What can mcp-security-hub do?

network-reconnaissance-via-nmap-mcp, vulnerability-scanning-with-nuclei-templates, ai-guided-tool-parameter-optimization, sql-injection-testing-with-sqlmap-automation, password-cracking-with-hashcat-acceleration, binary-analysis-with-ghidra-decompilation, osint-data-aggregation-and-enrichment, mcp-tool-registry-and-schema-binding, docker-containerized-tool-isolation, multi-tool-orchestration-and-chaining, structured-result-parsing-and-normalization

mcp-security-hub

MCP ServerFree

A growing collection of MCP servers bringing offensive security tools to AI assistants. Nmap, Ghidra, Nuclei, SQLMap, Hashcat and more.

Open Source

/ 100

11 capabilities

Capabilities11 decomposed

network-reconnaissance-via-nmap-mcp

Medium confidence

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.

Solves for

I need to scan a target network to identify open ports and running servicesHelp me enumerate what's listening on a specific host before I attempt exploitationI 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

Requires

Nmap 7.80+ installed and in system PATH

Root or administrator privileges for privileged scan types

Python 3.8+ for MCP server runtime

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

What makes it unique

Bridges Nmap's native CLI into MCP protocol with bidirectional translation: natural language → Nmap flags and XML output → structured JSON, enabling AI assistants to reason about network topology without manual command construction

vs alternatives

Unlike standalone Nmap or REST API wrappers, MCP integration allows Claude and other AI assistants to invoke scans as native tools with full context awareness and multi-step reasoning about results

vulnerability-scanning-with-nuclei-templates

Medium confidence

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.

Solves for

Scan a web application for common vulnerabilities (SQLi, XSS, CSRF) without manual template selectionCheck 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

Requires

Nuclei 2.8.0+ installed and in system PATH

Network connectivity to target hosts

Python 3.8+ for MCP server runtime

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

What makes it unique

Abstracts Nuclei's template complexity by mapping natural language vulnerability categories to template selectors, allowing non-security-experts to run targeted scans while maintaining expert-level template coverage and result filtering

vs alternatives

Nuclei via MCP enables AI assistants to reason about vulnerability patterns and chain scans across multiple targets with context awareness, versus running Nuclei as a standalone CLI tool with no semantic understanding of results

ai-guided-tool-parameter-optimization

Medium confidence

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.

Solves for

Automatically tune Nmap scan intensity based on target network size and time constraintsAdjust Nuclei template severity and coverage based on application type and risk profileOptimize 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

Requires

Python 3.8+ for MCP server runtime

Tool-specific parameter profiles and optimization heuristics

Historical performance data for parameter tuning

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

What makes it unique

Enables AI assistants to optimize security tool parameters based on target profiling and constraint analysis, versus manual parameter selection which requires expert knowledge of tool behavior and target characteristics

vs alternatives

AI-guided parameter optimization via mcp-security-hub enables adaptive tool configuration based on target context, versus static parameter presets which may be suboptimal for diverse targets

sql-injection-testing-with-sqlmap-automation

Medium confidence

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.

Solves for

Automatically test a web application's input parameters for SQL injection vulnerabilitiesIdentify which database backend is running and what data can be extracted from a vulnerable endpointGenerate 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

Requires

SQLMap 1.6.0+ installed and in system PATH

Python 3.8+ for MCP server runtime

Network connectivity to target web application

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

What makes it unique

Abstracts SQLMap's complex parameter tuning (risk/level/technique) by mapping AI-driven intent (e.g., 'find SQL injection vulnerabilities with minimal noise') to optimal SQLMap configurations, reducing false positives and improving detection speed

vs alternatives

SQLMap via MCP allows AI assistants to orchestrate multi-stage injection testing (detection → fingerprinting → extraction) with context awareness, versus manual SQLMap invocation which requires expert knowledge of payload tuning and result interpretation

password-cracking-with-hashcat-acceleration

Medium confidence

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.

Solves for

Crack a collection of password hashes (MD5, SHA-1, bcrypt, etc.) using GPU acceleration and common wordlistsGenerate 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

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

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

What makes it unique

Bridges Hashcat's GPU-accelerated cracking with MCP protocol, automating hash format detection and wordlist selection while exposing GPU resource constraints to AI assistants for intelligent attack planning (e.g., 'use GPU for bcrypt, CPU for MD5')

vs alternatives

Hashcat via MCP enables AI assistants to orchestrate multi-algorithm cracking campaigns with GPU resource awareness, versus standalone Hashcat which requires manual hash type identification and sequential execution

binary-analysis-with-ghidra-decompilation

Medium confidence

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.

Solves for

Decompile a binary executable and extract function signatures, control flow, and data structuresIdentify potentially vulnerable patterns (buffer overflows, use-after-free, format strings) in decompiled codeAnalyze 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

Requires

Ghidra 10.0+ installed and in system PATH

Java 11+ runtime environment

Python 3.8+ for MCP server runtime

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

What makes it unique

Automates Ghidra's headless analysis pipeline with AI-driven function targeting and result interpretation, translating decompiled code into structured analysis (function signatures, data flows, vulnerability patterns) that AI assistants can reason about without manual Ghidra GUI interaction

vs alternatives

Ghidra via MCP enables AI assistants to orchestrate multi-binary analysis campaigns with automated vulnerability pattern detection, versus standalone Ghidra which requires manual function navigation and expert interpretation of decompiled code

osint-data-aggregation-and-enrichment

Medium confidence

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.

Solves for

Gather all known subdomains, IP addresses, and DNS records for a target domainEnrich an IP address with WHOIS data, ASN information, and historical DNS recordsIdentify 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

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.)

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

What makes it unique

Aggregates multiple OSINT sources (DNS, WHOIS, CT logs, public databases) with deduplication and threat intelligence enrichment, presenting unified structured output that AI assistants can reason about for attack surface mapping without manual source querying

vs alternatives

OSINT via MCP enables AI assistants to orchestrate multi-source reconnaissance with threat context enrichment, versus manual OSINT tool usage which requires querying each source separately and manual correlation

mcp-tool-registry-and-schema-binding

Medium confidence

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.

Solves for

Expose security tools to Claude and other MCP-compatible AI assistants with standardized callable interfacesDefine tool schemas that enable AI assistants to understand tool capabilities, constraints, and expected inputs/outputsHandle 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

Requires

Python 3.8+ for MCP server runtime

MCP SDK/libraries for Python

Claude or other MCP-compatible AI assistant client

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

What makes it unique

Implements MCP protocol compliance as a unified registry layer that standardizes tool exposure across heterogeneous security tools (Nmap, Nuclei, SQLMap, etc.), enabling AI assistants to discover and invoke tools with consistent schema-based interfaces

vs alternatives

MCP tool registry via mcp-security-hub provides standardized tool exposure versus custom REST API wrappers, enabling AI assistants to understand tool capabilities declaratively and invoke tools with schema validation

docker-containerized-tool-isolation

Medium confidence

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.

Solves for

Run security tools in isolated containers to prevent dependency conflicts and resource exhaustionSafely execute untrusted tool binaries (e.g., malware analysis tools) without affecting host systemScale 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

Requires

Docker 20.10+ installed and running

Docker images for each security tool (pre-built or custom)

Python 3.8+ for MCP server runtime

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

What makes it unique

Wraps heterogeneous security tools (Nmap, Nuclei, SQLMap, Hashcat, Ghidra) in standardized Docker containers with resource isolation and lifecycle management, enabling safe parallel execution and multi-tenant deployment without dependency conflicts

vs alternatives

Docker containerization via mcp-security-hub provides strong isolation and scalability versus native tool execution, at the cost of container startup overhead and complexity

multi-tool-orchestration-and-chaining

Medium confidence

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.

Solves for

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 profileOrchestrate 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

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)

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

What makes it unique

Enables AI assistants to express complex multi-tool security workflows as high-level intent (e.g., 'run a complete assessment'), with automatic tool sequencing, data transformation, and error handling versus manual tool invocation

vs alternatives

Workflow orchestration via mcp-security-hub enables AI-driven multi-stage assessments with automatic tool chaining, versus manual tool invocation which requires expert knowledge of tool sequencing and data transformation

structured-result-parsing-and-normalization

Medium confidence

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).

Solves for

Convert tool-specific output formats (Nmap XML, Nuclei JSON, SQLMap text) into unified JSON for AI analysisDeduplicate 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

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)

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

What makes it unique

Abstracts tool-specific output parsing with unified normalization layer that converts heterogeneous formats (Nmap XML, Nuclei JSON, SQLMap text) into consistent JSON schema with deduplication and threat intelligence enrichment

vs alternatives

Structured result parsing via mcp-security-hub enables AI assistants to reason about findings from multiple tools with consistent schema, versus manual parsing which requires tool-specific knowledge and error-prone format conversion

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with mcp-security-hub, ranked by overlap. Discovered automatically through the match graph.

MCP Server48

hexstrike-ai

HexStrike AI MCP Agents is an advanced MCP server that lets AI agents (Claude, GPT, Copilot, etc.) autonomously run 150+ cybersecurity tools for automated pentesting, vulnerability discovery, bug bounty automation, and security research. Seamlessly bridge LLMs with real-world offensive security capa

context-aware parameter optimization for security toolsnuclei-based vulnerability scanning with template optimizationnetwork reconnaissance with adaptive scanning strategiessql injection testing with adaptive payload generation

4 shared capabilities

MCP Server48

hexstrike-ai

network reconnaissance with advanced nmap integration and optimizationweb application security scanning with gobuster and nuclei integrationsql injection testing with sqlmap automation and parameter optimizationmcp-based security tool orchestration with 150+ integrated tools

4 shared capabilities

MCP Server40

mcp-for-security

MCP for Security: A collection of Model Context Protocol servers for popular security tools like SQLMap, FFUF, NMAP, Masscan and more. Integrate security testing and penetration testing into AI workflows.

template-based vulnerability scanning with nucleinetwork service discovery and port scanning via nmapsql injection detection and exploitation via sqlmapmcp-standardized security tool abstraction layer

4 shared capabilities

MCP Server28

MCPWatch

** - A comprehensive security scanner for Model Context Protocol (MCP) servers that detects vulnerabilities and security issues in your MCP server implementations.

research-backed vulnerability pattern matchingparameter injection and protocol violation detectionmulti-scanner vulnerability orchestration with parallel execution

3 shared capabilities

MCP Server41

agent-scan

Security scanner for AI agents, MCP servers and agent skills.

mcp server static vulnerability scanning via natural-language analysisoffline local vulnerability inspection without remote submission

2 shared capabilities

MCP Server23

OSV

** - Access the [OSV (Open Source Vulnerabilities) database](https://osv.dev/) for vulnerability information. Query vulnerabilities by package version or commit, batch query multiple packages, and get detailed vulnerability information by ID.

mcp-tool-schema-based-function-calling

1 shared capability

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
✓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
✓security teams automating parameter tuning for diverse targets
✓penetration testers optimizing assessment speed without sacrificing coverage

Known 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
⚠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

Requirements

Nmap 7.80+ installed and in system PATHRoot or administrator privileges for privileged scan typesPython 3.8+ for MCP server runtimeNuclei 2.8.0+ installed and in system PATHNetwork connectivity to target hostsOptional: API keys for enhanced template features (GitHub, Shodan)Tool-specific parameter profiles and optimization heuristicsHistorical performance data for parameter tuning

Input / Output

Accepts: 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), target URL or IP (string), vulnerability category filter (e.g., 'cve', 'owasp', 'misconfig'), severity threshold (critical, high, medium, low), template tags (custom filtering), target characteristics (network size, application type, infrastructure scale), assessment constraints (time limit, risk tolerance, coverage requirements), tool-specific parameters (optional, for manual override), 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), 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), 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), target domain or IP address (string), OSINT data type filter (subdomains, DNS, WHOIS, certificates, emails, etc.), historical data depth (days/months to search), MCP protocol messages (JSON-RPC format), tool invocation requests with parameters, tool name (string), tool parameters (JSON), input files/data (mounted volumes), workflow definition (JSON or YAML specifying tool sequence and data flow), initial target specification (domain, IP, binary, etc.), tool parameters for each stage, raw tool output (XML, JSON, text, binary formats), tool type identifier (nmap, nuclei, sqlmap, etc.), enrichment options (include CVSS, remediation, etc.)

Produces: structured JSON with open ports, services, versions, XML-formatted Nmap output, plain text summary, structured JSON with vulnerability findings (type, severity, affected parameter, remediation), SARIF format for IDE integration, plain text report, recommended tool parameters (JSON), optimization rationale (explanation of parameter choices), performance predictions (estimated scan time, coverage), structured JSON with vulnerable parameters, injection types, database info, exploitation proof-of-concept payloads, extracted data samples, plain text assessment report, structured JSON with cracked passwords, hash-password pairs, attack statistics, plain text results file, performance metrics (hashes/second, GPU utilization), 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, 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), MCP protocol responses (JSON-RPC format), structured tool results, error messages with diagnostic context, tool results (from container stdout/stderr), output files (from mounted volumes), container execution logs, aggregated results from all workflow stages, execution timeline and performance metrics, error logs and recovery actions, normalized JSON with standardized finding schema, CSV/TSV for spreadsheet import

UnfragileRank

Adoption20%(30% weight)

Quality50%(25% weight)

Ecosystem70%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

11 capabilities

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Repository Details

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Topics

aiclaudecybersecuritydockerghidramcpmcp-servernmapnucleioffensive-securityosintpentestingsecurityvulnerability-scanner

Last commit: Apr 8, 2026

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A growing collection of MCP servers bringing offensive security tools to AI assistants. Nmap, Ghidra, Nuclei, SQLMap, Hashcat and more.

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Capabilities11 decomposed

network-reconnaissance-via-nmap-mcp

Medium confidence

Solves for

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

Requires

Nmap 7.80+ installed and in system PATH

Root or administrator privileges for privileged scan types

Python 3.8+ for MCP server runtime

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

What makes it unique

vs alternatives

Unlike standalone Nmap or REST API wrappers, MCP integration allows Claude and other AI assistants to invoke scans as native tools with full context awareness and multi-step reasoning about results

vulnerability-scanning-with-nuclei-templates

Medium confidence

Solves for

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

Requires

Nuclei 2.8.0+ installed and in system PATH

Network connectivity to target hosts

Python 3.8+ for MCP server runtime

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

What makes it unique

vs alternatives

ai-guided-tool-parameter-optimization

Medium confidence

Solves for

Best for

security teams automating parameter tuning for diverse targets

penetration testers optimizing assessment speed without sacrificing coverage

organizations standardizing tool configuration across assessments

Requires

Python 3.8+ for MCP server runtime

Tool-specific parameter profiles and optimization heuristics

Historical performance data for parameter tuning

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

What makes it unique

vs alternatives

AI-guided parameter optimization via mcp-security-hub enables adaptive tool configuration based on target context, versus static parameter presets which may be suboptimal for diverse targets

sql-injection-testing-with-sqlmap-automation

Medium confidence

Solves for

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

Requires

SQLMap 1.6.0+ installed and in system PATH

Python 3.8+ for MCP server runtime

Network connectivity to target web application

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

What makes it unique

vs alternatives

password-cracking-with-hashcat-acceleration

Medium confidence

Solves for

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

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

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

What makes it unique

vs alternatives

binary-analysis-with-ghidra-decompilation

Medium confidence

Solves for

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

Requires

Ghidra 10.0+ installed and in system PATH

Java 11+ runtime environment

Python 3.8+ for MCP server runtime

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

What makes it unique

vs alternatives

osint-data-aggregation-and-enrichment

Medium confidence

Solves for

Best for

penetration testers conducting reconnaissance on target organizations

threat intelligence analysts tracking threat actor infrastructure

security researchers mapping attack surface and digital footprints

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.)

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

What makes it unique

vs alternatives

mcp-tool-registry-and-schema-binding

Medium confidence

Solves for

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

Requires

Python 3.8+ for MCP server runtime

MCP SDK/libraries for Python

Claude or other MCP-compatible AI assistant client

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

What makes it unique

vs alternatives

docker-containerized-tool-isolation

Medium confidence

Solves for

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

Requires

Docker 20.10+ installed and running

Docker images for each security tool (pre-built or custom)

Python 3.8+ for MCP server runtime

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

What makes it unique

vs alternatives

Docker containerization via mcp-security-hub provides strong isolation and scalability versus native tool execution, at the cost of container startup overhead and complexity

multi-tool-orchestration-and-chaining

Medium confidence

Solves for

Best for

penetration testers automating multi-stage security assessments

security teams building repeatable assessment workflows

researchers studying attack chains and vulnerability correlations

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)

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

What makes it unique

vs alternatives

structured-result-parsing-and-normalization

Medium confidence

Solves for

Best for

teams aggregating results from multiple security tools

developers building security dashboards and reporting systems

organizations standardizing vulnerability data formats across tools

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)

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

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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mcp-security-hub

Capabilities11 decomposed

network-reconnaissance-via-nmap-mcp

vulnerability-scanning-with-nuclei-templates

ai-guided-tool-parameter-optimization

sql-injection-testing-with-sqlmap-automation

password-cracking-with-hashcat-acceleration

binary-analysis-with-ghidra-decompilation

osint-data-aggregation-and-enrichment

mcp-tool-registry-and-schema-binding

docker-containerized-tool-isolation

multi-tool-orchestration-and-chaining

structured-result-parsing-and-normalization

Related Artifactssharing capabilities

hexstrike-ai

hexstrike-ai

mcp-for-security

MCPWatch

agent-scan

OSV

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

About

Categories

Alternatives to mcp-security-hub

Are you the builder of mcp-security-hub?

Get the weekly brief

Data Sources

mcp-security-hub

Capabilities11 decomposed

network-reconnaissance-via-nmap-mcp

vulnerability-scanning-with-nuclei-templates

ai-guided-tool-parameter-optimization

sql-injection-testing-with-sqlmap-automation

password-cracking-with-hashcat-acceleration

binary-analysis-with-ghidra-decompilation

osint-data-aggregation-and-enrichment

mcp-tool-registry-and-schema-binding

docker-containerized-tool-isolation

multi-tool-orchestration-and-chaining

structured-result-parsing-and-normalization

Related Artifactssharing capabilities

hexstrike-ai

hexstrike-ai

mcp-for-security

MCPWatch

agent-scan

OSV

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

About

Categories

Alternatives to mcp-security-hub

Are you the builder of mcp-security-hub?

Get the weekly brief

Data Sources