Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects instances where a user grants an OAuth application sensitive, high-privilege permissions (e.g., Mail.Read, Files.ReadWrite.All, Directory.ReadWrite.All) that are commonly abused in consent phishing campaigns to maintain persistent, silent access to mail and files independent of user credentials.
This rule detects successful non-interactive sign-ins to high-value applications (e.g., Office 365, Azure, Okta) where no multi-factor authentication (MFA) challenge was performed during the current session. Such activity is a strong indicator of session hijacking or token replay attacks, where adversaries reuse stolen session cookies to bypass interactive login requirements and MFA.
Detects M365 Copilot information extraction jailbreak attacks that attempt to obtain sensitive, classified, or comprehensive data through various social engineering techniques including fictional entity impersonation, bulk data requests, and privacy bypass attempts. The detection searches exported eDiscovery prompt logs for extraction keywords like "transcendent," "tell me everything," "confidential," "dump," "extract," "reveal," and "bypass" in the Subject_Title field, categorizing each attempt by extraction type and assigning severity levels (CRITICAL for classified/proprietary data, HIGH for bulk extraction or privacy bypass). Prompts are further analyzed for compound risk patterns such as "Confidential+Extraction" or "Bulk_Request+Bypass," filtering out low-severity cases to surface the most dangerous attempts to exfiltrate sensitive organizational information through AI manipulation.
Detects anomalous high-volume API querying patterns against an AI model, characterized by an abnormally high number of distinct input hashes and query frequency compared to the population baseline for that model. This pattern is consistent with model extraction or stealing attacks aimed at reconstructing a proxy model.
Detects a sequence of potentially malicious local command execution (via PowerShell, cmd, or mshta) that mirrors the 'ClickFix' social engineering pattern, followed by an OAuth application consent grant or device-code authorization by the same user within a short timeframe. This behavior is indicative of an adversary attempting to bypass MFA by tricking a user into authorizing a malicious application after establishing local execution on their endpoint.
Detects potential consent phishing attempts by identifying when a user grants high-risk, persistent-access OAuth scopes (such as Mail or Files) to a new third-party application. The rule specifically excludes known first-party Microsoft apps and utilizes a lookback period to avoid alerting on applications that have already been consented to within the tenant, reducing noise from routine enterprise app usage.
Detects an anomalous OfficeHome sign-in from an unmanaged device that completes secondary auth (error 50074) followed by a keep-me-signed-in event (error 50140), with the same session/user then rapidly accessing multiple sensitive applications (My Apps, My Profile, Account Controls, My SignIns, SharePoint/OneDrive/Outlook) - consistent with AiTM-assisted MFA bypass and persistent session establishment
Device code authentication flow completed, followed by subsequent session activity from a different IP/user-agent, indicating token replay to an attacker-controlled client that bypasses MFA
Detects a suspicious sequence of events involving NetScaler ADC or Gateway appliances: anomalous account authentication, followed by the establishment of a VPN or remote access session, and subsequent unusual outbound network activity indicating potential lateral movement or C2 communication from the appliance.
Detects a suspicious sequence of events involving NetScaler ADC or Gateway appliances: anomalous account authentication, followed by the establishment of a VPN or remote access session, and subsequent unusual outbound network activity indicating potential lateral movement or C2 communication from the appliance.
Detects high-risk GitHub events indicating data or asset exposure, such as creating public repositories with specific naming conventions, changing private repositories to public, or creating new gists, which could indicate exfiltration of sensitive assets or information.
Detects a suspicious pattern where an AI coding agent or user creates a public GitHub repository with specific naming conventions (e.g., 'sweeper-demo', 'gitshot-images') and subsequently pushes content containing sensitive terms like 'screenshot' within a short timeframe. This behavior is indicative of potential data exfiltration of internal development screenshots to a public repository.
Detects a suspicious pattern where an AI coding agent or user creates a public GitHub repository with specific naming conventions (e.g., 'sweeper-demo', 'gitshot-images') and subsequently pushes content containing sensitive terms like 'screenshot' within a short timeframe. This behavior is indicative of potential data exfiltration of internal development screenshots to a public repository.
Detects a suspicious sequence of events where Kubernetes API resources (namespaces, secrets, configmaps, pods, or nodes) are enumerated, followed by a search query against the Slack API, indicative of a potential reconnaissance phase or sandbox escape attempt.
Detects potential help-desk impersonation or social engineering activity involving account credential or MFA method resets, followed shortly by a sign-in from an unknown device or geography. The rule excludes activity tied to established ticketing systems or standard automated password expiration processes.
Detects a suspicious pattern where a user authenticates to a remote access portal (like Citrix or VPN) without multi-factor authentication (MFA) or with an existing risk flag, followed by a surge in file activity (creation, modification, or renaming of >500 files or >200 distinct files) on the same account within a 24-hour window, potentially indicating compromised credential usage for staging data for exfiltration or ransomware.
Detects instances where a user account registers a new MFA security method shortly after an initial successful sign-in from an unfamiliar device or network (Autonomous System Number). This behavioral pattern is characteristic of attackers, such as ShinyHunters vishing operators, who gain initial access via compromised credentials and subsequently enroll their own MFA devices to establish persistent access.
Detects unauthorized configuration or content changes (e.g., login or portal pages) performed outside of standard business hours by user accounts that have recently exhibited high-risk or compromised authentication behavior. This pattern aligns with tactics observed in the ShinyHunters playbook where compromised accounts are used to deface public-facing applications.
Detects potential MFA push fatigue (bombing) attacks by identifying users who receive an abnormal frequency of MFA push notification denials or interruptions within a short window, followed immediately by a successful authentication on a previously unseen device fingerprint. This pattern is characteristic of attackers attempting to wear down users into approving unauthorized login requests.
Detects a sequence of events indicative of an account takeover (ATO) attack, starting with a help-desk initiated password reset, followed immediately by new MFA factor or device enrollment, and concluding with an application login from an unrecognized device or location.
Detects a burst of file access or download operations from SharePoint or OneDrive by a single user account within a short timeframe. This behavior is indicative of bulk data collection or exfiltration, often associated with post-credential-theft activity where an attacker stage and extract organizational data.
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