MCP Security Scanning Tool for CI/CD

Integrates security scanning directly into CI/CD pipelines via the Model Context Protocol (MCP), allowing LLM-powered agents to invoke vulnerability detection as a native tool rather than shell commands. Uses MCP's standardized resource and tool interfaces to expose scanning capabilities, enabling bidirectional communication between CI/CD orchestrators and security analysis engines without custom API wrappers or subprocess management.

Leverages LLM reasoning to automatically prioritize and contextualize security findings based on code impact, exploitability, and business context. The agent analyzes vulnerability metadata (CVSS, CWE, affected code paths) alongside codebase semantics to generate ranked remediation steps, suggesting patches or architectural changes rather than just listing CVEs. Uses chain-of-thought reasoning to explain why certain vulnerabilities pose higher risk in the specific codebase.

Implements configurable security policies as MCP tools that block or warn on CI/CD pipeline execution based on scanning results. Policies are expressed as declarative rules (e.g., 'fail if any critical CVE in production dependencies') and evaluated by the agent before deployment. Integrates with standard CI/CD webhooks to enforce gates without modifying pipeline YAML, using MCP as the policy evaluation and decision-making layer.

Orchestrates multiple security scanners (SAST, DAST, dependency checkers, container scanners) via MCP and deduplicates findings across tools using semantic matching and fingerprinting. Normalizes output from heterogeneous scanners (different JSON schemas, severity scales, CWE mappings) into a unified vulnerability model, then uses LLM-based deduplication to identify duplicate findings across tools while preserving scanner-specific metadata.

Analyzes project dependencies (direct and transitive) to identify supply chain risks beyond known CVEs, including unmaintained packages, suspicious version jumps, typosquatting candidates, and license compliance issues. Uses LLM reasoning to correlate dependency metadata (maintainer activity, GitHub stars, commit frequency, dependency graph depth) with risk signals, generating a supply chain risk score that factors in both security and operational stability.

Scans source code, configuration files, and CI/CD logs for exposed secrets (API keys, database passwords, tokens, private keys) using pattern matching, entropy analysis, and LLM-based semantic detection. Distinguishes between actual secrets and false positives (test credentials, example values) by analyzing context and usage patterns. Integrates with secret management systems to verify if detected credentials are still active and should be rotated.

Scans OCI container images for vulnerabilities in base OS layers, application dependencies, and misconfigurations (exposed ports, root user, missing security capabilities). Analyzes image layers to identify which packages introduce vulnerabilities and suggests base image upgrades or dependency patches. Integrates with container registries (Docker Hub, ECR, GCR) to scan images before deployment and tracks image provenance via SBOM (Software Bill of Materials).

Scans Terraform, CloudFormation, Kubernetes manifests, and other IaC files for security misconfigurations (overly permissive IAM policies, unencrypted storage, exposed databases, missing network segmentation). Uses policy-as-code rules (similar to Checkov, TFLint) but enhances them with LLM reasoning to understand intent and context — can recognize that a permissive security group is intentional for a dev environment but risky in production.

Maps detected vulnerabilities and misconfigurations to compliance frameworks (OWASP Top 10, CWE, CVSS, PCI-DSS, HIPAA, SOC 2, ISO 27001) and generates compliance reports. Uses LLM reasoning to determine which findings are relevant to specific compliance requirements and prioritizes remediation based on regulatory impact. Tracks compliance status over time and generates audit-ready reports for compliance teams.

Exposes all scanning and remediation capabilities as callable MCP tools that LLM agents can invoke autonomously as part of multi-step workflows. Enables agents to orchestrate security operations (scan → triage → recommend → generate PR) without human intervention, using natural language planning and reasoning to make decisions about vulnerability remediation.