Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects unauthorized or anomalous modifications to machine learning model checkpoints. The rule triggers on significant deviations in weight delta percentages outside of scheduled fine-tuning windows, or when a model checkpoint hash is altered by users or service accounts without corresponding CI/CD pipeline approval or documented code review.
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 attempts to bypass Vite development server file access restrictions by appending query parameters such as ?raw, ?import&raw, or ?import&url&inline to request paths for sensitive files like .env, cloud credentials, and terraform state files.
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 web requests targeting common LLM/AI inference API endpoints containing strings indicative of prompt injection attempts, such as attempts to bypass filters, exfiltrate system instructions, or force model behavior changes (e.g., DAN mode).
This rule detects potentially malicious POST requests to SharePoint endpoints (such as AddGallery.aspx) that include indicators of WebPartMarkup or ToolPane payloads. This activity attempts to bypass the SPUtility.EnsureAuthentication() check that is typically enforced on the ToolPane.aspx page, potentially allowing unauthorized code execution or configuration manipulation.
This rule detects attempts to exploit CVE-2026-5027 in Langflow by identifying path traversal sequences (such as '../', '..\', or absolute paths) within the query parameters of requests directed to the '/api/v2/files' endpoint.
Detects an AI agent or automated process that accesses sensitive data sources (such as vaults, databases, or documentation platforms) followed by an outbound HTTP POST request to known high-risk SaaS or web services (e.g., paste sites, file sharing, or webhook services) with a suspicious or large payload.
Detects instances where an AI agent's output contains sensitive credential patterns such as AWS access keys, JWTs, private keys, or Bearer tokens. This rule monitors user communication, generic events, and file creation events to identify potential security compromises or unintended data leakage from the AI model.
Detects anomalous, high-volume HTTP requests to AI agent orchestration endpoints (e.g., tool invocation, plugins, or webhooks) followed by suspicious process execution or outbound network connections from the same host. This behavior is indicative of RCE exploitation against an AI agent framework, such as insecure deserialization or SSRF within a plugin connector.
Detects the creation of scheduled tasks, recurring jobs, or webhook subscriptions by a user identity flagged as an AI_AGENT. This activity is monitored for persistence, especially when the event lacks a descriptive context or targets external networks, suggesting potential unauthorized agent behavioral drift or malicious persistence.
Detects UNC6240 PSEMHUB.war web shell/tunnel/trojanized installer artifacts by distinctive content markers (x.jsp, u.jsp, tunnel.jsp/jspx, Ple64.exe)
Detects Salesforce Experience Cloud guest user sessions performing queries or reads on objects not within the authorized allowlist. This behavior, characterized by an abnormal volume of records retrieved or anomalous query patterns, is consistent with attempts to exploit misconfigured guest profile permissions to access internal sensitive data.
Detects the creation of ransom or extortion note files within known web server directories immediately following suspicious HTTP activity targeting an internet-facing host. This pattern aligns with known ShinyHunters extortion tactics where applications are exploited to deploy extortion notes.
This rule detects potential password spraying attacks against PeopleSoft or PeopleTools applications. It monitors for a pattern where a single source IP address generates multiple failed authentication attempts against a variety of distinct user accounts, followed by at least one successful login from the same source IP. This behavior is indicative of an adversary attempting to gain unauthorized access by iterating through a list of usernames with common or guessed passwords.
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.
Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
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