Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
1,235 detections
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Detects autonomous behavior where AI-driven processes attempt multiple network connections (SMB, SSH, HTTP, RDP, WinRM) to diverse targets following failed access attempts. The rule correlates initial failure activity with subsequent credential-related events and new lateral movement traffic from the same agent process within a short window, suggesting adaptive adversary pivoting.
Detects an suspicious progression of activities originating from identified AI assistant/agent processes. The rule monitors for a chain of behaviors occurring within a 15-minute window: execution of an AI agent/tool, followed by host discovery (network/system enumeration), access to sensitive local credential files, archival of data, and outbound network communication to common file-sharing/exfiltration platforms.
Detects successful sign-ins to Azure AI/ML related administrative consoles (such as Azure OpenAI, Azure Machine Learning, and Cognitive Services) from different countries within a short time window (4 hours), which is indicative of potential account compromise or credential abuse.
Detects anomalous authentication patterns where multiple distinct user identities authenticate from the same source IP (often a VPN or proxy) within a short timeframe, characteristic of North Korean IT worker laptop-farm or identity-mule operations where a single operator manages multiple contractor accounts.
Detects a credential stuffing attack characterized by a high volume of failed authentication attempts from a small set of source IP addresses followed by a successful authentication within a short timeframe. This behavior is indicative of an attacker attempting to use valid credentials obtained from infostealer logs or credential leaks to gain unauthorized access to accounts.
Detects a suspicious pattern consistent with rapid handoff from an initial access broker to an operator: a user account is added to a privileged group (e.g., Domain Admins) followed within 5 minutes by successful logins to 3 or more distinct hosts. This behavior indicates immediate privilege escalation followed by rapid lateral movement.
Detects activity from known Storm-3168 (JADEPUFFER) campaign IP addresses across Azure control-plane, sign-in, and App Service logs. The rule monitors for malicious indicators in Azure Activity logs, Azure Sign-in logs, and Service Principal sign-ins, as well as specific sensitive URI paths (e.g., shells, admin login paths) within App Service HTTP logs.
Detects Azure AD / Entra ID sign-in events that utilize the OAuth 2.0 device code authentication flow. This protocol, designed for input-constrained devices, is frequently exploited by adversary-in-the-middle phishing kits (e.g., EvilTokens) to trick users into authorizing a malicious session, effectively bypassing traditional MFA by obtaining an active session token.
Detects post-exploitation indicators of CVE-2026-62911, an authentication-bypass-by-capture-replay vulnerability in Microsoft Exchange. The rule identifies anomalous behavior following a potential bypass: either the assignment of the ApplicationImpersonation management role or a single impersonation session accessing an abnormally high number of distinct mailboxes (FolderBind, MessageBind, or SendAs).
Detects the presence of an unauthorized administrative account (e.g., 'ops') on MikroTik RouterOS devices following a system 'Flagged' indicator. This rule correlates administrative account creation or modification events with system-reported compromise indicators to identify post-exploitation persistence mechanisms related to known RouterOS vulnerabilities.
Detects a multi-stage, automated attack chain performed by AI-based development agents (e.g., Claude, Copilot, Cursor). The rule correlates process discovery, credential access, file collection (staging), and exfiltration over web services within a tight 15-minute window, identifying potential autonomous exploitation and data exfiltration. Covered T1046, T1552.001, T1078, T1005, T1567
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.
Detects impossible travel activity for Azure AD accounts by calculating the speed between consecutive sign-in events using the Haversine formula. The rule correlates sign-ins sharing the same SessionId or DeviceId and identifies instances where the calculated travel speed exceeds 900 km/h over a distance greater than 300 km. It includes filtering to exclude known corporate VPN egress points to reduce noise.
Detects impossible travel activity for Azure AD accounts by calculating the speed between consecutive sign-in events using the Haversine formula. The rule correlates sign-ins sharing the same SessionId or DeviceId and identifies instances where the calculated travel speed exceeds 900 km/h over a distance greater than 300 km. It includes filtering to exclude known corporate VPN egress points to reduce noise.
Detects the creation of GCP compute instances that are attached to service accounts with broad 'cloud-platform' scope permissions. This activity is monitored to ensure that only authorized automation service accounts (e.g., IaC pipelines) are performing these actions, as creating instances with highly privileged service accounts can be a technique used to escalate privileges or establish persistence.
Detects anomalous, high-velocity authentication activity where a single identity accesses multiple high-value SaaS applications (such as CRM, DevOps, or admin consoles) in a short time frame, correlated with risk signals indicative of token reuse or 'pass-the-cookie' attacks.
Detects potential lateral movement by users who have recently been flagged for credential theft or infostealer activity. The rule correlates initial security alerts with subsequent Windows logon events (RDP, network/WinRM) or SMB share access, identifying users connecting to multiple distinct destinations within a 48-hour window.
Detects anomalous authentication patterns where multiple distinct user identities authenticate from the same source IP (often a VPN or proxy) within a short timeframe, characteristic of North Korean IT worker laptop-farm or identity-mule operations where a single operator manages multiple contractor accounts.
Detects user authentication events to recruitment, career, or applicant-tracking applications where the connection originates from an anonymizing proxy, VPN, or datacenter IP address. This behavior has been observed as a precursor to North Korean IT worker recruitment fraud, where attackers mask their true geographic location to gain unauthorized employment.
Detects user account sign-in attempts originating from a country not observed in the previous 30 days, or from an ISP flagged as a VPN, proxy, or hosting/cloud provider. This behavior is consistent with an adversary utilizing stolen credentials or session tokens through anonymity infrastructure to bypass conditional access policies.
Detects anomalous AWS activity combining bulk secret retrieval from AWS Secrets Manager using the BatchGetSecretValue API with subsequent authentication failures or validation errors in AWS STS. This behavior is indicative of automated credential harvesting pipelines or agentic frameworks rapidly enumerating and testing stolen credentials.
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