{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-fuzzinglabs-mcp-security-hub","slug":"mcp-fuzzinglabs-mcp-security-hub","name":"mcp-security-hub","type":"mcp","url":"https://github.com/FuzzingLabs/mcp-security-hub","page_url":"https://unfragile.ai/mcp-fuzzinglabs-mcp-security-hub","categories":["mcp-servers","code-review-security"],"tags":["ai","claude","cybersecurity","docker","ghidra","mcp","mcp-server","nmap","nuclei","offensive-security","osint","pentesting","security","vulnerability-scanner"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-fuzzinglabs-mcp-security-hub__cap_0","uri":"capability://tool.use.integration.network.reconnaissance.via.nmap.mcp","name":"network-reconnaissance-via-nmap-mcp","description":"Exposes Nmap network scanning capabilities through MCP protocol, allowing AI assistants to execute port scans, service enumeration, and OS detection against target hosts. The implementation wraps Nmap's command-line interface as MCP tools, translating natural language scan requests into structured Nmap arguments (scan types, port ranges, timing templates, output formats) and parsing machine-readable XML output back into assistant-consumable structured data.","intents":["I need to scan a target network to identify open ports and running services","Help me enumerate what's listening on a specific host before I attempt exploitation","I want to run aggressive OS fingerprinting against a range of IPs and get structured results"],"best_for":["penetration testers integrating reconnaissance into AI-assisted workflows","security researchers automating network discovery tasks","red teams needing rapid network enumeration with AI-driven analysis"],"limitations":["Nmap execution requires root/admin privileges for certain scan types (SYN, OS detection), limiting deployment in restricted environments","Large-scale scans (Class B networks) may timeout or consume excessive resources in cloud-hosted MCP deployments","Output parsing depends on Nmap version consistency; version mismatches may break structured result extraction"],"requires":["Nmap 7.80+ installed and in system PATH","Root or administrator privileges for privileged scan types","Python 3.8+ for MCP server runtime"],"input_types":["target IP/hostname (string)","port range specification (e.g., '1-65535', '80,443,8080')","scan type flag (e.g., 'sS', 'sV', 'sO')","timing template (T0-T5)"],"output_types":["structured JSON with open ports, services, versions","XML-formatted Nmap output","plain text summary"],"categories":["tool-use-integration","security-scanning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-fuzzinglabs-mcp-security-hub__cap_1","uri":"capability://tool.use.integration.vulnerability.scanning.with.nuclei.templates","name":"vulnerability-scanning-with-nuclei-templates","description":"Integrates Nuclei vulnerability scanner as an MCP tool, enabling AI assistants to execute templated security checks against web applications and infrastructure. The implementation manages Nuclei's template library, translates high-level vulnerability categories (OWASP Top 10, CVE patterns, misconfiguration checks) into template selectors, executes scans with configurable severity filters, and returns structured vulnerability findings with remediation context.","intents":["Scan a web application for common vulnerabilities (SQLi, XSS, CSRF) without manual template selection","Check infrastructure for known misconfigurations (exposed S3 buckets, default credentials, weak TLS)","Run templated checks for a specific CVE across multiple targets and aggregate results"],"best_for":["security teams automating vulnerability assessment workflows","developers integrating security scanning into CI/CD pipelines via AI orchestration","bug bounty hunters scaling reconnaissance across multiple targets"],"limitations":["Nuclei template accuracy depends on community-maintained templates; false positives increase with permissive severity settings","Scanning large target lists sequentially can be slow; parallel execution requires careful rate-limiting to avoid target DoS","Template updates require manual refresh or integration with Nuclei's update mechanism; stale templates miss new CVEs"],"requires":["Nuclei 2.8.0+ installed and in system PATH","Network connectivity to target hosts","Python 3.8+ for MCP server runtime","Optional: API keys for enhanced template features (GitHub, Shodan)"],"input_types":["target URL or IP (string)","vulnerability category filter (e.g., 'cve', 'owasp', 'misconfig')","severity threshold (critical, high, medium, low)","template tags (custom filtering)"],"output_types":["structured JSON with vulnerability findings (type, severity, affected parameter, remediation)","SARIF format for IDE integration","plain text report"],"categories":["tool-use-integration","security-scanning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-fuzzinglabs-mcp-security-hub__cap_10","uri":"capability://planning.reasoning.ai.guided.tool.parameter.optimization","name":"ai-guided-tool-parameter-optimization","description":"Enables AI assistants to optimize tool parameters (scan intensity, detection sensitivity, resource allocation) based on target characteristics, time constraints, and risk tolerance. The implementation profiles target properties (network size, application complexity, infrastructure scale), recommends optimal tool parameters, and adjusts parameters dynamically based on intermediate results and feedback.","intents":["Automatically tune Nmap scan intensity based on target network size and time constraints","Adjust Nuclei template severity and coverage based on application type and risk profile","Optimize SQLMap risk/level parameters to balance detection accuracy and WAF evasion"],"best_for":["security teams automating parameter tuning for diverse targets","penetration testers optimizing assessment speed without sacrificing coverage","organizations standardizing tool configuration across assessments"],"limitations":["Parameter optimization heuristics are tool-specific and may not generalize across tool versions","Optimization requires baseline performance data; initial assessments may be suboptimal","Dynamic parameter adjustment during execution may cause tool instability or inconsistent results","No guarantee of optimal parameters; heuristics are probabilistic and may miss edge cases"],"requires":["Python 3.8+ for MCP server runtime","Tool-specific parameter profiles and optimization heuristics","Historical performance data for parameter tuning"],"input_types":["target characteristics (network size, application type, infrastructure scale)","assessment constraints (time limit, risk tolerance, coverage requirements)","tool-specific parameters (optional, for manual override)"],"output_types":["recommended tool parameters (JSON)","optimization rationale (explanation of parameter choices)","performance predictions (estimated scan time, coverage)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-fuzzinglabs-mcp-security-hub__cap_2","uri":"capability://tool.use.integration.sql.injection.testing.with.sqlmap.automation","name":"sql-injection-testing-with-sqlmap-automation","description":"Wraps SQLMap's automated SQL injection detection engine as an MCP tool, translating high-level injection testing requests into SQLMap payloads and options. The implementation handles parameter enumeration, injection point detection, database fingerprinting, and data extraction, with result parsing that surfaces discovered vulnerabilities, affected parameters, and exploitation techniques in structured format for AI-driven analysis and remediation planning.","intents":["Automatically test a web application's input parameters for SQL injection vulnerabilities","Identify which database backend is running and what data can be extracted from a vulnerable endpoint","Generate a prioritized list of SQL injection findings with exploitation proof-of-concept for remediation teams"],"best_for":["penetration testers automating SQL injection discovery in web application assessments","security researchers studying injection patterns across multiple targets","developers validating input sanitization in CI/CD security gates"],"limitations":["SQLMap's aggressive payloads may trigger WAF/IDS alerts or cause application instability; requires careful tuning of risk/level parameters","Detection accuracy depends on application response patterns; applications with non-standard error handling may produce false negatives","Data extraction can be slow on large databases; requires explicit table/column enumeration to avoid timeout","Requires network access to target; cannot test internal databases without tunneling setup"],"requires":["SQLMap 1.6.0+ installed and in system PATH","Python 3.8+ for MCP server runtime","Network connectivity to target web application","Optional: proxy configuration (Burp, OWASP ZAP) for request interception"],"input_types":["target URL (string)","HTTP method (GET, POST, etc.)","request body or query parameters (string or JSON)","cookie/session data (optional)","risk level (1-3, controls payload aggressiveness)","detection level (1-5, controls test coverage)"],"output_types":["structured JSON with vulnerable parameters, injection types, database info","exploitation proof-of-concept payloads","extracted data samples","plain text assessment report"],"categories":["tool-use-integration","security-scanning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-fuzzinglabs-mcp-security-hub__cap_3","uri":"capability://tool.use.integration.password.cracking.with.hashcat.acceleration","name":"password-cracking-with-hashcat-acceleration","description":"Exposes Hashcat GPU-accelerated password cracking as an MCP tool, enabling AI assistants to execute hash cracking attacks with configurable wordlists, rule sets, and attack modes. The implementation handles hash format detection, GPU resource management, wordlist selection/generation, and result parsing that surfaces cracked passwords and attack statistics for security assessment workflows.","intents":["Crack a collection of password hashes (MD5, SHA-1, bcrypt, etc.) using GPU acceleration and common wordlists","Generate custom rule-based attacks for targeted password cracking (e.g., company-specific patterns)","Estimate password strength by attempting to crack hashes with increasing complexity levels"],"best_for":["penetration testers validating password strength in post-compromise assessments","security researchers analyzing password datasets for patterns","incident response teams recovering credentials from compromised systems"],"limitations":["GPU acceleration requires NVIDIA/AMD GPU with CUDA/HIP support; CPU-only systems fall back to slow brute-force","Hashcat's memory requirements scale with wordlist size; large wordlists (>10GB) may exhaust GPU memory","Rule-based attacks require manual rule crafting; generic rules have low success rates on strong passwords","Cracking time is non-deterministic; no reliable ETA for complex hash types (bcrypt, scrypt, Argon2)","Legal/ethical constraints: cracking hashes outside authorized scope is illegal in most jurisdictions"],"requires":["Hashcat 6.2.0+ installed and in system PATH","NVIDIA GPU with CUDA 11.0+ or AMD GPU with HIP support (optional but recommended)","Python 3.8+ for MCP server runtime","Wordlist files (rockyou.txt, custom wordlists, or generation capability)"],"input_types":["hash value or hash file (string or file path)","hash type/algorithm (e.g., 'md5', 'sha1', 'bcrypt', 'argon2')","attack mode (dictionary, brute-force, hybrid, rule-based)","wordlist file path (string)","rule file path (optional, for rule-based attacks)","GPU device ID (optional, for multi-GPU systems)"],"output_types":["structured JSON with cracked passwords, hash-password pairs, attack statistics","plain text results file","performance metrics (hashes/second, GPU utilization)"],"categories":["tool-use-integration","security-assessment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-fuzzinglabs-mcp-security-hub__cap_4","uri":"capability://tool.use.integration.binary.analysis.with.ghidra.decompilation","name":"binary-analysis-with-ghidra-decompilation","description":"Integrates Ghidra reverse engineering framework as an MCP tool, enabling AI assistants to perform automated binary analysis including decompilation, function identification, data flow analysis, and symbol recovery. The implementation manages Ghidra's headless mode, translates analysis requests into Ghidra scripts, parses decompiled code and analysis results, and surfaces function signatures, control flow graphs, and vulnerability patterns in structured format.","intents":["Decompile a binary executable and extract function signatures, control flow, and data structures","Identify potentially vulnerable patterns (buffer overflows, use-after-free, format strings) in decompiled code","Analyze malware binaries to extract IOCs (IP addresses, domain names, file paths) and understand execution flow"],"best_for":["malware analysts automating binary triage and IOC extraction","security researchers identifying vulnerability patterns across multiple binaries","reverse engineers accelerating code comprehension with AI-assisted analysis"],"limitations":["Ghidra decompilation accuracy varies by architecture and optimization level; heavily optimized binaries produce less readable pseudocode","Headless mode has limited interactivity; complex analysis requiring manual refinement must fall back to GUI","Analysis time scales with binary size; large binaries (>100MB) may timeout or consume excessive memory","Symbol recovery depends on debug information; stripped binaries require heuristic-based function identification which is error-prone","Ghidra's Java runtime adds startup overhead (~2-5 seconds per analysis)"],"requires":["Ghidra 10.0+ installed and in system PATH","Java 11+ runtime environment","Python 3.8+ for MCP server runtime","Binary file to analyze (ELF, PE, Mach-O, etc.)"],"input_types":["binary file path (string)","analysis type (decompile, disassemble, data-flow, symbol-recovery)","target architecture (optional, auto-detected if not specified)","function address or name (optional, for targeted analysis)"],"output_types":["decompiled pseudocode (C-like syntax)","structured JSON with function signatures, parameters, return types","control flow graph (DOT format or JSON)","data flow analysis results","extracted strings, imports, exports"],"categories":["tool-use-integration","code-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-fuzzinglabs-mcp-security-hub__cap_5","uri":"capability://search.retrieval.osint.data.aggregation.and.enrichment","name":"osint-data-aggregation-and-enrichment","description":"Provides OSINT (Open Source Intelligence) data collection and enrichment capabilities through MCP, aggregating information from public sources (DNS records, WHOIS, certificate transparency, public databases) about targets. The implementation queries multiple OSINT APIs and data sources, deduplicates results, enriches findings with threat intelligence context, and surfaces structured intelligence (domains, IPs, email addresses, historical data) for reconnaissance and threat assessment.","intents":["Gather all known subdomains, IP addresses, and DNS records for a target domain","Enrich an IP address with WHOIS data, ASN information, and historical DNS records","Identify email addresses, social media accounts, and other digital footprints associated with a target organization"],"best_for":["penetration testers conducting reconnaissance on target organizations","threat intelligence analysts tracking threat actor infrastructure","security researchers mapping attack surface and digital footprints"],"limitations":["OSINT data quality depends on source reliability; some sources may contain outdated or inaccurate information","Rate limiting on public OSINT APIs (DNS, WHOIS, CT logs) may throttle large-scale queries","Some OSINT sources require API keys or paid subscriptions; free tier coverage is limited","Data aggregation from multiple sources introduces deduplication complexity and potential false positives","Privacy regulations (GDPR, CCPA) may restrict collection of personal data from OSINT sources"],"requires":["Python 3.8+ for MCP server runtime","Network connectivity to OSINT data sources (DNS, WHOIS, CT logs, etc.)","Optional: API keys for enhanced OSINT services (Shodan, VirusTotal, SecurityTrails, etc.)"],"input_types":["target domain or IP address (string)","OSINT data type filter (subdomains, DNS, WHOIS, certificates, emails, etc.)","historical data depth (days/months to search)"],"output_types":["structured JSON with aggregated OSINT findings (domains, IPs, emails, ASN, WHOIS data)","certificate transparency logs (domain, issuer, validity dates)","DNS resolution history","threat intelligence enrichment (known malware associations, threat actor links)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-fuzzinglabs-mcp-security-hub__cap_6","uri":"capability://tool.use.integration.mcp.tool.registry.and.schema.binding","name":"mcp-tool-registry-and-schema-binding","description":"Implements MCP protocol compliance layer that registers all security tools (Nmap, Nuclei, SQLMap, Hashcat, Ghidra, OSINT) as callable MCP resources with standardized schema definitions. The implementation defines tool schemas (input parameters, output types, constraints), handles MCP protocol marshaling/unmarshaling, manages tool lifecycle (initialization, execution, cleanup), and provides error handling with structured failure reporting for AI assistant integration.","intents":["Expose security tools to Claude and other MCP-compatible AI assistants with standardized callable interfaces","Define tool schemas that enable AI assistants to understand tool capabilities, constraints, and expected inputs/outputs","Handle MCP protocol communication (request/response marshaling, error handling, resource management)"],"best_for":["AI assistant developers integrating security tools into LLM workflows","teams building security automation platforms on top of MCP","organizations standardizing tool exposure across multiple AI assistants"],"limitations":["MCP protocol overhead adds ~50-200ms latency per tool invocation for serialization/deserialization","Schema validation is strict; malformed tool invocations fail with protocol errors rather than graceful degradation","Tool lifecycle management (initialization, cleanup) must be explicitly handled; resource leaks possible if cleanup fails","MCP protocol version compatibility: tools may break if MCP spec changes"],"requires":["Python 3.8+ for MCP server runtime","MCP SDK/libraries for Python","Claude or other MCP-compatible AI assistant client"],"input_types":["MCP protocol messages (JSON-RPC format)","tool invocation requests with parameters"],"output_types":["MCP protocol responses (JSON-RPC format)","structured tool results","error messages with diagnostic context"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-fuzzinglabs-mcp-security-hub__cap_7","uri":"capability://automation.workflow.docker.containerized.tool.isolation","name":"docker-containerized-tool-isolation","description":"Provides Docker containerization for security tools, isolating each tool's runtime environment to prevent dependency conflicts, resource exhaustion, and security incidents. The implementation manages container lifecycle (creation, execution, cleanup), mounts necessary volumes for input/output, configures resource limits (CPU, memory, network), and handles inter-container communication for tool orchestration.","intents":["Run security tools in isolated containers to prevent dependency conflicts and resource exhaustion","Safely execute untrusted tool binaries (e.g., malware analysis tools) without affecting host system","Scale tool execution across multiple containers for parallel processing of large target lists"],"best_for":["teams deploying mcp-security-hub in shared/multi-tenant environments","organizations requiring strong isolation between security tool executions","cloud-native deployments requiring containerized tool orchestration"],"limitations":["Docker overhead adds ~500ms-2s per tool invocation for container startup/teardown; persistent containers required for performance-critical workflows","Container resource limits (CPU, memory) may cause tool timeouts on large datasets; requires careful tuning per tool","GPU passthrough to containers adds complexity; not all tools support GPU acceleration in containerized environments","Volume mounts for input/output files introduce I/O latency; network-based file access (NFS, S3) may be slower than local disk","Requires Docker daemon running; not suitable for serverless/function-as-a-service deployments"],"requires":["Docker 20.10+ installed and running","Docker images for each security tool (pre-built or custom)","Python 3.8+ for MCP server runtime","Sufficient disk space for container images and temporary volumes"],"input_types":["tool name (string)","tool parameters (JSON)","input files/data (mounted volumes)"],"output_types":["tool results (from container stdout/stderr)","output files (from mounted volumes)","container execution logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-fuzzinglabs-mcp-security-hub__cap_8","uri":"capability://automation.workflow.multi.tool.orchestration.and.chaining","name":"multi-tool-orchestration-and-chaining","description":"Enables AI assistants to orchestrate multi-step security workflows by chaining outputs from one tool as inputs to another (e.g., Nmap → Nuclei → SQLMap). The implementation manages tool execution order, data transformation between tools, error handling and recovery, and context propagation across tool boundaries, allowing complex security assessments to be expressed as high-level workflows.","intents":["Run a complete security assessment workflow: network scan (Nmap) → vulnerability scan (Nuclei) → exploitation testing (SQLMap)","Chain reconnaissance tools (OSINT → Nmap → service enumeration) to build comprehensive target profile","Orchestrate analysis pipeline: binary collection → Ghidra decompilation → vulnerability pattern matching"],"best_for":["penetration testers automating multi-stage security assessments","security teams building repeatable assessment workflows","researchers studying attack chains and vulnerability correlations"],"limitations":["Tool chaining introduces cumulative latency; complex workflows may take hours to complete","Error propagation: failure in one tool stage may invalidate downstream stages; requires explicit error handling and recovery logic","Data transformation between tools requires schema mapping; incompatible output formats require custom adapters","Workflow state management: long-running workflows require persistent state storage to enable resumption after failures","Tool dependencies: some tool combinations may conflict (e.g., aggressive Nmap scans may trigger IDS, blocking Nuclei scans)"],"requires":["All constituent tools installed and configured (Nmap, Nuclei, SQLMap, Hashcat, Ghidra, OSINT sources)","Python 3.8+ for MCP server runtime","Workflow orchestration logic (custom or via external framework like Airflow, Prefect)"],"input_types":["workflow definition (JSON or YAML specifying tool sequence and data flow)","initial target specification (domain, IP, binary, etc.)","tool parameters for each stage"],"output_types":["aggregated results from all workflow stages","execution timeline and performance metrics","error logs and recovery actions"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-fuzzinglabs-mcp-security-hub__cap_9","uri":"capability://data.processing.analysis.structured.result.parsing.and.normalization","name":"structured-result-parsing-and-normalization","description":"Parses and normalizes output from heterogeneous security tools (Nmap XML, Nuclei JSON, SQLMap results, Ghidra analysis, OSINT data) into unified structured formats (JSON, SARIF) for consistent consumption by AI assistants. The implementation handles tool-specific output formats, extracts relevant fields, deduplicates findings, and enriches results with context (severity, affected components, remediation guidance).","intents":["Convert tool-specific output formats (Nmap XML, Nuclei JSON, SQLMap text) into unified JSON for AI analysis","Deduplicate and correlate findings across multiple tools (e.g., same vulnerability found by Nuclei and SQLMap)","Enrich raw findings with context (severity, CVSS scores, remediation guidance) for decision-making"],"best_for":["teams aggregating results from multiple security tools","developers building security dashboards and reporting systems","organizations standardizing vulnerability data formats across tools"],"limitations":["Parser brittleness: tool output format changes break parsing logic; requires maintenance as tools update","Deduplication heuristics may miss correlated findings or create false positives; requires tuning per tool combination","Enrichment data (CVSS, remediation) depends on external sources (NVD, vendor advisories); may be incomplete or outdated","Performance: parsing large result sets (thousands of findings) may introduce latency"],"requires":["Python 3.8+ for MCP server runtime","Tool-specific output parsers (XML, JSON, text parsing libraries)","Optional: external enrichment APIs (NVD, CVSS, vendor advisories)"],"input_types":["raw tool output (XML, JSON, text, binary formats)","tool type identifier (nmap, nuclei, sqlmap, etc.)","enrichment options (include CVSS, remediation, etc.)"],"output_types":["normalized JSON with standardized finding schema","SARIF format for IDE integration","CSV/TSV for spreadsheet import"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Nmap 7.80+ installed and in system PATH","Root or administrator privileges for privileged scan types","Python 3.8+ for MCP server runtime","Nuclei 2.8.0+ installed and in system PATH","Network connectivity to target hosts","Optional: API keys for enhanced template features (GitHub, Shodan)","Tool-specific parameter profiles and optimization heuristics","Historical performance data for parameter tuning","SQLMap 1.6.0+ installed and in system PATH","Network connectivity to target web application"],"failure_modes":["Nmap execution requires root/admin privileges for certain scan types (SYN, OS detection), limiting deployment in restricted environments","Large-scale scans (Class B networks) may timeout or consume excessive resources in cloud-hosted MCP deployments","Output parsing depends on Nmap version consistency; version mismatches may break structured result extraction","Nuclei template accuracy depends on community-maintained templates; false positives increase with permissive severity settings","Scanning large target lists sequentially can be slow; parallel execution requires careful rate-limiting to avoid target DoS","Template updates require manual refresh or integration with Nuclei's update mechanism; stale templates miss new CVEs","Parameter optimization heuristics are tool-specific and may not generalize across tool versions","Optimization requires baseline performance data; initial assessments may be suboptimal","Dynamic parameter adjustment during execution may cause tool instability or inconsistent results","No guarantee of optimal parameters; heuristics are probabilistic and may miss edge cases","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.37035659297423956,"quality":0.47,"ecosystem":0.7000000000000001,"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":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:44.761Z","last_commit":"2026-04-08T01:44:15Z"},"community":{"stars":535,"forks":77,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-fuzzinglabs-mcp-security-hub","compare_url":"https://unfragile.ai/compare?artifact=mcp-fuzzinglabs-mcp-security-hub"}},"signature":"v7k/c7K2mWFYZheHTOmIEjr9cYM8fMRwz8aNbG+yEz124PeBX5Ewz11+GlGriJUWxY/dhgCYCMLSkMFCqceOAw==","signedAt":"2026-06-21T04:36:37.969Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-fuzzinglabs-mcp-security-hub","artifact":"https://unfragile.ai/mcp-fuzzinglabs-mcp-security-hub","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-fuzzinglabs-mcp-security-hub","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"}}