Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous, high-volume account enumeration against the Microsoft Self-Service Password Reset (SSPR) endpoint from a single source IP. The rule identifies a pattern of mixed response codes, specifically interleaving '50034' (Account does not exist) with successful or other outcomes, which indicates the use of automated tools to validate the existence of corporate accounts via response differentiation.
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.
This rule detects Salesforce Agentforce or similar AI-driven chat responses that contain external URLs with non-standard Top-Level Domains (TLDs). It identifies cases where the organization's redactor failed to strip a URL due to unexpected TLDs or unusual trailing termination characters (e.g., brackets) that are still rendered as clickable links in the interface. Such links may facilitate phishing, credential harvesting, or redirection to malicious sites.
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.
Detects outbound network traffic to Microsoft Graph API originating from a user-agent string identified as 'python-httpx'. This behavior is characteristic of automated scripts or malicious tools using Python libraries to interact with Microsoft cloud services, potentially for data exfiltration or unauthorized API interaction.
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.
Detects modifications or creations of Azure DevOps pipelines and service connections, as well as pipeline executions performed outside of standard business hours (6 AM to 8 PM on weekdays). This activity is intended to identify potential unauthorized infrastructure or pipeline configuration changes that could indicate persistence or lateral movement via CI/CD pipelines.
Detects modifications or creations of Azure DevOps pipelines and service connections, as well as pipeline executions performed outside of standard business hours (6 AM to 8 PM on weekdays). This activity is intended to identify potential unauthorized infrastructure or pipeline configuration changes that could indicate persistence or lateral movement via CI/CD pipelines.
Detects proxy requests directed towards the Microsoft OAuth2 device authorization endpoint originating from suspicious referrers (e.g., .de top-level domains). This pattern is consistent with device code phishing attacks where users are redirected from external malicious sites to a legitimate Microsoft authentication page to obtain an authorization code.
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 instances where a user account registers a new Multi-Factor Authentication (MFA) method (such as TOTP or Authenticator app) shortly (within 30 minutes) after a high-risk or potentially anomalous sign-in event. This pattern is commonly associated with adversary-in-the-middle (AiTM) phishing attacks where an attacker captures an authenticated session and uses it to enroll a secondary, attacker-controlled MFA device for persistent account access.
Detects instances where a user account registers a new Multi-Factor Authentication (MFA) method (such as TOTP or Authenticator app) shortly (within 30 minutes) after a high-risk or potentially anomalous sign-in event. This pattern is commonly associated with adversary-in-the-middle (AiTM) phishing attacks where an attacker captures an authenticated session and uses it to enroll a secondary, attacker-controlled MFA device for persistent account access.
Detects an exploitation attempt against the SharePoint WebPartPagesWebService using the GetWebPartPageConnectionInfo method. The rule monitors for suspicious input patterns such as 'Register' and 'ignoreParentFrozen' within the URI query, which are indicative of a directive injection vulnerability.
Detects modifications to Microsoft Entra authentication policy or external authentication method configurations, which may indicate an adversary attempting to establish a 'TrustSink' or alternate authentication persistence mechanism by registering a malicious OIDC provider or modifying authentication policy.
This rule detects a multi-stage phishing campaign where an attacker uses legitimate Notion infrastructure to host PDF invitation lures. The detection correlates email delivery of a Notion-themed invitation with a subsequent user click on a URL containing device code authorization keywords (such as 'device', 'devicelogin', or 'code') within a 60-minute window, which is indicative of a device-code harvesting attack.
This rule detects potential account hijacking via OAuth device-code authorization flow, specifically identifying patterns associated with phishing kits (e.g., EvilTokens, ARToken). It monitors sign-in events where the device code flow is utilized, specifically flagging successful grants that potentially bypass standard interactive credential prompts.
Detects potential session hijacking where a user logs into Google Workspace from a new IP or country, followed within an hour by the registration of a new security key or passkey, which is a common technique to establish persistent unauthorized access.
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