Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous non-interactive sign-ins that bypass multi-factor authentication (MFA) requirements from a geographical location not previously seen in the user's historical sign-in baseline. This pattern is indicative of attackers using stolen OAuth access or refresh tokens to maintain persistence and bypass standard authentication controls.
Detects anomalous activity associated with OAuth 2.0 device code phishing campaigns. The rule identifies accounts showing frequent device code authentication attempts, including successful logins from potentially unmanaged or unrecognized devices, which is consistent with the behavior of phishing kits targeting OAuth device grant flows.
Detects anomalous activity associated with OAuth 2.0 device code phishing campaigns. The rule identifies accounts showing frequent device code authentication attempts, including successful logins from potentially unmanaged or unrecognized devices, which is consistent with the behavior of phishing kits targeting OAuth device grant flows.
Detects anomalous activity associated with OAuth 2.0 device code phishing campaigns. The rule identifies accounts showing frequent device code authentication attempts, including successful logins from potentially unmanaged or unrecognized devices, which is consistent with the behavior of phishing kits targeting OAuth device grant flows.
Detects potential N0va-style phishing or token-exchange abuse where a user completes a device-code authentication flow, followed immediately by an access token usage from a different IP address or device. This sequence indicates an adversary has captured the resulting token/session and is attempting to bypass multi-factor authentication (MFA) to gain unauthorized access to cloud resources.
Detects potential N0va-style phishing or token-exchange abuse where a user completes a device-code authentication flow, followed immediately by an access token usage from a different IP address or device. This sequence indicates an adversary has captured the resulting token/session and is attempting to bypass multi-factor authentication (MFA) to gain unauthorized access to cloud resources.
Detects anomalous access to sensitive Microsoft Graph API endpoints (messages, mailFolders, contacts) following a device-code authentication. The rule specifically flags activity from non-standard or generic client applications, which is a pattern often associated with post-compromise proxying or token abuse, such as the 'ARToken' technique.
Detects anomalous cloud authentication events occurring within a 3-hour window after suspected unauthorized access to sensitive configuration or credential files on a Vite development server. The rule flags sign-ins with high risk, anomalous location, or missing device compliance information, excluding trusted corporate locations and devices, serving as an indicator of potential post-exfiltration account takeover.
Detects anomalous cloud authentication events occurring within a 3-hour window after suspected unauthorized access to sensitive configuration or credential files on a Vite development server. The rule flags sign-ins with high risk, anomalous location, or missing device compliance information, excluding trusted corporate locations and devices, serving as an indicator of potential post-exfiltration account takeover.
Detects anomalous cloud authentication events occurring within a 3-hour window after suspected unauthorized access to sensitive configuration or credential files on a Vite development server. The rule flags sign-ins with high risk, anomalous location, or missing device compliance information, excluding trusted corporate locations and devices, serving as an indicator of potential post-exfiltration account takeover.
This rule detects potential exploitation attempts (CVE-2026-59822) against LiteLLM MCP tool endpoints. It identifies HTTP requests where the authorization bearer token is unusually short (<= 12 chars), lacks the 'sk-' prefix, and does not appear to be a valid JWT, yet successfully results in a 200 OK status, indicating a potential bypass of authentication logic.
This rule detects potential exploitation attempts (CVE-2026-59822) against LiteLLM MCP tool endpoints. It identifies HTTP requests where the authorization bearer token is unusually short (<= 12 chars), lacks the 'sk-' prefix, and does not appear to be a valid JWT, yet successfully results in a 200 OK status, indicating a potential bypass of authentication logic.
This rule detects attempts to exploit the LiteLLM proxy pass-through endpoint to interact with the cloud Instance Metadata Service (IMDS). Specifically, it identifies configuration attempts to route traffic to the IMDS IP (169.254.169.254) and subsequent requests to retrieve IAM security credentials, which is indicative of a Server-Side Request Forgery (SSRF) attack to steal cloud identity tokens.
Detects inbound traffic, sign-ins, and email activity originating from the Anthropic IP range 160.79.104.0/21.
This rule correlates email sender activity with network, cloud, and identity logs by matching traffic against a specific IP range (160.79.104.0/21) identified as malicious or suspicious. The rule flags instances where this range appears as an email sender IP or as a remote/source IP address across various infrastructure indices, potentially indicating adversary communication or exfiltration.
This rule detects various event types (email, identity authentication, network connections, and cloud audit logs) originating from or interacting with the specific IP address range 160.79.104.0/22 to 160.79.111.0/22, which is associated with known malicious activity.
Detects high-volume credential guessing attacks against applications, characterized by a large number of distinct source IP addresses and geographic locations. This pattern is indicative of attackers using residential proxy pools to distribute their attempts, effectively bypassing traditional rate limiting or per-IP WAF thresholds while targeting authentication or error oracles. The rule specifically filters for relevant failure codes and enforces constraints on IP/user diversity to reduce noise from single-point-of-failure events or legitimate infrastructure.
Detects high-volume credential guessing attacks against applications, characterized by a large number of distinct source IP addresses and geographic locations. This pattern is indicative of attackers using residential proxy pools to distribute their attempts, effectively bypassing traditional rate limiting or per-IP WAF thresholds while targeting authentication or error oracles. The rule specifically filters for relevant failure codes and enforces constraints on IP/user diversity to reduce noise from single-point-of-failure events or legitimate infrastructure.
Detects high-volume credential guessing attacks against applications, characterized by a large number of distinct source IP addresses and geographic locations. This pattern is indicative of attackers using residential proxy pools to distribute their attempts, effectively bypassing traditional rate limiting or per-IP WAF thresholds while targeting authentication or error oracles. The rule specifically filters for relevant failure codes and enforces constraints on IP/user diversity to reduce noise from single-point-of-failure events or legitimate infrastructure.
Detects successful authentication events in Microsoft Entra ID (formerly Azure AD) that utilize the 'device code' OAuth flow. This flow is often used by command-line utilities and headless applications to authenticate users on devices without a web browser, but it is also frequently abused by attackers to perform phishing and session hijacking.
This rule detects anomalous interaction with the Microsoft Graph API by monitoring for high-frequency requests across multiple functional categories (e.g., tenant, directory, privilege, application, repository, mailbox) from a single identity. A large number of distinct API paths across multiple categories in a short time frame is indicative of an attacker performing reconnaissance or automated environment enumeration.
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