Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
771 detections
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Detects network connections to command-and-control infrastructure associated with the JeetBot/Twitch Enhanced Viewer malicious browser extension. The rule triggers on connections to known malicious domains or IPs, as well as specific API endpoints on ambiguous domains that are used for exfiltrating Twitch OAuth tokens.
Detects network connections to command-and-control infrastructure associated with the JeetBot/Twitch Enhanced Viewer malicious browser extension. The rule triggers on connections to known malicious domains or IPs, as well as specific API endpoints on ambiguous domains that are used for exfiltrating Twitch OAuth tokens.
Detects network connections to command-and-control infrastructure associated with the JeetBot/Twitch Enhanced Viewer malicious browser extension. The rule triggers on connections to known malicious domains or IPs, as well as specific API endpoints on ambiguous domains that are used for exfiltrating Twitch OAuth tokens.
Detects network connections to JeetBot/Twitch Enhanced Viewer malicious browser extension infrastructure exfiltrating Twitch OAuth tokens. Apex domains (jeetbot.cc, api.jeetbot.cc) are gated on a token-forwarding indicator to avoid false positives.
Detects the granting of OAuth permissions or consent to the 'eM Client' application within the Azure Active Directory environment. This activity is monitored as it can be used to authorize malicious applications to access user email data, often associated with OAuth-based token theft or persistence techniques.
Detects a GitLab personal access token used from multiple distinct source IPs or from IPs associated with the leaked gitlab-triage token, inconsistent with expected CI runner infrastructure.
Detects anomalous sign-in behavior where a device code authentication flow is initiated from one location, but the resulting session is replayed from a different IP address in a different country within a one-hour window. This behavior is indicative of an adversary capturing an authentication token via a device code phishing or relay attack and attempting to use it from their own infrastructure to bypass conditional access policies and MFA.
Detects potential device-code phishing abuse where a successful device code authentication is followed by scripted API activity using common automation user agents within a short time window (10 minutes). This behavior is indicative of an attacker who has successfully phished a user's device code and is immediately leveraging the stolen token to interact with Microsoft Graph, Azure, or Intune services.
Detects potential device-code phishing abuse where a successful device code authentication is followed by scripted API activity using common automation user agents within a short time window (10 minutes). This behavior is indicative of an attacker who has successfully phished a user's device code and is immediately leveraging the stolen token to interact with Microsoft Graph, Azure, or Intune services.
Detects potential device-code phishing abuse where a successful device code authentication is followed by scripted API activity using common automation user agents within a short time window (10 minutes). This behavior is indicative of an attacker who has successfully phished a user's device code and is immediately leveraging the stolen token to interact with Microsoft Graph, Azure, or Intune services.
Detects high-risk account modifications, such as adding MFA methods or OAuth consent grants, occurring shortly after a user sign-in. This pattern is indicative of post-AiTM session theft (e.g., NovaCookies) where an adversary uses a stolen session token to modify account settings for persistence or MFA bypass.
This rule detects potential OAuth token theft resulting from device code phishing (e.g., N0va phishkit). It monitors for a successful OAuth device-code authentication followed within 5 minutes by a secondary successful sign-in for the same user from a different IP address, different browser client, or different user agent, which is a common indicator of an adversary-in-the-middle (AiTM) session hijacking attempt.
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 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 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.
This rule detects OAuth authorization requests (targeting Microsoft or Google identity providers) that use 'prompt=none' (silent authentication) and redirect the user to an unexpected or untrusted domain. This pattern is commonly used in OAuth consent phishing or token theft attacks, where an attacker attempts to silently acquire an authorization code by redirecting it to a malicious application server instead of the legitimate service provider's domain.
This rule detects OAuth authorization requests (targeting Microsoft or Google identity providers) that use 'prompt=none' (silent authentication) and redirect the user to an unexpected or untrusted domain. This pattern is commonly used in OAuth consent phishing or token theft attacks, where an attacker attempts to silently acquire an authorization code by redirecting it to a malicious application server instead of the legitimate service provider's domain.
This rule detects potentially unauthorized access to cloud resources or OAuth applications by identifying sign-ins or API calls originating from IP addresses or locations not previously observed for the user in the 30 days prior to a documented Coder registry compromise exposure window.
Detects a GitLab personal access token used from multiple distinct source IPs or from IPs associated with the leaked gitlab-triage token, inconsistent with expected CI runner infrastructure.
Detects anomalous outbound HTTP requests to the Google OAuth token endpoint that mimic the behavior of the JSCeal malware. The rule identifies requests that lack standard browser-based User-Agent strings and omit the 'code_verifier' parameter, which is typically required in legitimate PKCE-enabled OAuth flows, indicating potential automated token theft.
Detects anomalous outbound HTTP requests to the Google OAuth token endpoint that mimic the behavior of the JSCeal malware. The rule identifies requests that lack standard browser-based User-Agent strings and omit the 'code_verifier' parameter, which is typically required in legitimate PKCE-enabled OAuth flows, indicating potential automated token theft.
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