Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
2,831 detections
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Detects high-frequency S3 object upload activity (PutObject/CompleteMultipartUpload) originating from the Rclone tool, a common indicator of potential data exfiltration to cloud storage.
Detects a sequence of events starting with a device code OAuth sign-in, followed within two hours by the creation or modification of inbox rules and high-frequency enumeration of email items (contacts, folders, messages, threads, people) via Microsoft Graph API. This behavior is indicative of post-compromise activity where an adversary uses OAuth device code grants to maintain access and subsequently automates inbox analysis or data harvesting.
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 anomalous GitHub activity where a single actor interacts with 20 or more unique repositories within a 24-hour period. This behavior is indicative of potential repository scraping, unauthorized data harvesting, or mass configuration changes associated with reconnaissance or exfiltration activity.
Detects anomalous GitLab project enumeration where an account performs an excessive number of requests to the Projects API using the 'membership=true' parameter within a one-hour window. This behavior is indicative of an attacker attempting to discover all accessible repositories associated with a compromised or over-privileged GitLab token.
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 unreviewed commits to .gitlab-ci.yml that alter how the gitlab-triage tool invokes the sensitive API_TOKEN variable, with no associated merge request.
Detects a single GitLab token/account holding Owner-level access across an unusually large number of projects (50+), the standing-privilege condition that turned one leaked token into a 55-project supply-chain exposure.
Detects activity from Anthropic network range across multiple event types
Detects the creation of a new AWS S3 bucket followed by a large volume of data uploads (more than 200 objects) within a 2-hour window. The detection focuses on activity attributed to a single identity with limited source IP diversity, which is highly indicative of scripted data staging and potential exfiltration preparation by an unauthorized actor.
Detects the creation of a new AWS S3 bucket followed by a large volume of data uploads (more than 200 objects) within a 2-hour window. The detection focuses on activity attributed to a single identity with limited source IP diversity, which is highly indicative of scripted data staging and potential exfiltration preparation by an unauthorized actor.
Detects the creation of a new AWS S3 bucket followed by a large volume of data uploads (more than 200 objects) within a 2-hour window. The detection focuses on activity attributed to a single identity with limited source IP diversity, which is highly indicative of scripted data staging and potential exfiltration preparation by an unauthorized actor.
Identifies service principals or managed identities assigned high-privilege roles or permissions that exhibit anomalous access patterns by interacting with cloud resources not seen in the established historical baseline.
Detects the creation of new AWS access keys or login profiles for a target identity immediately following the deletion or modification of a credential for the same identity, where the actors performing the two actions are different. This pattern indicates an adversary creating persistent access after a security incident responder or automated system has revoked a previously compromised credential.
Detects high-frequency creation and revocation of service principal credentials, indicating potential malicious credential cycling, or unusual bursts of service account JWT/refresh token rotation across multiple IP addresses, which may indicate an attempt to evade detection or maintain persistence.
Detects anomalous, large-scale cloning or mirroring of code repositories combined with targeted access to infrastructure-as-code configuration files (e.g., .tfvars, .tfstate) or automated exfiltration of CI/CD build artifacts. This behavior is indicative of an adversary attempting to exfiltrate proprietary source code or sensitive secrets stored within repositories.
Detects anomalous, large-scale cloning or mirroring of code repositories combined with targeted access to infrastructure-as-code configuration files (e.g., .tfvars, .tfstate) or automated exfiltration of CI/CD build artifacts. This behavior is indicative of an adversary attempting to exfiltrate proprietary source code or sensitive secrets stored within repositories.
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.
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