Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the ingestion of documents into production RAG (Retrieval-Augmented Generation) indexes from untrusted or external-facing sources, which may indicate an attempt to perform RAG poisoning attacks.
This rule detects potential cost harvesting or service abuse against AI gateway endpoints. It establishes a baseline of typical user activity over a 30-day period and flags anomalies where current request volume (in the last 1 hour or 24 hours) significantly exceeds the user's established P95 baseline. To reduce noise, it specifically identifies scenarios where these high-frequency requests are associated with low-complexity, short prompts, which are indicative of automated probing or resource-intensive scraping rather than legitimate high-utility usage.
This rule detects anomalous behavior in AI agent tool invocations, specifically monitoring for high volumes of tool calls, high costs associated with token consumption, or suspicious tool interactions following a flagged indirect prompt injection. This behavior is indicative of potential resource exhaustion (denial of service) or abusive agent behavior as defined by the MITRE ATLAS framework for AI security.
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.
This rule detects potential Denial of Service (DoS) attacks against AI inference endpoints. It monitors for sustained high latency and high error rates combined with a high volume of 'heavy' requests (those exceeding a token/character count threshold) from specific source IPs. This pattern suggests an attempt to exhaust system resources by forcing the processing of complex or overly large prompts.
This rule detects potential Denial of Service (DoS) attacks against AI inference endpoints. It monitors for sustained high latency and high error rates combined with a high volume of 'heavy' requests (those exceeding a token/character count threshold) from specific source IPs. This pattern suggests an attempt to exhaust system resources by forcing the processing of complex or overly large prompts.
Detects suspicious modifications or registrations of AI agent tools within the registry. The rule identifies three core indicators of compromise: usage of an unverified or external registry source, unauthorized tool definition modifications (hash mismatch against baseline), and a suspicious 'update chain' where a non-standard maintainer pushes a tool update that simultaneously increases requested operational scopes.
Detects attempts to bypass LLM safety alignment by utilizing roleplay or hypothetical framing (e.g., DAN mode) alongside evidence of prompt iteration, identified by repeated moderation blocks within the same session.
Detects a suspicious sequence of events where a user's registered phone number or recovery contact method is updated, followed shortly by an MFA factor reset or new factor enrollment. This pattern is a common indicator of a SIM-swap attack, where an attacker intercepts SMS-based MFA codes after hijacking the victim's phone number.
Detects anomalous access to SharePoint or OneDrive by identities identified as bots, agents, or service principals. The rule specifically monitors for multiple file accesses where the file names suggest the presence of secrets, passwords, or API keys, which may indicate a compromised or malicious AI agent attempting to harvest credentials.
Detects potential passkey phishing follow-on activity by monitoring for successful sign-ins from unmanaged or non-compliant devices to OfficeHome or via device code flow, followed within 30 minutes by access to self-service account-management applications, accompanied by specific authentication errors (50074 or 50140).
Detects anomalous GitHub audit log activity indicative of a potential supply-chain compromise. The rule identifies workflows or automated entities (Bot/App) that access sensitive Action secrets followed by the publishing of packages to public registries like PyPI or npm. This activity can represent the precursor steps of token-theft-driven malicious package injection.
Detects anomalous, high-volume file access or preview activity in OneDrive or SharePoint from non-browser user agents. This pattern is indicative of potential data exfiltration where adversaries use scripted clients or APIs to mass-read or preview files, bypassing traditional download-based alerts.
Detects anomalous, high-volume file access or preview activity in OneDrive or SharePoint from non-browser user agents. This pattern is indicative of potential data exfiltration where adversaries use scripted clients or APIs to mass-read or preview files, bypassing traditional download-based alerts.
Detects an exploitation attempt targeting SharePoint through the EditingPageParser by injecting a Register directive. This attempt involves bypassing SafeControls through specifically crafted HTTP POST requests to ToolPane.aspx, utilizing the GetPartPreviewAndPropertiesFromMarkup function to execute malicious tag patterns.
Detects a suspicious pattern where a user authenticates via device code and, within a short timeframe (two hours), registers a new device to their account. This behavior is indicative of the 'ARToken' persistence pattern, where an adversary uses a stolen session to bind a rogue device to the victim's account, potentially to mint a Primary Refresh Token (PRT) for persistent access.
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 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 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.
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