Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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 the granting of highly privileged OAuth or API scopes to AI-related applications (such as Copilot plugins, browser-automation agents, or AI connectors) immediately followed by high-volume data access activities (file reads or email transfers) performed by the same user account. This behavior is indicative of an attacker weaponizing a compromised identity to grant an external AI agent broad access to sensitive data for exfiltration or automated reconnaissance.
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 high-frequency authentication attempts originating from user agents associated with automation frameworks (Selenium, Playwright, Puppeteer, etc.). This pattern is often indicative of automated credential stuffing, brute force attacks, or CAPTCHA/2FA bypass attempts orchestrated by headless browsers.
Detects HTTP requests to popular LLM API services (OpenAI, Anthropic, Google, Cohere) containing common jailbreak or role-play patterns. These patterns attempt to bypass safety guardrails by using techniques such as persona-switching (e.g., 'DAN'), hypothetical scenario framing, or unauthorized security testing pretexts.
Detects multiple MFA push notifications generated within a short timeframe for a single user during a Self-Service Password Reset (SSPR) flow. This behavior is indicative of an attacker attempting to perform MFA fatigue/push harassment to gain unauthorized access via SSPR or to bypass account security controls.
Detects a suspicious sequence where an account undergoes a help-desk-initiated password or credential reset, followed shortly (within 30 minutes) by the registration of a new multi-factor authentication (MFA) device for that same account. This pattern is characteristic of social engineering attacks, such as those used by UNC3944/Scattered Spider, where an adversary impersonates a user to gain initial access and subsequently establishes persistence by adding an MFA device they control.
Detects a pattern of MFA fatigue or 'push-bombing' where a user receives 5 or more MFA challenge requests within 10 minutes, immediately followed by a successful authentication event. This pattern is characteristic of attackers who have obtained user credentials and are attempting to bypass MFA by repeatedly triggering push notifications until the user grants access.
Detects a suspicious sequence of administrative actions where a user's phishing-resistant multi-factor authentication (MFA) factor, such as FIDO2, passkeys, or certificates, is removed and immediately replaced by a weaker authentication method like SMS, voice call, or TOTP. This behavior, when performed by an administrator account on a different user account, is a common indicator of an MFA bypass attempt often associated with social engineering or insider threat scenarios.
Detects modifications to Entra ID Conditional Access or Okta authentication policies that introduce MFA exclusions, trusted network bypasses, or authentication-strength downgrades. This activity is highly indicative of an adversary attempting to maintain persistence or bypass MFA following a successful initial account compromise.
Detects high-privilege OAuth application consent grants occurring shortly after a user identity recovery event (e.g., password or MFA reset). This pattern is indicative of a help-desk social engineering attack where an adversary uses an identity reset to gain access to an account and subsequently establishes persistent access via a malicious OAuth application.
Detects high-risk identity modifications (password or MFA resets) following an IT helpdesk ticket that lacks mandatory security controls, such as callback verification, manager approval, or established ticket history. This behavior is indicative of vishing and social engineering attacks aimed at helpdesk personnel to compromise user accounts.
Detects anomalous large-volume file download or export activity from cloud repositories (SharePoint, OneDrive, or Salesforce) occurring shortly after a help-desk-mediated credential or MFA reset for the same user. This behavior is indicative of account takeover, specifically vishing-to-exfiltration patterns used by threat actors.
Detects suspicious account management activities, such as password resets or MFA re-enrollment, performed by an operator on a high-privileged or administrator account outside of typical business hours (06:00-20:00). This behavior is often indicative of help-desk impersonation or credential takeover attempts targeting Identity Provider (IdP) administrative roles.
Detects instances where an account assigned a help-desk or support role performs an MFA, password, or credential reset on an account tagged as an executive or VIP. This behavior is indicative of social engineering and help-desk impersonation attacks commonly utilized by threat actors to perform rogue MFA takeovers.
Detects a suspicious sequence of events indicating account takeover: a password reset, followed by an MFA factor re-enrollment, and concluding with a successful login from a geographically impossible location compared to recent history. This pattern often signifies a help-desk-assisted account compromise or token theft.
This rule monitors for the creation or modification of inbox rules in Microsoft 365 that contain financial-related keywords (e.g., 'payment', 'wire', 'invoice') and disruptive actions such as deleting, moving, forwarding, or redirecting messages. These patterns are characteristic of Business Email Compromise (BEC) attacks, where adversaries attempt to conceal financial correspondence from the victim to facilitate fraudulent transactions.
Detects instances where Microsoft 365 Copilot surfaces content labeled with sensitivity markings to a user who only has access to that content via inherited or broad sharing permissions (e.g., Company link, Anonymous link, or inherited permissions), rather than explicit access. This identifies potential AI-retrieval exploitation patterns where Copilot inadvertently exposes sensitive information due to oversharing configurations.
This rule detects high-risk administrative operations in Azure Active Directory (such as role assignment, password resets, and account status changes) that occur within 3 hours of a user's sign-in. This pattern often indicates an account compromise followed by immediate privilege escalation or persistence establishment.
Detects the creation of email mailbox or transport rules that perform actions like hiding, deleting, marking as read, forwarding, or redirecting emails with finance or payment-related keywords. This behavior is indicative of a Business Email Compromise (BEC) post-exploitation workflow where adversaries attempt to conceal malicious activity or divert financial communications.
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