Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects network activity related to the exploitation of the CVE-2025-31702 vulnerability in Dahua P2P Relay services. The rules monitor for probe attempts, device parameter fetching (randsalt), serial number enumeration bursts, and successful no-auth channel responses, indicating a bypass of authentication mechanisms on vulnerable Dahua devices.
This rule detects potential 'Quishing' (QR code phishing) attempts by identifying email-based phishing detection or malicious URL indicators followed by a user clicking the URL and subsequently authenticating to a corporate system from a mobile device (Android or iOS) within a 30-minute window. This behavior often suggests an adversary successfully harvesting credentials via a QR code link and then using them to gain unauthorized access.
Detects package publishing or version creation events on npm or PyPI that originate from non-CI/CD service accounts, suggesting the use of stolen registry authentication tokens by unauthorized entities to push malicious package versions.
This rule detects potential CI/CD security compromises in GitHub by monitoring for modifications to workflow permissions that grant 'write-all' access, access to sensitive workflow secrets via 'pull_request_target' from forked repositories, and the registration of new self-hosted runners. These actions are common indicators of supply chain attacks aimed at poisoning pipeline execution or exfiltrating build secrets.
Detects sensitive cPanel/WHM administrative commands or system file modifications occurring within one hour of a suspected authentication bypass event, indicative of potential post-exploitation activity related to CVE-2026-41940.
Detects sensitive cPanel/WHM administrative commands or system file modifications occurring within one hour of a suspected authentication bypass event, indicative of potential post-exploitation activity related to CVE-2026-41940.
Detects scenarios where a user registers a new WebAuthn/Passkey credential and subsequently performs a sign-in from a different network or device shortly after, preceded by a sign-in from a risky or non-passkey-based method. This pattern is often associated with credential abuse or MFA registration hijacking.
Detects scenarios where a user registers a new WebAuthn/Passkey credential and subsequently performs a sign-in from a different network or device shortly after, preceded by a sign-in from a risky or non-passkey-based method. This pattern is often associated with credential abuse or MFA registration hijacking.
Detects a suspicious sequence of events indicative of social engineering. An administrator or help-desk account resets a user's password, followed within 15 minutes by the target user performing a successful MFA authentication from a device or IP address not associated with that user in the preceding 30 days, suggesting an attacker hijacked the session or coerced the user after the password reset.
Detects scenarios where a user registers a new WebAuthn/Passkey credential and subsequently performs a sign-in from a different network or device shortly after, preceded by a sign-in from a risky or non-passkey-based method. This pattern is often associated with credential abuse or MFA registration hijacking.
Detects rapid, machine-speed, sequential attack-path adaptation by suspected AI agent binaries. The rule monitors for a correlation of four stages occurring within a tight 20-minute window: 1) Failed service connections across three or more distinct protocols (SMB, SSH, HTTP, RDP, WinRM), 2) Credential access attempts (Logon events or execution of known credential-dumping tools), and 3) A lateral movement pivot to a new, previously un-targeted internal destination. The rule specifically keys on the InitiatingProcessId and InitiatingProcessCreationTime to ensure the sequence is attributed to a single process instance, distinguishing it from human manual penetration testing activity. Covers T1190, T1210, T1078
Detects an anomalous burst of git clone, fetch, or download events performed by a single GitHub user account against a large number of private repositories within a short timeframe (15-minute window). This pattern often indicates unauthorized bulk collection or exfiltration of sensitive source code by an actor using a valid account.
This rule detects a potential vishing attack by correlating suspicious IT helpdesk requests (e.g., password or MFA reset without manager approval or verified callback) with subsequent sensitive Entra ID identity changes (e.g., self-service password resets, MFA device registration, or new device registration) occurring for the same user within a short time window. This activity is indicative of an attacker attempting to social-engineer helpdesk staff to bypass authentication controls and compromise a user account.
This rule detects the creation or modification of Exchange mailbox rules (Forward, Redirect, Delete, or Mark as Read) involving sensitive keywords (invoice, wire, payment, finance, remittance) shortly after an anomalous sign-in event or a password reset on the same account. This combination is a strong indicator of account takeover and mailbox persistence.
This rule identifies potential automated activity by monitoring for specific HTTP User-Agents (e.g., Python requests, Microsoft Graph client) associated with file and mail access events. It groups by UserId and UserAgent to detect anomalous patterns that may indicate unauthorized data access or interaction by automated tools or scripts.
This rule detects potentially unauthorized device registration or Intune enrollment activity by monitoring Azure Sign-in logs. It specifically flags accounts that exhibit rapid, multiple device registration attempts (>= 2 in 5 minutes) combined with suspicious indicators such as scripted User Agents ('python-requests'), the presence of Primary Refresh Tokens (PRT), specific device naming patterns, or concurrent Intune enrollment activity. These behaviors are common indicators of automated adversary activity aiming to bypass conditional access policies by enrolling illicit devices.
Detects login attempts to source code repositories and developer tooling (GitHub, GitLab, Bitbucket, Azure DevOps, Slack, Visual Studio Code) originating from unmanaged or non-compliant devices, excluding traffic originating from known corporate network ranges.
This rule detects suspicious activity related to specific hardcoded indicators 'p2pwn', 'p2password', and references to 'cve-2024-39943'. It scans authentication logs (Identity) and EDR logs (process command lines) for these terms, flagging potential unauthorized access, credential use, or exploitation attempts associated with this specific threat activity.
Detects the creation or unauthorized usage of the 'p2pwn' backdoor account and associated 'p2password' credentials on Dahua devices. This indicator is linked to exploitation of CVE-2024-39943, which allows for persistent unauthorized access that survives device factory resets.
Detects network packets targeting the Dahua SDK service (port 37777) that match the signature of a crafted login request, specifically checking for the 0xFF/0x00 magic header and the 0x00/0xA0 opcode. This pattern is commonly associated with unauthorized authentication attempts, including brute-force attacks or exploitation of known authentication bypass vulnerabilities in Dahua devices.
This rule detects a user account performing its first-ever GitHub OAuth or repository access within a short timeframe (30 minutes) after authenticating to a designated low-trust OpenAI community forum. This pattern is designed to identify potential adversary activity where a compromised or newly created account is used to pivot from a low-trust environment to corporate repository access.
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