Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects unauthorized abuse of the OAuth 2.0 device authorization grant flow in Salesforce environments, commonly used by threat actors like ShinyHunters (UNC6240) to gain programmatic access. Attackers register malicious connected apps to impersonate legitimate tools, then use social engineering (vishing) to trick victims into approving device codes. This rule flags token issuance from non-allowlisted connected apps using the device_code grant type.
Detects instances where a user account consents to an OAuth application requesting high-privilege or sensitive scopes such as Mail.Read, Files.ReadWrite.All, or offline_access. This behavior is a common indicator of consent phishing attacks, where adversaries trick users into granting persistent access to their cloud resources via malicious OAuth applications.
Detects potential AI model extraction attacks where an adversary repeatedly queries a private model's inference API. By combining high-volume request counts with high input diversity, the rule identifies behavior indicative of an adversary constructing a labeled dataset to train a substitute, replicated model.
Detects adversarial backdoor triggers (such as pixel patches, watermark-like artifacts, or malicious token sequences) injected into AI inference inputs. The rule identifies these inputs by observing a combination of suspicious trigger patterns and significant, anomalous jumps in prediction confidence directed at a specific target class, indicative of a model integrity compromise.
This rule detects potentially malicious activity related to AI agent tool manipulation. It flags two primary scenarios: 1) The invocation of an AI tool shortly after an unmanaged or potentially unauthorized modification to its definition, and 2) Detection of prompt injection patterns within the output generated by an AI agent tool, which could indicate an attacker attempting to override the agent's instructions.
Detects a suspicious sequence where a user completes a device-code authentication flow followed shortly by consenting to high-risk OAuth permissions. This pattern is characteristic of consent phishing and device-code phishing attacks, where an attacker leverages these methods to gain persistent access to a user's cloud resources via malicious application tokens.
Detects potential consent phishing attacks by identifying newly registered multi-tenant Azure AD applications that receive OAuth consent from end-users within a short timeframe (10 minutes) of application registration. The rule specifically flags consents requesting high-risk delegated scopes (e.g., Mail.Read, Mail.Send, Files.ReadWrite.All, offline_access) where the application has not been verified by a trusted publisher.
Detects OAuth2 permission consent events where a user grants a third-party or newly registered application access to high-risk scopes (such as Mail, Files, or Directory). This technique is commonly used to establish persistence and bypass MFA by gaining access to user accounts via malicious or compromised OAuth applications.
Detects anomalous authentication behavior where a single session identifier is used to access three or more distinct SaaS applications within a one-hour window. This behavior often indicates the unauthorized use of a stolen session cookie or OAuth token (Pass-the-Cookie) across multiple SSO-integrated platforms.
The following analytic identifies the first-time execution of child processes spawned by Zoom (zoom.exe or zoom.us). It leverages Endpoint Detection and Response (EDR) data, specifically monitoring process creation events and comparing them against previously seen child processes. This activity is significant because the execution of unfamiliar child processes by Zoom could indicate malicious exploitation or misuse of the application. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further compromise of the endpoint.
The following analytic utilizes Windows AppLocker event logs to identify attempts to bypass application restrictions. AppLocker is a feature that allows administrators to specify which applications are permitted to run on a system. This analytic is designed to identify attempts to bypass these restrictions, which could be indicative of an attacker attempting to escalate privileges. The analytic uses EventCodes 8007, 8004, 8022, 8025, 8029, and 8040 to identify these attempts. The analytic will identify the host, full file path, and target user associated with the bypass attempt. These EventCodes are related to block events and focus on 5 attempts or more.
Detects browser extension updates that request a combination of high-risk permissions capable of account takeover (e.g., cookies, webRequest, identity) for extensions previously classified as low-risk. This pattern often indicates a supply chain compromise where a benign extension is acquired and subsequently updated with malicious capabilities.
Detects instances where a user grants OAuth application permissions to an unverified third-party application. The rule specifically monitors for high-privilege scopes such as Mail.Read, Files.ReadWrite.All, or full_access_as_app, which are commonly abused in illicit consent grant attacks to achieve persistent access to sensitive mailbox and cloud data.
Detects the installation or execution of known MALFEX malicious packages via npm or Node.js, which are used as entry points for polyglot or disguised payload delivery.
Detects the installation or execution of known MALFEX malicious packages via npm or Node.js, which are used as entry points for polyglot or disguised payload delivery.
Detects anomalous use of an LLM provider API key by identifying successful API requests originating from multiple distinct countries within a one-hour time window. This activity often indicates the potential theft and concurrent unauthorized use of an API token.
Detects the registration of new tools, plugins, or connectors into an AI agent's registry, particularly from unverified publishers or outside of sanctioned deployment windows. The rule also flags cases where these newly registered tools are immediately invoked with sensitive-scope permissions (e.g., admin, secrets, write), which is consistent with indicators of AI agent supply chain compromise or unauthorized tool injection.
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 Azure AD sign-in events indicative of 'EvilTokens' phishing-as-a-service tradecraft. The rule monitors for initial token-grant events (device-code authentication or OAuth application consent) and subsequent successful sign-ins using existing SessionIds. This rule is designed as a foundational detection component for identifying session token replay, which allows adversaries to bypass MFA and password requirements by hijacking valid session tokens.
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 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.
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