Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the use of the default, hardcoded 'sk-1234' secret key for HS256 JWT signing in LiteLLM proxy logs. This configuration is insecure and allows for the forgery of arbitrary user session tokens.
Detects the use of the default, hardcoded 'sk-1234' secret key for HS256 JWT signing in LiteLLM proxy logs. This configuration is insecure and allows for the forgery of arbitrary user session tokens.
Detects the creation or update of LiteLLM custom code guardrails that include suspicious Python snippets, specifically 'import os' and 'os.popen', which are indicative of attempts to execute arbitrary OS commands via guardrail logic (CVE-2026-59821).
Detects the creation or update of LiteLLM custom code guardrails that include suspicious Python snippets, specifically 'import os' and 'os.popen', which are indicative of attempts to execute arbitrary OS commands via guardrail logic (CVE-2026-59821).
Detects anomalous access to Microsoft 365 services (OneDrive, SharePoint, Mail) where the user activity is characterized by a high volume of operations (more than 30) across a diverse set of objects (more than 15) within a one-hour window. The detection focuses on activity originating from specific common automation/scripting User-Agents (Microsoft Graph, Python httpx/requests) that deviate from typical, focused service account behavior.
Detects anomalous unauthenticated password reset requests targeting the Keycloak reset-credentials endpoint. The rule monitors for high-volume password reset attempts from single source IP addresses, specifically flagging those that target common administrative usernames or attempt to reset passwords across multiple distinct user accounts, which may indicate exploitation attempts (e.g., CVE-2026-18963).
Detects the installation of MSIX/AppX packages with full trust privileges which run with elevated privileges outside normal AppX container restrictions
This rule detects potential Server-Side Request Forgery (SSRF) activity where web application or runtime processes attempt to access the Cloud Instance Metadata Service (IMDS) or container task metadata endpoints. By monitoring network connections and application logs, the rule filters out known legitimate metadata clients and identifies suspicious processes frequently associated with web-based vulnerabilities that are repeatedly querying sensitive metadata paths.
Detects instances where a development-oriented web server (such as Vite or Node.js) is configured to bind to all network interfaces ('0.0.0.0') and is simultaneously receiving inbound connections from non-private, external IP addresses on a common development port (5173). This rule filters out common CI/CD environments to focus on potentially insecure exposure of development tools to the public internet.
This rule identifies successful user sign-ins (ResultType 0) originating from specific Autonomous System Numbers (197540, 216154) where Multi-Factor Authentication (MFA) was not required or not recorded, and where Conditional Access policies either did not apply or succeeded without enforcing MFA.
This rule identifies successful user sign-ins (ResultType 0) originating from specific Autonomous System Numbers (197540, 216154) where Multi-Factor Authentication (MFA) was not required or not recorded, and where Conditional Access policies either did not apply or succeeded without enforcing MFA.
Detects JFrog Artifactory access-token issuance events on/after the suspected compromise window (2026-08-28) where the token is tied to the anonymous identity, granted administrator scope, or has no attributable initiating user, per Fastly's remediation guidance.
Detects JFrog Artifactory access-token issuance events on/after the suspected compromise window (2026-08-28) where the token is tied to the anonymous identity, granted administrator scope, or has no attributable initiating user, per Fastly's remediation guidance.
Detects JFrog Artifactory access-token issuance events on/after the suspected compromise window (2026-08-28) where the token is tied to the anonymous identity, granted administrator scope, or has no attributable initiating user, per Fastly's remediation guidance.
Detects multiple successful GitLab authentication events from a specific source IP that contain parameters indicating an attempt to skip or bypass two-factor authentication, consistent with known GitLab 2FA bypass vulnerabilities.
Detects multiple successful GitLab authentication events from a specific source IP that contain parameters indicating an attempt to skip or bypass two-factor authentication, consistent with known GitLab 2FA bypass vulnerabilities.
Detects potential session replay or cookie theft attacks against Google Workspace accounts. The rule identifies anomalous session activity where a single session ID is associated with multiple country codes within a short timeframe (less than 60 minutes), suggesting that a stolen session token is being used by an adversary in a different location than the legitimate user.
Detects the installation of software packages in build or CI pipelines where the package was published to its registry between 30 and 90 days prior to being first used in the environment. This pattern is indicative of potential dependency confusion or typosquatting attacks, where adversaries use newly created packages to deliver malicious code.
Detects GitLab CI/CD pipeline configuration (.gitlab-ci.yml) that sets job artifacts to 'untracked: true' with 'when: always', which publishes every untracked file as a downloadable CI artifact -- the exact misconfiguration that exposed the leaked GitLab token in the GnuTLS incident.
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 the sensitive project-wide CI/CD variable API_TOKEN being exposed to job scopes other than the gitlab-triage automation job that is its only intended consumer, expanding the blast radius of the credential.
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