Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects Cost Harvesting activity (AML.T0034) targeting ML inference services, characterized by a significant spike in request volume or compute consumption coupled with low payload uniqueness. This behavior suggests an adversary is intentionally driving up billing costs by submitting redundant or low-value requests to the API.
Detects Large Language Model (LLM) responses that contain instructions designed to propagate the current prompt to other users, systems, or agents. This pattern is indicative of a self-replicating prompt worm, which aims to spread malicious instructions across an AI ecosystem by leveraging the model's ability to generate text that instructs subsequent interactions.
Detects instances where an AI agent or LLM invokes a high-privilege or sensitive tool (such as code execution, outbound HTTP, or credential store access) immediately following a prompt that exhibits characteristics of prompt injection or jailbreaking. This behavior matches MITRE ATLAS pattern AML.T0053, suggesting an attempt to use the AI agent as a pivot or execution vehicle for unauthorized actions.
This rule identifies potential Adversary-in-the-Middle (AiTM) activity by detecting multiple successful MFA-authenticated logins for the same user within a short timeframe, originating from different IP addresses/ASNs and featuring inconsistent user agents or non-managed device traits. This pattern is characteristic of session hijacking via phishing kits such as EvilProxy, Mamba2FA, or Tycoon2FA, where the attacker replays the stolen session token after the user has completed the MFA flow.
Detects MFA fatigue or push-bombing attacks where a user account experiences an abnormally high volume of MFA request denials in a short duration (10 minutes), followed by a successful authentication event. The rule specifically looks for evidence where the successful authentication potentially originates from a different IP address than the failed attempts.
Detects newly consented OAuth applications that immediately perform mass access or exfiltration of mail and file data across multiple mailboxes or drives within a short period, potentially indicating an attacker utilizing a malicious OAuth application.
Detects instances where a user grants consent to an unverified OAuth application requesting high-privilege scopes (e.g., Mail.Read, Files.ReadWrite.All). This behavior is characteristic of consent phishing attacks aimed at obtaining persistent access to sensitive data and mailbox contents.
Detects instances where an AI agent's persistent memory store is updated with suspicious content (e.g., prompt injection, role overrides, or embedded credentials) by a user other than the authorized agent owner, followed by subsequent tool invocations in an independent session. This behavior aligns with prompt injection attacks aimed at manipulating agent logic and unauthorized resource access via the AI agent.
Detects malicious prompt injection attempts within an LLM conversation session. The rule monitors for ingested content containing common instruction-override keywords (e.g., 'ignore previous instructions', 'system prompt override'), followed immediately by the agent process executing unauthorized actions such as process launch, network connections, or file creation within the same session/user context.
Detects bulk insertions or content updates into vector databases, document stores, or knowledge bases that contain suspected instruction-override, jailbreak, or role-hijacking phrases, particularly when performed by unauthenticated or unknown identities.
Detects high-volume, systematic querying of LLM inference endpoints, which may indicate unauthorized model extraction, distillation attempts, or targeted data exfiltration from an LLM.
Detects bulk insertions or content updates into vector databases, document stores, or knowledge bases that contain suspected instruction-override, jailbreak, or role-hijacking phrases, particularly when performed by unauthenticated or unknown identities.
Detects high-volume, individually-tailored spear-phishing campaigns likely assisted by Generative AI. The rule monitors for a single sender targeting a high number of unique recipients with distinct, non-identical email subjects within a short timeframe. It specifically flags activity involving newly registered look-alike domains and interactions with credential-harvesting patterns in URLs.
Detects a suspicious sequence of events where a user engagement (voice/video call) involving themes of urgency or helpdesk interaction is closely followed by a high-risk administrative action (e.g., password reset, MFA device enrollment, or financial transfer) by the same user, where the communication session context differs from standard activity, indicative of a deepfake-enabled vishing attack.
Detects malicious prompt injection attempts within an LLM conversation session. The rule monitors for ingested content containing common instruction-override keywords (e.g., 'ignore previous instructions', 'system prompt override'), followed immediately by the agent process executing unauthorized actions such as process launch, network connections, or file creation within the same session/user context.
Detects bulk insertions or content updates into vector databases, document stores, or knowledge bases that contain suspected instruction-override, jailbreak, or role-hijacking phrases, particularly when performed by unauthenticated or unknown identities.
This rule detects potential prompt injection attacks against internal AI-powered assistants, agents, or LLM gateways. It monitors network HTTP requests for common patterns used to override system instructions, bypass safety filters, or force the model to adopt a privileged, unauthorized persona (e.g., administrator or debug mode). These techniques are often used in social engineering to manipulate the AI's output or security posture.
Detects anomalous account recovery or MFA reset activity performed by a single help-desk user account. A high volume of reset actions across numerous distinct users within a short duration is a key behavioral indicator of potential help-desk account compromise or insider abuse involving unauthorized account takeovers.
Detects the registration of a new MFA device or authenticator by a user shortly after a password reset event, where the registration originates from an IP address classified as a hosting provider, datacenter, or VPN. This pattern is indicative of a help-desk or vishing-based account takeover attack where an adversary registers their own MFA device to gain persistent access.
Detects potential post-authentication persistent actions (e.g., OAuth app grants, MFA changes, mailbox rule updates) performed shortly after a successful Google Workspace login, specifically flagging actions originating from IP addresses different from the login session to identify session hijacking or token replay (AiTM) activity. Includes suppression for known corporate IP ranges, trusted OAuth application consents, and recurring user behavior.
Detects potential exploitation of CVE-2026-41940 affecting cPanel/WHM, where administrative actions occur without a preceding successful login event from the same source IP within a 30-minute window. This indicates an authentication bypass attempt.
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