Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects execution of AWS CLI subcommands used for credential or identity enumeration (e.g., sts get-caller-identity, iam get-user) when spawned from Amazon Q-related or development environment processes (Node.js/VS Code). This behavior is indicative of potential malicious activity involving a compromised MCP server configuration file (CVE-2026-12957).
Detects the creation of GitHub Actions workflow files within the .github/workflows/ directory that mimic Dependabot naming conventions while containing suspicious indicators associated with the Miasma/Mini Shai-Hulud supply chain attack. Indicators include hardcoded secret references, specific operational markers like 'RevokeAndItGoesKaboom', and unauthorized OIDC token permissions.
Detects browser/node subprocesses spawned by AutoGen/Python processes making loopback WebSocket connections to the AutoGen Studio MCP control plane, which is indicative of the AutoJack exploit chain. This rule specifically looks for Python processes associated with 'autogen' or 'autogenstudio' that initiate network connections to loopback addresses on ports 8080 or 8081, often with specific URL paths related to the MCP WebSocket API.
Supply chain worm targeting npm packages and GitHub Actions, linked to Shai-Hulud/Hades lineage
Supply chain worm targeting npm packages and GitHub Actions, linked to Shai-Hulud/Hades lineage
Supply chain worm targeting npm packages and GitHub Actions, linked to Shai-Hulud/Hades lineage
Photo-themed ZIP phishing campaign dropping a Node.js implant
ClickFix style social engineering cluster delivering Mistic and ModeloRAT
Supply chain worm targeting npm packages and GitHub Actions, linked to Shai-Hulud/Hades lineage
Phishing-as-a-Service kit abusing OAuth device code flow against Microsoft 365
Phishing-as-a-Service kit abusing OAuth device code flow against Microsoft 365
Phishing-as-a-Service kit abusing OAuth device code flow against Microsoft 365
Detects suspicious execution of .scr (screensaver) files, execution of 'SecureMessage' named executables (potentially Pony malware), and email attachments containing .scr or .zip files, which are common vectors for malware delivery.
Detects POST requests to vLLM multimodal inference endpoints that contain 'video_url' fields pointing to external resources or specific video file extensions (.jp2, .mov, .mp4). This activity is indicative of exploitation attempts related to CVE-2026-22778, where an attacker might try to inject malicious video URLs to trigger vulnerabilities in the vLLM service.
This rule detects two stages of a potential exploitation chain targeting vLLM multimodal endpoints. Stage 1 identifies POST requests to vLLM API endpoints that contain 'video_url' in the request context, which could indicate an attempt to exploit CVE-2026-22778. Stage 2 detects post-exploitation activity by looking for unexpected child processes (like shell interpreters or download utilities) spawned from vLLM or Python inference processes, suggesting successful remote code execution.
AI-powered phishing-as-a-service abusing device-code authentication
AI-powered phishing-as-a-service abusing device-code authentication
AI-powered phishing-as-a-service abusing device-code authentication
Detects malicious emails bypassing spam filters due to a mismatch between the P1 (Envelope) and P2 (Header) sender addresses, exploiting routing misconfigurations. Specifically, it looks for emails where the 'SenderFromDomain' (P2 header) matches 'yourcompany.com' but differs from 'SenderMailFromDomain' (P1 envelope), and the email was successfully delivered.
Detects successful remote logons (LogonType 3 or 10) to Windows systems using highly privileged accounts (admin, administrator, root, or service-specific accounts) where the source IP address is external to defined private network CIDR ranges. This pattern is indicative of potential unauthorized access or compromised credentials.
This rule detects potentially malicious use of Managed Service Provider (MSP) credentials by correlating network or explicit authentication events (Logon Types 3, 9, or 10) occurring outside of typical business hours or originating from VPN/private network ranges, with concurrent SSH process activity on the same host. This pattern suggests an adversary may be using compromised credentials to move laterally or persist within an environment via SSH.
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