Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous authentication behavior where the same session token or identifier is utilized by multiple distinct IP addresses or User-Agent strings within a short timeframe. This behavior is indicative of Adversary-in-the-Middle (AiTM) attacks, such as those employing Evilginx, where an attacker intercepts and replays stolen session cookies to bypass standard authentication and MFA controls.
Detects potential vishing or account takeover precursor activity where an external or guest Microsoft Teams user, impersonating IT or Help Desk support, communicates with a user who subsequently modifies their credentials or MFA configuration within the same 24-hour period.
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 MFA or credential reset activities on high-value or privileged accounts that lack a corresponding out-of-band verification signal, such as manager approval or identity proofing, within a one-hour window. This behavior is indicative of social engineering attempts where attackers manipulate help-desk procedures to bypass identity protections.
Detects npm package publication events specifically related to the @dforge-core/dforge-mcp library, including detection of OIDC-based trusted publishing mechanisms to identify potential supply chain compromise or unauthorized releases.
Detects anomalous Agentforce session activity where a user, via the General CRM subagent, queries Lead records followed shortly by an access request to Account records. This pattern is indicative of a potentially hijacked agent session attempting to pivot from lead review into unauthorized data access of broader customer information.
Detects anomalous, high-frequency interaction with AI model endpoints characterized by a high volume of requests with a near 1:1 ratio of distinct inputs. This pattern is indicative of automated knowledge-distillation or model extraction attacks against AI services.
This rule detects potential AI agent prompt injection by monitoring email subjects or attachment names for keywords often associated with system instruction overrides. It correlates these potentially malicious emails with subsequent execution of common administrative or script-interpreting binaries (e.g., PowerShell, cmd.exe) on the recipient's device within a one-hour window, suggesting a potential successful hijack of an automated process or AI agent.
This rule detects potential spear-phishing emails impersonating the Microsoft Account Team. The emails use lures related to one-time password (OTP) security notices and contain malicious ZIP or LNK files. It monitors sender addresses, display names, and subjects typical of this phishing campaign, and flags emails containing specific attachment patterns designed to trick users into executing malicious code.
Detects high-volume file access or download operations in SharePoint or OneDrive performed by common scripting libraries and client user agents, which may indicate automated data exfiltration.
This rule detects a correlation between a successful user login from an IP address not previously seen for that specific user in the last 14 days, followed immediately (within 1 hour) by sensitive administrative operations performed by the same user, such as adding members to roles or modifying application permissions.
Detects successful authentication attempts in Azure AD or Microsoft 365 logs originating from known proxy/VPN service providers or identified by suspicious Python-requests user agent strings. The rule specifically filters for events where MFA was not satisfied, indicating potentially bypassed or failed security controls from potentially malicious infrastructure.
Detects incoming phishing emails that contain both a shortened URL and references to a password-protected RAR archive, often used to deliver malware while bypassing email security content inspection.
This rule detects potential social engineering attacks where an attacker initiates a Microsoft Teams call from an external domain while impersonating an internal IT support or help desk entity. The detection specifically correlates this event with a recent preceding email bombing campaign (exceeding 20 emails) targeting the same user within the last 3 hours, indicating a coordinated attempt to pressure or confuse the victim.
Detects downstream delivery of likely AI-paraphrased mass-produced lure content by identifying bursts of near-duplicate subject lines from newly-seen domains with auth failures.
This rule detects anomalous, high-volume activity from a single user account interacting with known public generative AI platforms (e.g., ChatGPT, Copilot, Claude). It uses a threshold-based approach on CloudAppEvents logs to identify potential reconnaissance or bulk information gathering activities which could signify unauthorized use or automated data scraping via AI interfaces.
This rule detects anomalous, high-volume activity from a single user account interacting with known public generative AI platforms (e.g., ChatGPT, Copilot, Claude). It uses a threshold-based approach on CloudAppEvents logs to identify potential reconnaissance or bulk information gathering activities which could signify unauthorized use or automated data scraping via AI interfaces.
This rule detects anomalous, high-volume activity from a single user account interacting with known public generative AI platforms (e.g., ChatGPT, Copilot, Claude). It uses a threshold-based approach on CloudAppEvents logs to identify potential reconnaissance or bulk information gathering activities which could signify unauthorized use or automated data scraping via AI interfaces.
Detects instances where a single user account exhibits an anomalously high frequency of requests to public generative-AI platforms within a one-hour window, potentially indicating automated reconnaissance or data staging activities using AI tools.
Detects instances where a single user account exhibits an anomalously high frequency of requests to public generative-AI platforms within a one-hour window, potentially indicating automated reconnaissance or data staging activities using AI tools.
Detects activity associated with the 'BlueKit' session relay tool, which uses the rrweb library to capture and replay user session interactions. This rule identifies web browser network events involving rrweb-related scripts occurring within 10 minutes of a successful user sign-in, indicating a potential Adversary-in-the-Middle (AiTM) phishing attack where credentials and session cookies are being captured.
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