{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-46050507","slug":"mcp-security-scanning-tool-for-ci-cd","name":"MCP Security Scanning Tool for CI/CD","type":"mcp","url":"https://smart.mcpshark.sh/","page_url":"https://unfragile.ai/mcp-security-scanning-tool-for-ci-cd","categories":["mcp-servers","code-review-security"],"tags":["hackernews","show-hn"],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-46050507__cap_0","uri":"capability://tool.use.integration.mcp.native.security.vulnerability.scanning","name":"mcp-native security vulnerability scanning","description":"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.","intents":["Embed security scanning into LLM-driven CI/CD workflows without writing custom integrations","Allow AI agents to autonomously trigger and interpret security scans during code review","Standardize security tool invocation across heterogeneous CI/CD systems via MCP protocol"],"best_for":["Teams building LLM-powered CI/CD agents that need native security tooling","Organizations standardizing on MCP for tool orchestration across development workflows","DevSecOps teams automating security gates in agent-driven pipelines"],"limitations":["Requires MCP-compatible CI/CD orchestrator or agent framework — not compatible with legacy Jenkins/GitLab CI without adapter layer","Scanning performance depends on underlying security engine; MCP protocol overhead adds ~50-200ms per invocation","No built-in result persistence — requires external logging/SIEM integration for audit trails"],"requires":["MCP server runtime (Claude Desktop, Cline, or compatible MCP host)","CI/CD system with MCP client support or custom MCP bridge","Network access to security scanning backend or local scanner installation"],"input_types":["code repositories (git URLs, local paths)","dependency manifests (package.json, requirements.txt, go.mod)","container images (OCI image references)","configuration files (YAML, JSON, HCL)"],"output_types":["structured vulnerability reports (JSON/XML)","severity-ranked findings with CVSS scores","remediation guidance and patch recommendations","compliance mapping (CWE, OWASP, PCI-DSS)"],"categories":["tool-use-integration","ci-cd-security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46050507__cap_1","uri":"capability://planning.reasoning.agentic.vulnerability.triage.and.remediation.recommendation","name":"agentic vulnerability triage and remediation recommendation","description":"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.","intents":["Automatically prioritize which vulnerabilities to fix first based on actual code exposure","Generate context-aware remediation steps tailored to the project's architecture","Reduce security alert fatigue by filtering false positives and low-impact findings"],"best_for":["Development teams drowning in security alerts from traditional scanners","Small teams without dedicated security engineers who need intelligent triage","Organizations wanting AI-assisted security reviews integrated into code review workflows"],"limitations":["LLM reasoning quality depends on code context window — large codebases may require selective indexing","Cannot guarantee remediation suggestions are optimal or production-ready without human review","Reasoning latency (3-10s per vulnerability set) may be too slow for real-time blocking gates","Requires sufficient context about business logic to assess true risk — may over/under-prioritize in unfamiliar domains"],"requires":["LLM with function calling support (Claude 3+, GPT-4, etc.)","Access to codebase AST or semantic analysis (via tree-sitter or language server)","Vulnerability database with structured metadata (NVD, GitHub Advisory, Snyk)"],"input_types":["raw vulnerability findings (CVE ID, CWE, affected package/version)","source code snippets (affected functions, call chains)","dependency graph (to assess transitive exposure)","business context (criticality labels, deployment environment)"],"output_types":["prioritized vulnerability list with risk scores","remediation steps (code patches, dependency updates, architectural changes)","false positive filtering (marked as 'not exploitable in this context')","explanation chains (why this vulnerability matters in this codebase)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46050507__cap_2","uri":"capability://automation.workflow.ci.cd.pipeline.security.gate.enforcement.via.mcp","name":"ci/cd pipeline security gate enforcement via mcp","description":"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.","intents":["Enforce security policies in CI/CD without hardcoding checks into pipeline configuration","Allow security teams to update policies dynamically without redeploying CI/CD infrastructure","Make policy decisions explainable by having the agent reason through violations"],"best_for":["Enterprise teams needing centralized, auditable security policy enforcement","Organizations with multiple CI/CD systems (GitHub Actions, GitLab CI, Jenkins) wanting unified policy","Compliance-heavy industries (fintech, healthcare) requiring documented policy decisions"],"limitations":["Policy evaluation latency (2-5s) may delay CI/CD pipelines if gates are synchronous","Requires MCP client in CI/CD orchestrator — not natively supported by all platforms","Policy language expressiveness limited by LLM reasoning capabilities — complex boolean logic may be unreliable","No built-in rollback or exception workflow — requires manual override mechanism"],"requires":["MCP-compatible CI/CD system or custom webhook bridge","Policy definition format (JSON, YAML, or natural language)","Audit logging system to record policy decisions and overrides"],"input_types":["scanning results (vulnerability list, severity distribution)","deployment metadata (target environment, change scope, approvers)","policy definitions (rules, thresholds, exceptions)","historical context (previous deployments, incident data)"],"output_types":["pass/fail/warn decision with justification","policy violation details (which rule triggered, why)","remediation requirements (what must be fixed to proceed)","audit log entry (decision, timestamp, approver)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46050507__cap_3","uri":"capability://data.processing.analysis.multi.scanner.aggregation.and.deduplication","name":"multi-scanner aggregation and deduplication","description":"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.","intents":["Run multiple security tools in parallel and get a single deduplicated report","Avoid alert fatigue from the same vulnerability reported by multiple scanners","Maintain visibility into which scanners detected each vulnerability for validation"],"best_for":["Organizations with existing investments in multiple security tools","Teams wanting to compare scanner coverage without manual reconciliation","Security platforms building unified dashboards across heterogeneous tooling"],"limitations":["Deduplication accuracy depends on scanner output quality — some tools provide insufficient metadata for reliable matching","LLM-based matching adds latency (5-15s for large finding sets) and cost per scan","Requires scanner-specific adapters to normalize output — adding new scanners requires development","Cannot deduplicate findings from scanners with fundamentally different detection methods (e.g., SAST vs runtime monitoring)"],"requires":["MCP adapters for each scanner (or REST API bridges)","Unified vulnerability schema (e.g., SARIF, custom JSON)","LLM with semantic understanding of code vulnerabilities"],"input_types":["raw scanner outputs (JSON, XML, CSV from SAST, DAST, dependency tools)","scanner metadata (tool name, version, detection method)","deduplication rules (fingerprinting algorithms, similarity thresholds)"],"output_types":["unified vulnerability list with scanner attribution","deduplication groups (findings identified as the same issue)","confidence scores for deduplication matches","scanner coverage analysis (which tools detected which vulnerability types)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46050507__cap_4","uri":"capability://data.processing.analysis.dependency.supply.chain.risk.assessment","name":"dependency supply chain risk assessment","description":"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.","intents":["Identify risky dependencies before they cause security incidents or maintenance headaches","Detect typosquatting and dependency confusion attacks in package manifests","Assess license compliance risks across the entire dependency tree"],"best_for":["Teams managing large, complex dependency trees (100+ direct dependencies)","Organizations with strict supply chain security requirements (defense, finance)","Open source projects wanting to reduce downstream security risks"],"limitations":["Requires real-time access to package registry metadata (npm, PyPI, Maven) — may hit rate limits","Risk scoring heuristics are opinionated — may flag legitimate niche packages as risky","Cannot detect zero-day compromises in package registries (requires behavioral monitoring)","Transitive dependency analysis is expensive at scale — may require sampling for 1000+ dependencies"],"requires":["Package registry API access (npm, PyPI, Maven, etc.)","Dependency resolution engine (npm, pip, Maven, Gradle)","GitHub API access for maintainer activity analysis (optional but recommended)"],"input_types":["dependency manifests (package.json, requirements.txt, go.mod, pom.xml)","lock files (package-lock.json, Pipfile.lock, go.sum)","package registry metadata (version history, maintainer info, download stats)","license information (SPDX identifiers, license text)"],"output_types":["supply chain risk scores (per dependency and aggregate)","risk factors (unmaintained, suspicious activity, license issues, typosquatting)","recommendations (upgrade, replace, audit, remove)","dependency graph visualization with risk coloring"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46050507__cap_5","uri":"capability://safety.moderation.secrets.and.credential.detection.in.code.and.configs","name":"secrets and credential detection in code and configs","description":"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.","intents":["Prevent accidental credential commits before they reach the repository","Identify and remediate already-exposed secrets in git history","Verify that detected credentials are revoked or rotated"],"best_for":["Teams with high commit velocity wanting to catch secrets before push","Organizations auditing existing repositories for exposed credentials","Security teams managing credential rotation and revocation workflows"],"limitations":["Pattern-based detection has high false positive rate — requires manual review or ML training","Cannot detect secrets in encrypted or obfuscated formats","Entropy-based detection is unreliable for short secrets or those with low entropy","Verifying credential validity requires access to secret management systems — not always available","Removing secrets from git history requires force-push or BFG Repo-Cleaner — disruptive to teams"],"requires":["Git repository access (local or via API)","Pattern database (regex rules for common secret formats)","Optional: secret management system API (Vault, AWS Secrets Manager) for verification"],"input_types":["source code files (all languages)","configuration files (YAML, JSON, HCL, .env)","CI/CD logs and build artifacts","git commit history and diffs"],"output_types":["detected secrets with location (file, line, commit)","confidence scores (high/medium/low based on pattern strength)","false positive indicators (test credentials, example values)","remediation steps (revoke, rotate, remove from history)"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46050507__cap_6","uri":"capability://data.processing.analysis.container.and.image.security.scanning","name":"container and image security scanning","description":"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).","intents":["Identify vulnerabilities in container images before deployment to production","Determine which base image or dependency version introduces a vulnerability","Enforce container security policies (no root user, minimal base images, signed images)"],"best_for":["Teams deploying containerized applications with strict security requirements","Organizations managing large container registries needing automated scanning","DevOps teams wanting to shift security left in the container build pipeline"],"limitations":["Scanning large images (1GB+) is slow and resource-intensive — requires caching or sampling","Vulnerability data for container images lags behind source package databases","Cannot detect vulnerabilities in custom-built binaries or proprietary code in images","Layer-by-layer analysis is complex — determining which layer introduced a vulnerability requires full image reconstruction"],"requires":["Container image access (local, registry API, or image tarball)","OCI image format support (Docker, Podman, containerd)","Vulnerability database for OS packages (Alpine, Debian, CentOS, etc.)"],"input_types":["OCI image references (registry.example.com/image:tag)","image tarballs (docker save output)","Dockerfile or image build configuration","SBOM files (CycloneDX, SPDX format)"],"output_types":["vulnerability list with layer attribution","base image recommendations (upgrade to patched version)","dependency upgrade suggestions (for application packages in image)","configuration issues (security best practices violations)","SBOM generation (for supply chain transparency)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46050507__cap_7","uri":"capability://code.generation.editing.infrastructure.as.code.iac.security.misconfiguration.detection","name":"infrastructure-as-code (iac) security misconfiguration detection","description":"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.","intents":["Catch infrastructure security misconfigurations before deployment","Enforce security baselines across infrastructure code without manual review","Provide context-aware remediation suggestions (not just 'deny all traffic')"],"best_for":["Teams using infrastructure-as-code for cloud deployments (AWS, Azure, GCP)","Organizations wanting to shift security left in the infrastructure pipeline","DevOps teams managing complex multi-environment deployments"],"limitations":["IaC scanning rules are opinionated — may flag legitimate configurations as insecure","Context awareness requires understanding deployment environment and business logic — may miss subtle misconfigurations","Requires parsers for each IaC language (Terraform, CloudFormation, Kubernetes, etc.) — not all formats equally supported","Cannot detect runtime misconfigurations or drift from deployed state"],"requires":["IaC file access (Terraform, CloudFormation, Kubernetes YAML, etc.)","Policy rule definitions (built-in or custom)","Optional: cloud provider API access for context (environment type, tags, etc.)"],"input_types":["Terraform files (.tf, .tfvars)","CloudFormation templates (JSON, YAML)","Kubernetes manifests (YAML)","Ansible playbooks, Docker Compose, Helm charts","environment metadata (dev/staging/prod labels)"],"output_types":["misconfiguration findings with severity","affected resources and properties","remediation code (corrected IaC snippet)","policy rule reference (which rule triggered)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46050507__cap_8","uri":"capability://data.processing.analysis.compliance.and.regulatory.mapping","name":"compliance and regulatory mapping","description":"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.","intents":["Understand which security findings impact specific compliance requirements","Generate compliance reports for auditors and regulators","Prioritize security work based on compliance deadlines and requirements"],"best_for":["Regulated industries (fintech, healthcare, government) with strict compliance requirements","Organizations preparing for audits or certifications","Security teams needing to communicate risk to compliance and executive stakeholders"],"limitations":["Compliance mappings are opinionated and may not reflect your specific regulatory interpretation","Requires domain expertise to validate compliance relevance — LLM reasoning may be incorrect","Compliance requirements change frequently — mappings require regular updates","Cannot guarantee compliance based on security findings alone — requires operational controls and policies"],"requires":["Vulnerability and misconfiguration findings (from other scanning capabilities)","Compliance framework definitions (OWASP, CWE, PCI-DSS, etc.)","Organizational compliance scope (which frameworks apply)"],"input_types":["vulnerability findings with CWE/CVE identifiers","misconfiguration findings with policy rules","compliance framework selection (which standards apply)","organizational context (industry, data types, regulations)"],"output_types":["compliance mapping (finding → framework requirement)","compliance status dashboard (% compliant per framework)","audit reports (findings grouped by compliance requirement)","remediation roadmap (prioritized by compliance deadline)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46050507__cap_9","uri":"capability://tool.use.integration.integration.with.llm.agents.for.autonomous.security.workflows","name":"integration with llm agents for autonomous security workflows","description":"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.","intents":["Enable LLM agents to autonomously manage vulnerability remediation workflows","Automate security decision-making based on vulnerability context and policy","Generate and propose security fixes as pull requests without manual intervention"],"best_for":["Teams building LLM-powered security automation agents","Organizations seeking to reduce manual security review overhead","DevOps teams implementing autonomous remediation workflows"],"limitations":["LLM decision-making may not align with organizational risk tolerance — requires careful prompt engineering and guardrails","Autonomous PR generation may introduce unintended side effects — requires code review before merge","Requires careful permission scoping to prevent agents from making unauthorized changes"],"requires":["LLM with MCP client support (Claude, GPT-4, etc.)","MCP server exposing security scanning tools","Git repository write access for PR generation"],"input_types":["natural language instructions for security workflows","repository context and configuration"],"output_types":["scan results and remediation recommendations","generated pull requests","workflow execution logs"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["MCP server runtime (Claude Desktop, Cline, or compatible MCP host)","CI/CD system with MCP client support or custom MCP bridge","Network access to security scanning backend or local scanner installation","LLM with function calling support (Claude 3+, GPT-4, etc.)","Access to codebase AST or semantic analysis (via tree-sitter or language server)","Vulnerability database with structured metadata (NVD, GitHub Advisory, Snyk)","MCP-compatible CI/CD system or custom webhook bridge","Policy definition format (JSON, YAML, or natural language)","Audit logging system to record policy decisions and overrides","MCP adapters for each scanner (or REST API bridges)"],"failure_modes":["Requires MCP-compatible CI/CD orchestrator or agent framework — not compatible with legacy Jenkins/GitLab CI without adapter layer","Scanning performance depends on underlying security engine; MCP protocol overhead adds ~50-200ms per invocation","No built-in result persistence — requires external logging/SIEM integration for audit trails","LLM reasoning quality depends on code context window — large codebases may require selective indexing","Cannot guarantee remediation suggestions are optimal or production-ready without human review","Reasoning latency (3-10s per vulnerability set) may be too slow for real-time blocking gates","Requires sufficient context about business logic to assess true risk — may over/under-prioritize in unfamiliar domains","Policy evaluation latency (2-5s) may delay CI/CD pipelines if gates are synchronous","Requires MCP client in CI/CD orchestrator — not natively supported by all platforms","Policy language expressiveness limited by LLM reasoning capabilities — complex boolean logic may be unreliable","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36,"quality":0.3,"ecosystem":0.31,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.326Z","last_scraped_at":"2026-05-04T08:10:01.171Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-security-scanning-tool-for-ci-cd","compare_url":"https://unfragile.ai/compare?artifact=mcp-security-scanning-tool-for-ci-cd"}},"signature":"T3vTjDhbSl//yueN77oVX7kLnYc67tTPnzqUbwuDf83zdL37Vw6l+EygmfjUmCf/NgwQc4+9PMXDR4qJZEUdAw==","signedAt":"2026-06-20T15:18:19.000Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-security-scanning-tool-for-ci-cd","artifact":"https://unfragile.ai/mcp-security-scanning-tool-for-ci-cd","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-security-scanning-tool-for-ci-cd","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}