agent-bom

Security scanner and graph for AI supply chain and infrastructure — agents, MCP, runtime, and blast radius.

От сообществаРаботаетБез входаГлобальныйБесплатноТолько чтение

Что умеет

  • Scan: Run a full AI supply chain security scan and return an AI-BOM. Point it at a target with one of: • repo_url — a public git repo URL (cloned + scanned, no checkout) • config_path — a local projec
  • Check: Check a specific package for known CVEs before installing. Queries OSV.dev for vulnerabilities in the given package. Use this before installing an MCP server or dependency to verify it is safe.
  • Intel Lookup: Look up one advisory from the local threat-intel database.

Какие данные видит

Нужен ли аккаунт

Не нужен: сервер работает без входа

Security scanner and graph for AI supply chain and infrastructure — agents, MCP, runtime, and blast radius.

Список инструментов сервера (8)

Технические названия из tools/list. Нужны только разработчикам.

scanRun a full AI supply chain security scan and return an AI-BOM. Point it at a target with one of: • repo_url — a public git repo URL (cloned + scanned, no checkout) • config_path — a local project / MCP-config directory • image — a Docker image • sbom_path — an existing CycloneDX/SPDX SBOM • package — a single package or MCP launch command (pair it with ``ecosystem`` when the spec names no launcher) With none of these, it auto-discovers local MCP clients (Claude Desktop, Cursor, Windsurf, VS Code Copilot, OpenClaw, etc.). It extracts package dependencies, queries OSV.dev for CVEs, assesses config security (credential exposure, tool access), computes blast radius, and returns structured results. Scanning is fully static and read-only — repository and image contents are parsed, never executed. Returns: JSON with the complete AI-BOM report including agents, packages, vulnerabilities, blast radius, and remediation guidance.
checkCheck a specific package for known CVEs before installing. Queries OSV.dev for vulnerabilities in the given package. Use this before installing an MCP server or dependency to verify it is safe. Args: package: Package name with optional version, e.g. "express@4.18.2", "@modelcontextprotocol/server-filesystem@2025.1.14", or just "requests" (resolves @latest). ecosystem: Package ecosystem — "npm", "pypi", "go", "cargo", "maven", "nuget", "rubygems", "composer", "swift", "pub", "hex", "conda", "deb", "apk", or "rpm". Defaults to "npm". Returns: JSON with package, version, ecosystem, vulnerability count, and vulnerability details (id, severity, cvss, fix version, summary).
intel_lookupLook up one advisory from the local threat-intel database.
exposure_pathsReturn ranked ExposurePath JSON for headless security agents. This is the agent-native graph surface: Claude, Cursor, Codex, Windsurf, Cortex, and other MCP clients can request the same investigation objects used by the dashboard without scraping UI state.
policy_checkEvaluate a security policy against current scan results. Runs a scan, then evaluates the provided policy rules against the findings. Policies can gate on severity thresholds, CISA KEV status, AI risk flags, credential exposure, and denied packages. Args: policy_json: JSON string containing policy rules. Example: {"rules": [{"id": "no-critical", "severity_gte": "critical", "action": "fail"}, {"id": "no-kev", "kev": true, "action": "fail"}]} Returns: JSON with passed (bool), violations list, failure_count, and warning_count.
generate_sbomGenerate a Software Bill of Materials (SBOM) for your AI agent setup. Discovers AI agents and MCP servers, extracts all package dependencies, and generates a standards-compliant SBOM. Args: format: SBOM format — "cyclonedx" (CycloneDX 1.7) or "spdx" (SPDX 3.0). config_path: Path to a specific MCP config directory. If not provided, auto-discovers all local agent configs. Returns: JSON string containing the SBOM in the requested format.
complianceGet OWASP LLM Top 10 / OWASP MCP Top 10 / MITRE ATLAS / NIST AI RMF compliance posture. Scans local MCP configurations, maps findings to 47 security controls across four AI security frameworks, and returns per-control pass/warning/fail status with an overall compliance score. Args: config_path: Path to a specific MCP config directory. If not provided, auto-discovers all local agent configs. image: Docker image reference to scan (e.g. "nginx:1.25"). Returns: JSON with overall_score (0-100), overall_status (pass/warning/fail/no_data), and per-control details for OWASP LLM Top 10 (10 controls), OWASP MCP Top 10 (10 controls), MITRE ATLAS (13 techniques), and NIST AI RMF (14 subcategories). Plus a nist_800_53_catalog line: the vendor-asserted, catalog-backed NIST SP 800-53 Rev 5 score over evaluated controls only (with ISO-27001-by-id attribution), scored independently and NOT folded into overall_score.
remediateGenerate a remediation plan for vulnerabilities in your AI agent setup. Scans for vulnerabilities, then generates actionable fix commands for each affected package (npm install, pip install), credential scope reduction guidance, and reports on unfixable vulnerabilities. Args: config_path: Path to a specific MCP config directory. If not provided, auto-discovers all local agent configs. image: Docker image reference to scan (e.g. "nginx:1.25"). Returns: JSON with package_fixes (upgrade commands by ecosystem), credential_fixes (scope reduction steps), and unfixable items.