, and subsequently exfiltrating data.
Detects patterns associated with the TeamFiltration backdoor module, specifically unauthorized OneDrive/SharePoint file modifications occurring shortly after an authentication from a previously unseen device/IP. The rule flags file changes (modifications, deletions, or renames) involving potential executable or script extensions, or those performed by the OneDrive SyncEngine app from a new source.
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 adversarial prompts designed to induce an Large Language Model (LLM) to disclose sensitive information, such as PII, secrets, or internal training data. This rule monitors the input prompt for extraction-based patterns and evaluates the model's response for indicators of a sensitive data leak (DLP hit).
Detects attempts to perform a Denial of Service (DoS) attack against an AI-enabled system by flooding the inference API with high volumes of requests or overly large inputs (high token counts). The rule identifies potential resource exhaustion by correlating high request rates or large input sizes with system-wide impacts, such as increased inference queue depth or excessive GPU utilization, which could impede service availability for other users.
This rule detects attempts to bypass LLM safety guardrails using adversarial prompting techniques (e.g., 'DAN' persona, hypothetical scenarios) or by identifying patterns of repeated moderation flags and refusals. It specifically looks for user prompts containing known jailbreak templates or an excessive frequency of safety-related trigger events, while excluding authorized red-team and safety evaluation accounts.
Detects evidence of indirect prompt injection attacks where an adversary introduces malicious instructions into data sources retrieved by an LLM or agent. The rule identifies common obfuscation and override techniques, including the use of zero-width characters, HTML comments for prompt breaking, base64 encoded instruction verbs, and specific override phrases within retrieved documents or tool outputs.
Detects suspicious AI agent behavior by identifying two primary scenarios: 1) The invocation of sensitive tools (e.g., code execution, file deletion) within a short timeframe following a flagged prompt injection or jailbreak attempt in the same conversation; or 2) The invocation of a tool by an AI agent that is not explicitly defined in its allowed tool list for the given workflow.
Detects modifications to Microsoft Entra ID applications or service principals involving the addition or configuration of 'externalauthprovider' redirect URIs or properties. This behavior is indicative of an attacker attempting to establish a malicious federated identity or persistence mechanism by manipulating application configuration to use an adversary-controlled authentication provider.
Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
Detects successful Entra ID authentication events where the user login originated from an anonymized or malicious IP address, or exhibited unfamiliar features, despite successfully completing multi-factor authentication (MFA). This pattern is consistent with adversary-in-the-middle (AiTM) phishing attacks, where a proxy service is used to intercept and bypass MFA controls.
Detects anomalous, high-volume automated API querying or bulk data exports within SaaS environments that target keywords associated with sensitive information, such as passwords, tokens, or configuration secrets. This pattern is characteristic of automated reconnaissance and unauthorized data harvesting, often observed in campaigns like those attributed to UNC6395.
Detects suspicious HTTP requests directed at SharePoint applications containing a malformed Register directive. The rule identifies attempts to leverage the 'publishingribbon' Tagprefix combined with unauthorized namespaces (e.g., System.Xaml, System.Windows.Data) to bypass the EditingPageParser.InitializeRegisterTable safelist check, a common technique for SharePoint remote code execution.
Detects the creation, update, or activation of an external Identity Provider (IdP) or federated trust configuration within the Okta environment. This activity may indicate an attempt to establish a rogue federation trust, which allows an adversary to perform cross-tenant impersonation and authenticate as any user by minting assertions signed by attacker-controlled keys.
This rule detects when a user registers or adds a new device in Entra ID within a short timeframe (two hours) of experiencing a risky sign-in or a password reset. It specifically flags registrations involving potentially unmanaged, non-compliant, or unusually trusted devices, which may indicate an attempt to establish persistent access or bypass Conditional Access policies.
This rule detects when a user or admin grants consent to an unverified OAuth application that simultaneously requests sensitive delegated permissions across multiple categories (Mail, Files, Directory, and Contacts). This pattern is a strong indicator of consent phishing attacks, where adversaries trick users into granting broad permissions to a malicious application to harvest sensitive data or gain persistent access to a Microsoft 365 environment.
Detects the registration of a new MFA authentication method on an Entra ID account from an unrecognized IP or device, occurring in close temporal proximity to a password reset or a risky sign-in event. This behavior is indicative of an adversary establishing persistence via self-service MFA registration after compromising user credentials.
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