{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-0x4m4--hexstrike-ai","slug":"0x4m4--hexstrike-ai","name":"hexstrike-ai","type":"mcp","url":"https://www.hexstrike.com/","page_url":"https://unfragile.ai/0x4m4--hexstrike-ai","categories":["mcp-servers","code-editors","code-review-security"],"tags":["0x4m4","ai","ai-agents","ai-cybersecurity","ai-hacking","ai-penetration-testing","ai-security-tool","artificial-intelligence","ctf-tools","generative-ai","hexstrike","kali-linux","kali-tools","llm","llm-integration","mcp","mcp-server","mcp-tools","pentesting","pentesting-tools"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-0x4m4--hexstrike-ai__cap_0","uri":"capability://tool.use.integration.mcp.based.security.tool.orchestration.with.llm.agents","name":"mcp-based security tool orchestration with llm agents","description":"Exposes 150+ cybersecurity tools through the Model Context Protocol (MCP) as decorated functions (@mcp.tool) that external AI agents (Claude, GPT, Copilot) can invoke autonomously. The hexstrike_mcp.py FastMCP client translates natural language requests from LLMs into structured tool invocations with parameter binding, enabling multi-step security workflows without manual tool switching or context loss between agent and execution environment.","intents":["I want my AI agent to autonomously run penetration testing tools without leaving the chat interface","I need to chain multiple security tools together based on AI reasoning about what to run next","I want Claude or GPT to decide which scanning tool to use based on target analysis"],"best_for":["Security researchers automating pentesting workflows with LLM agents","Bug bounty hunters building autonomous vulnerability discovery pipelines","Teams integrating AI-driven security testing into CI/CD or security platforms"],"limitations":["MCP protocol adds ~200-500ms latency per tool invocation due to serialization and deserialization","Tool output context window limited by LLM token limits — large scan results may be truncated or summarized","No built-in result persistence across agent sessions — requires external state management for multi-session workflows","Requires explicit tool registration and schema definition; adding new tools requires code changes to hexstrike_mcp.py"],"requires":["Python 3.9+","FastMCP library (MCP implementation)","Claude API key, OpenAI API key, or local LLM with MCP support","Kali Linux or equivalent security tool suite (nmap, gobuster, nuclei, sqlmap, etc.)","Network access to target systems (for scanning tools)"],"input_types":["natural language instructions from LLM","structured tool parameters (target IP, domain, port range, wordlist path)","scan templates and configuration files"],"output_types":["structured JSON tool results","raw command output (stdout/stderr)","parsed vulnerability findings","agent reasoning traces"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_1","uri":"capability://planning.reasoning.intelligent.target.profiling.and.tool.recommendation","name":"intelligent target profiling and tool recommendation","description":"Analyzes target characteristics (IP ranges, domain structure, service fingerprints, cloud provider) via POST /api/intelligence/analyze-target endpoint and recommends optimal tool subsets via POST /api/intelligence/select-tools. Uses AI-powered decision logic to match target attributes (e.g., AWS infrastructure, web application, binary) to relevant tools from the 150+ arsenal, reducing tool selection overhead and improving scan efficiency by avoiding irrelevant tools.","intents":["I want the system to automatically choose which scanning tools are relevant for this target","I need to understand what security tools apply to an AWS environment vs a traditional network","I want to optimize scan time by only running applicable tools instead of the full toolkit"],"best_for":["Penetration testers with limited security tool expertise","Automated bug bounty platforms that need to adapt to diverse target types","Security teams building intelligent assessment workflows"],"limitations":["Target analysis accuracy depends on initial reconnaissance data quality — incomplete fingerprinting leads to suboptimal tool selection","Recommendation engine is rule-based or heuristic; no machine learning model tuning per organization's tool preferences","Cannot recommend tools for zero-day or novel attack vectors not in the training/rule set","Requires network connectivity to target for fingerprinting; offline target analysis not supported"],"requires":["Target information (IP, domain, or service endpoints)","Network access to perform initial reconnaissance","Populated tool metadata and capability mappings in hexstrike_server.py","AI client configured (Claude, GPT, or local LLM) for decision logic"],"input_types":["target IP address or domain","service fingerprint data (ports, banners, HTTP headers)","cloud provider metadata (AWS account structure, Azure subscriptions)","user-provided context (e.g., 'web application', 'cloud infrastructure')"],"output_types":["JSON object with target profile (services, OS, cloud provider, risk level)","ranked list of recommended tools with confidence scores","tool execution parameters pre-populated based on target analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_10","uri":"capability://automation.workflow.nuclei.based.vulnerability.scanning.with.template.optimization","name":"nuclei-based vulnerability scanning with template optimization","description":"Orchestrates nuclei_scan() MCP tool that executes community and custom vulnerability detection templates against targets. Agents analyze target characteristics and select optimal nuclei templates (by severity, relevance, execution time) to maximize vulnerability discovery while minimizing scan time. Implements template chaining where findings from one template inform execution of subsequent templates, and correlates results across templates to identify complex vulnerabilities requiring multiple detection vectors.","intents":["I want to run vulnerability scans using nuclei templates optimized for my specific target","I need to identify which nuclei templates are most relevant for the services I discovered","I want to correlate findings from multiple nuclei templates to identify complex vulnerabilities"],"best_for":["Security teams conducting rapid vulnerability assessments with nuclei","Bug bounty hunters automating template-based scanning","Organizations building custom nuclei templates for internal vulnerability detection"],"limitations":["Template quality varies; community templates may have high false-positive rates or miss edge cases","Template execution time is unpredictable; complex templates may timeout on slow networks","Template selection is heuristic-based; no machine learning model for predicting template relevance per target","Custom template development requires YAML expertise; integration of custom templates requires code changes","Template updates are manual; no automatic synchronization with nuclei template repository"],"requires":["nuclei installed with template repository","Target URL or IP address","AI agent for template selection and result correlation","Optional: custom nuclei templates for organization-specific vulnerabilities","Optional: nuclei configuration for concurrency, timeout, and output format"],"input_types":["target URL or IP address","nuclei template filters (by severity, type, tags)","scan scope (specific paths, all endpoints)","execution preferences (speed vs accuracy, concurrency level)","custom template paths"],"output_types":["vulnerability findings with template name and severity","matched request/response evidence","template execution logs and timing","correlated findings across multiple templates","vulnerability report with remediation guidance"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_11","uri":"capability://automation.workflow.sql.injection.testing.with.adaptive.payload.generation","name":"sql injection testing with adaptive payload generation","description":"Orchestrates sqlmap_scan() MCP tool with AI-driven payload adaptation based on target response analysis. Agents analyze HTTP responses to injection attempts, identify database type and version from error messages and behavior, and generate context-specific payloads (time-based blind, boolean-based blind, union-based, error-based) optimized for detected database. Implements intelligent parameter prioritization that tests most likely vulnerable parameters first, reducing total scan time.","intents":["I want to automatically test all parameters for SQL injection vulnerabilities","I need to identify the database type and version to generate appropriate SQL injection payloads","I want to minimize scan time by prioritizing likely vulnerable parameters"],"best_for":["Web application penetration testers automating SQL injection testing","Bug bounty hunters assessing web applications for SQL injection","Security teams conducting rapid web application vulnerability assessments"],"limitations":["Payload generation is limited to sqlmap's built-in techniques; novel injection vectors may not be detected","Database type detection relies on error messages and behavior; some databases may be misidentified","Time-based blind injection is slow; may timeout on high-latency networks","WAF/IDS evasion is limited to sqlmap's built-in options; advanced WAF bypass requires custom techniques","Parameter prioritization is heuristic-based; may miss vulnerabilities in unexpected parameters"],"requires":["Target web application URL with vulnerable parameter","sqlmap installed with payload templates","AI agent for response analysis and payload adaptation","HTTP authentication credentials (if required)","Optional: custom sqlmap tamper scripts for WAF bypass"],"input_types":["target URL with vulnerable parameter","HTTP method (GET, POST, etc.)","parameter list to test","HTTP headers and cookies","database type hint (optional)","WAF/rate limiting constraints"],"output_types":["identified SQL injection vulnerabilities with payload examples","detected database type and version","extracted database contents (tables, columns, data)","exploitation proof-of-concept","vulnerability severity assessment","remediation recommendations"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_12","uri":"capability://automation.workflow.rest.api.endpoint.discovery.and.security.testing","name":"rest api endpoint discovery and security testing","description":"Discovers REST API endpoints through multiple techniques: directory enumeration (gobuster), JavaScript analysis for API calls, OpenAPI/Swagger specification parsing, and HTTP method enumeration. Agents analyze discovered endpoints to identify authentication mechanisms, parameter types, and potential vulnerabilities. Implements automated API security testing including authentication bypass attempts, authorization flaws, rate limiting evasion, and injection attacks across API parameters.","intents":["I want to automatically discover all REST API endpoints in a web application","I need to identify authentication and authorization flaws in API endpoints","I want to test API endpoints for common vulnerabilities (injection, broken authentication, rate limiting)"],"best_for":["API security testers conducting comprehensive API assessments","Bug bounty hunters finding API vulnerabilities","Security teams auditing internal and external APIs"],"limitations":["Endpoint discovery relies on multiple techniques; some endpoints may be hidden or require specific headers","OpenAPI/Swagger specification may be outdated or incomplete; manual endpoint discovery may be required","Authentication mechanism detection is heuristic-based; custom authentication schemes may not be recognized","Authorization testing is limited to role-based access control; attribute-based or policy-based access control requires custom logic","Rate limiting evasion is limited to simple techniques; sophisticated rate limiting may require custom bypass logic"],"requires":["Target API base URL","gobuster for directory enumeration","JavaScript analysis tool (Burp, custom script) for API call extraction","OpenAPI/Swagger parser","AI agent for endpoint analysis and vulnerability testing","Optional: API documentation or specification"],"input_types":["target API base URL","API documentation (OpenAPI, Swagger, Postman collection)","authentication credentials (if required)","API parameter examples","testing scope (specific endpoints or all)"],"output_types":["discovered API endpoints with HTTP methods and parameters","identified authentication mechanisms","authorization flaws and privilege escalation paths","injection vulnerabilities in API parameters","rate limiting and DoS vulnerabilities","API security assessment report"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_13","uri":"capability://automation.workflow.caching.and.performance.optimization.for.repeated.scans","name":"caching and performance optimization for repeated scans","description":"Implements intelligent caching layer (GET /api/cache/stats endpoint) that stores scan results, tool outputs, and reconnaissance data to avoid redundant tool execution. Agents query cache before executing tools, reusing previous results for unchanged targets or similar reconnaissance queries. Cache invalidation is time-based and event-based (target changes, tool updates), and cache statistics track hit rates and storage usage to optimize cache size and retention policies.","intents":["I want to avoid re-running the same scans on the same target multiple times","I need to understand cache performance and optimize cache retention policies","I want to reuse reconnaissance data across multiple assessments of related targets"],"best_for":["Security teams conducting repeated assessments of the same targets","Platforms serving multiple users scanning similar targets","Organizations optimizing scanning costs and time"],"limitations":["Cache invalidation is time-based and event-based; manual cache invalidation not supported","Cache is in-memory; no persistence across server restarts without external storage","Cache size is unbounded; large-scale deployments may require cache size limits and eviction policies","Cache key generation is simple (target + tool); complex cache queries (e.g., 'all scans of this subnet') not supported","No cache sharing across multiple hexstrike instances; distributed deployments require external cache store (Redis)"],"requires":["hexstrike_server.py with caching enabled","Sufficient memory for cache storage","Optional: external cache store (Redis) for distributed deployments","Cache configuration (TTL, size limits, eviction policy)"],"input_types":["scan parameters (target, tool, options)","cache query (target, time range)","cache invalidation request (target, tool)"],"output_types":["cached scan results (if cache hit)","cache statistics (hit rate, storage usage, entry count)","cache invalidation confirmation"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_14","uri":"capability://automation.workflow.system.health.monitoring.and.telemetry.collection","name":"system health monitoring and telemetry collection","description":"Provides real-time system health monitoring via GET /api/health endpoint and telemetry collection via GET /api/telemetry endpoint. Tracks server status, tool availability, resource utilization (CPU, memory, disk), and scan performance metrics (execution time, success rate, tool-specific statistics). Agents use telemetry data to make decisions about scan aggressiveness, tool selection, and resource allocation, and health checks enable graceful degradation when tools or services become unavailable.","intents":["I want to monitor the health and performance of the hexstrike server and tools","I need to understand resource utilization and optimize scanning based on available resources","I want to detect when tools become unavailable and adapt scanning strategies accordingly"],"best_for":["Operations teams managing hexstrike deployments","Security platforms integrating hexstrike with monitoring and alerting","Teams optimizing scanning performance and resource utilization"],"limitations":["Health checks are synchronous; slow or hanging tools may block health check responses","Telemetry collection is in-memory; no persistence across server restarts","Telemetry granularity is limited to tool-level; per-scan or per-parameter metrics not available","No built-in alerting; external monitoring system required for alerts","Resource utilization metrics are server-level; per-tool resource usage not tracked"],"requires":["hexstrike_server.py with health and telemetry endpoints enabled","Tool health check scripts (ping, version check, etc.)","Optional: external monitoring system (Prometheus, Grafana) for visualization"],"input_types":["health check request (no parameters)","telemetry query (time range, metric type)"],"output_types":["server status (up/down, uptime)","tool availability status (available/unavailable per tool)","resource utilization metrics (CPU, memory, disk usage)","scan performance metrics (average execution time, success rate)","tool-specific statistics (scans executed, average duration)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_2","uri":"capability://planning.reasoning.context.aware.parameter.optimization.for.security.tools","name":"context-aware parameter optimization for security tools","description":"Optimizes tool execution parameters via POST /api/intelligence/optimize-parameters by analyzing target context (network size, service types, scan scope) and adjusting tool arguments (e.g., nmap timing templates, nuclei concurrency, sqlmap risk levels) to balance speed, accuracy, and resource consumption. Uses AI reasoning to select appropriate parameter presets (aggressive vs stealthy, comprehensive vs quick) based on engagement goals and target constraints.","intents":["I want to run a fast reconnaissance scan without overwhelming the target network","I need aggressive scanning for a controlled lab environment vs stealthy scanning for a live production system","I want to optimize nuclei template selection based on the services discovered on the target"],"best_for":["Penetration testers balancing scan speed vs stealth and accuracy","Automated security platforms that need to adapt scanning intensity to target environment","Teams conducting controlled security assessments with resource constraints"],"limitations":["Parameter optimization is heuristic-based; no machine learning model for predicting optimal parameters per target type","Cannot account for real-time network conditions or target rate-limiting during optimization","Requires manual feedback loop to refine parameter presets — no automatic tuning based on scan results","Limited to tools with parameter schemas defined in hexstrike_server.py; custom tools require manual parameter mapping"],"requires":["Target context data (network size, service types, engagement scope)","Tool parameter schemas and valid value ranges defined in configuration","AI client for reasoning about parameter tradeoffs","Baseline performance metrics or historical scan data (optional, for better optimization)"],"input_types":["target characteristics (network CIDR, service count, engagement type)","scan objectives (speed vs accuracy vs stealth)","tool-specific constraints (rate limits, resource availability)","user preferences (aggressive vs conservative scanning)"],"output_types":["JSON object with optimized tool parameters","parameter presets (e.g., 'fast-reconnaissance', 'thorough-assessment', 'stealthy-scan')","reasoning explanation for parameter choices","estimated scan duration and resource usage"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_3","uri":"capability://automation.workflow.autonomous.bug.bounty.hunting.workflow.orchestration","name":"autonomous bug bounty hunting workflow orchestration","description":"Implements BugBountyWorkflowManager that orchestrates multi-stage reconnaissance, vulnerability discovery, and reporting via specialized AI agents. Chains tools in sequence (nmap → service enumeration → vulnerability scanning → exploitation → impact assessment) with intelligent decision points where agents decide whether to escalate findings, pivot to new targets, or conclude assessment. Manages state across tool invocations and generates structured vulnerability reports with CVSS scores and remediation guidance.","intents":["I want to run a fully automated bug bounty assessment from initial reconnaissance to final report","I need the system to automatically discover vulnerabilities and determine their exploitability without manual intervention","I want to generate a professional vulnerability report with CVSS scores and remediation steps automatically"],"best_for":["Bug bounty hunters automating repetitive reconnaissance and scanning workflows","Security platforms offering automated vulnerability assessment as a service","Teams conducting rapid security assessments with minimal manual intervention"],"limitations":["Workflow assumes standard bug bounty scope (web applications, APIs, infrastructure); custom scopes require workflow customization","No built-in legal/authorization verification — assumes proper scope authorization before execution","Exploitation phase is limited to safe, non-destructive techniques; cannot perform actual system compromise or data exfiltration","Report generation is template-based; customization requires modifying report templates in code","Workflow state is in-memory; long-running assessments may lose state on server restart without external persistence"],"requires":["Target authorization and scope definition (in-scope domains, IP ranges, excluded systems)","API keys for external services (e.g., Shodan for enrichment, vulnerability databases)","Configured AI agents for decision-making at workflow checkpoints","Tool arsenal fully installed and configured (nmap, nuclei, sqlmap, etc.)","Wordlists and nuclei templates for comprehensive scanning"],"input_types":["target domain or IP range","bug bounty scope definition (in-scope/out-of-scope)","engagement parameters (aggressiveness level, time limit, resource constraints)","custom scanning templates or tool configurations"],"output_types":["structured vulnerability findings (CVE ID, CVSS score, affected component)","proof-of-concept evidence (screenshots, command output, request/response)","professional vulnerability report (HTML/PDF with remediation guidance)","workflow execution log with tool invocations and decision points"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_4","uri":"capability://planning.reasoning.ctf.challenge.solving.with.autonomous.reasoning","name":"ctf challenge solving with autonomous reasoning","description":"Implements CTFWorkflowManager that decomposes CTF challenges into sub-tasks (flag discovery, cryptography, reverse engineering, web exploitation) and assigns specialized AI agents to solve each category. Agents reason about challenge hints, execute relevant tools (ghidra for binary analysis, hashcat for cracking, sqlmap for web exploits), and iteratively refine approaches based on tool output. Maintains challenge state and coordinates multi-agent collaboration for complex challenges requiring cross-domain expertise.","intents":["I want an AI agent to autonomously solve CTF challenges without manual tool switching","I need the system to recognize challenge types and apply appropriate solving techniques","I want to learn how CTF challenges are solved by observing the agent's reasoning and tool usage"],"best_for":["CTF competitors automating challenge solving for speed competitions","Security training platforms providing AI-assisted challenge walkthroughs","Researchers studying AI reasoning in security problem-solving"],"limitations":["Challenge solving success depends on challenge type coverage in agent training; novel challenge types may not be recognized","Reverse engineering and cryptanalysis are computationally expensive; large binaries or strong encryption may timeout","No built-in integration with CTF platforms (HackTheBox, TryHackMe); requires manual challenge input/output handling","Reasoning traces may be verbose and difficult to interpret for complex multi-step challenges","Cannot solve challenges requiring physical interaction, social engineering, or out-of-band communication"],"requires":["CTF challenge description or binary/file","Specialized tools for challenge categories (ghidra, hashcat, sqlmap, steganography tools, crypto libraries)","AI agents configured with CTF-specific reasoning prompts","Sufficient compute resources for reverse engineering and cryptanalysis","Challenge metadata (category hints, difficulty level) for agent routing"],"input_types":["CTF challenge description (text, binary, web service, encrypted file)","challenge category (crypto, reverse engineering, web, forensics, steganography)","hints or partial solutions","challenge constraints (time limit, resource limits)"],"output_types":["discovered flag or solution","step-by-step reasoning trace showing agent's approach","tool invocations and their outputs","alternative solution approaches explored","learning insights about challenge-solving techniques"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_5","uri":"capability://data.processing.analysis.advanced.vulnerability.research.with.multi.tool.correlation","name":"advanced vulnerability research with multi-tool correlation","description":"Implements VulnerabilityResearchWorkflowManager that correlates findings from multiple scanning tools (nuclei, nessus, burp, custom scripts) to identify complex vulnerabilities requiring cross-tool evidence. Agents analyze tool outputs, identify patterns (e.g., multiple XSS vectors in same parameter, privilege escalation chains), and synthesize findings into high-confidence vulnerability reports. Integrates with vulnerability databases (NVD, CVE feeds) to enrich findings with exploit availability and patch status.","intents":["I want to correlate findings from multiple vulnerability scanners to reduce false positives","I need to identify complex vulnerability chains that require evidence from multiple tools","I want to understand the exploitability and impact of discovered vulnerabilities with current patch status"],"best_for":["Security researchers conducting in-depth vulnerability analysis","Vulnerability management platforms correlating scanner findings","Teams prioritizing vulnerabilities based on exploitability and patch availability"],"limitations":["Correlation accuracy depends on tool output consistency; different tools may report same vulnerability with different naming/classification","Vulnerability database enrichment requires API access to NVD, CVE feeds, and exploit databases (rate-limited)","Complex vulnerability chains may require manual validation; automated chain detection has high false-positive rates","Cannot identify zero-day vulnerabilities not in public databases","Exploit availability data is point-in-time; real-time exploit tracking requires continuous database updates"],"requires":["Output from multiple vulnerability scanning tools (nuclei, nessus, burp, custom scanners)","API keys for vulnerability databases (NVD, CVE feeds, exploit databases like Metasploit)","Tool output parsers for each scanner format (JSON, XML, CSV)","AI agents configured for vulnerability correlation and analysis","Vulnerability classification schemas (CVSS, CWE, OWASP)"],"input_types":["raw output from multiple vulnerability scanners","tool configuration and template metadata","target system information (OS, services, patch level)","vulnerability database feeds"],"output_types":["correlated vulnerability findings with confidence scores","identified vulnerability chains and attack paths","enriched vulnerability data (CVSS score, CVE ID, exploit availability, patch status)","prioritized vulnerability list for remediation","detailed analysis report with tool evidence and reasoning"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_6","uri":"capability://automation.workflow.network.reconnaissance.with.adaptive.scanning.strategies","name":"network reconnaissance with adaptive scanning strategies","description":"Orchestrates multi-phase network reconnaissance (ping sweep → port scanning → service enumeration → OS fingerprinting) via nmap_scan() MCP tool with adaptive strategy selection. Agents analyze network topology and service distribution to decide between aggressive full-port scans, targeted scanning of common ports, or stealthy scanning with timing evasion. Implements incremental scanning where initial results inform subsequent scan parameters, reducing total scan time and network impact.","intents":["I want to discover all services on a network without overwhelming it or triggering IDS alerts","I need to adapt my scanning strategy based on what I discover in early reconnaissance phases","I want to minimize scan time while maintaining comprehensive coverage of the target network"],"best_for":["Penetration testers conducting network assessments with stealth requirements","Security teams performing network discovery in large environments","Automated security platforms that need to adapt scanning to network characteristics"],"limitations":["Adaptive strategy selection is heuristic-based; no machine learning model for predicting optimal strategy per network type","Large networks (>10,000 hosts) may require excessive scan time even with optimization; chunking into subnets required","Firewall/IDS evasion techniques are limited to nmap's built-in options; advanced evasion requires custom tools","OS fingerprinting accuracy degrades with modern systems using generic banners; may require additional enumeration","Network topology discovery is limited to directly connected networks; multi-hop networks require additional routing enumeration"],"requires":["Network access to target range (direct or via VPN)","nmap installed and configured with timing templates and evasion options","Target network CIDR range or host list","AI agent for strategy selection and adaptation","Optional: custom nmap scripts for service-specific enumeration"],"input_types":["target network CIDR range or individual host list","scanning objectives (speed vs stealth vs accuracy)","network constraints (rate limits, IDS/firewall presence)","service enumeration preferences (common ports vs all ports)","timing template preference (paranoid to insane)"],"output_types":["discovered hosts with IP addresses and status (up/down)","open ports with service names and versions","OS fingerprinting results with confidence levels","network topology map (optional, if traceroute enabled)","service enumeration details (banners, HTTP headers, SSL certificates)","scan execution log with timing and strategy decisions"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_7","uri":"capability://automation.workflow.web.application.security.assessment.with.payload.generation","name":"web application security assessment with payload generation","description":"Orchestrates web application testing via gobuster_scan() for directory enumeration and sqlmap_scan() for SQL injection detection, with AI-driven payload generation and parameter fuzzing. Agents analyze application structure (forms, parameters, API endpoints) discovered by gobuster and generate context-aware payloads for sqlmap based on parameter types and application behavior. Implements intelligent parameter discovery that identifies hidden parameters through JavaScript analysis and API endpoint enumeration.","intents":["I want to automatically discover hidden directories and parameters in a web application","I need to test all discovered parameters for SQL injection vulnerabilities with context-aware payloads","I want to identify and exploit API endpoints that aren't documented in the application UI"],"best_for":["Web application penetration testers automating reconnaissance and vulnerability discovery","Bug bounty hunters assessing web applications for common vulnerabilities","Security platforms providing automated web application scanning"],"limitations":["Directory enumeration relies on wordlists; undocumented directories not in wordlist will be missed","SQL injection detection is limited to sqlmap's detection logic; advanced injection techniques (time-based blind, out-of-band) may be slow","Payload generation is rule-based; novel injection vectors not in sqlmap templates may not be detected","JavaScript analysis for parameter discovery requires browser automation; static analysis may miss dynamically-generated parameters","Rate limiting and WAF evasion are limited to sqlmap's built-in options; advanced WAF bypass requires custom techniques"],"requires":["Target web application URL","gobuster installed with directory wordlists","sqlmap installed with payload templates","AI agent for parameter analysis and payload generation","Optional: browser automation tool (Selenium, Playwright) for JavaScript analysis","Optional: custom wordlists for organization-specific directories"],"input_types":["target web application URL","directory enumeration wordlists","HTTP authentication credentials (if required)","parameter fuzzing preferences (aggressive vs conservative)","WAF/rate limiting constraints"],"output_types":["discovered directories and files with HTTP status codes","identified parameters (GET, POST, headers, cookies)","SQL injection vulnerabilities with payload examples","parameter type analysis (numeric, string, boolean)","API endpoint enumeration results","assessment report with vulnerability severity and remediation"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_8","uri":"capability://code.generation.editing.binary.analysis.and.reverse.engineering.with.ghidra.integration","name":"binary analysis and reverse engineering with ghidra integration","description":"Integrates Ghidra reverse engineering platform via ghidra_analyze() MCP tool, enabling AI agents to analyze binary files, decompile code, and identify vulnerabilities. Agents interpret decompiled code, recognize common vulnerability patterns (buffer overflows, format strings, use-after-free), and generate exploitation strategies. Supports automated function identification, string extraction, and cross-reference analysis to understand binary behavior without manual reverse engineering.","intents":["I want to automatically analyze a binary and identify potential vulnerabilities","I need to understand what a compiled executable does without manually reverse engineering it","I want to generate exploitation strategies based on identified code vulnerabilities"],"best_for":["Security researchers analyzing malware and vulnerable binaries","CTF competitors solving reverse engineering challenges","Teams conducting binary security assessments"],"limitations":["Decompilation accuracy varies by binary complexity, optimization level, and architecture; heavily optimized binaries produce poor decompilation","Ghidra analysis is time-consuming for large binaries (>100MB); analysis may timeout or consume excessive memory","Vulnerability pattern recognition is limited to known patterns; novel vulnerability types may not be detected","Obfuscation and anti-analysis techniques (packing, encryption, anti-debugging) can defeat analysis","Cross-architecture support is limited; analysis quality varies significantly between x86, ARM, MIPS, etc.","No built-in dynamic analysis; static analysis alone may miss runtime behavior and side effects"],"requires":["Binary file (ELF, PE, Mach-O, or other supported format)","Ghidra installed and configured with appropriate language modules","AI agent for code interpretation and vulnerability pattern recognition","Sufficient compute resources for decompilation (4GB+ RAM recommended)","Optional: debug symbols or DWARF information for improved analysis"],"input_types":["compiled binary file (executable, shared library, firmware)","binary architecture and format metadata","analysis scope (full binary vs specific functions)","vulnerability pattern preferences (memory safety, logic flaws, etc.)"],"output_types":["decompiled code in pseudocode or C-like syntax","identified functions with signatures and cross-references","extracted strings and constants","vulnerability findings with code locations and severity","exploitation strategy recommendations","control flow and data flow analysis results"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-0x4m4--hexstrike-ai__cap_9","uri":"capability://automation.workflow.cloud.infrastructure.security.assessment.aws.azure.gcp","name":"cloud infrastructure security assessment (aws/azure/gcp)","description":"Implements cloud security assessment via prowler_assess() MCP tool that audits AWS, Azure, and GCP configurations against CIS benchmarks and compliance frameworks (PCI-DSS, HIPAA, SOC2). Agents analyze cloud resource configurations (IAM policies, security groups, encryption settings, logging) and identify misconfigurations, overprivileged accounts, and compliance violations. Generates remediation recommendations aligned with cloud provider best practices and organizational policies.","intents":["I want to audit my AWS/Azure/GCP environment for security misconfigurations and compliance violations","I need to identify overprivileged IAM roles and excessive permissions in my cloud infrastructure","I want to generate a compliance report showing which CIS benchmarks my cloud environment violates"],"best_for":["Cloud security teams auditing multi-cloud environments","Organizations preparing for compliance audits (PCI-DSS, HIPAA, SOC2)","DevOps teams integrating security checks into infrastructure-as-code pipelines"],"limitations":["Assessment accuracy depends on IAM permissions; limited credentials may miss resources in other accounts or regions","Prowler checks are point-in-time snapshots; continuous monitoring requires scheduled assessments","Remediation recommendations are generic; organization-specific policies may require manual customization","Multi-cloud assessment requires separate credentials for each provider; no unified credential management","Assessment scope is limited to cloud provider APIs; on-premises resources or hybrid cloud require separate tools"],"requires":["Cloud provider credentials (AWS access keys, Azure service principal, GCP service account)","Prowler installed and configured with appropriate cloud provider SDKs","AI agent for configuration analysis and remediation recommendation","Cloud provider API access (read-only minimum for assessment)","Compliance framework definitions (CIS benchmarks, PCI-DSS, HIPAA, SOC2)"],"input_types":["cloud provider credentials and account IDs","regions to assess (all or specific)","compliance frameworks to check against","resource filters (specific resource types, tags)","custom policy definitions (optional)"],"output_types":["list of misconfigurations with severity levels","compliance violation findings mapped to framework requirements","IAM policy analysis with overprivileged role identification","encryption and logging configuration audit results","remediation recommendations with implementation steps","compliance report with pass/fail status per benchmark"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","FastMCP library (MCP implementation)","Claude API key, OpenAI API key, or local LLM with MCP support","Kali Linux or equivalent security tool suite (nmap, gobuster, nuclei, sqlmap, etc.)","Network access to target systems (for scanning tools)","Target information (IP, domain, or service endpoints)","Network access to perform initial reconnaissance","Populated tool metadata and capability mappings in hexstrike_server.py","AI client configured (Claude, GPT, or local LLM) for decision logic","nuclei installed with template repository"],"failure_modes":["MCP protocol adds ~200-500ms latency per tool invocation due to serialization and deserialization","Tool output context window limited by LLM token limits — large scan results may be truncated or summarized","No built-in result persistence across agent sessions — requires external state management for multi-session workflows","Requires explicit tool registration and schema definition; adding new tools requires code changes to hexstrike_mcp.py","Target analysis accuracy depends on initial reconnaissance data quality — incomplete fingerprinting leads to suboptimal tool selection","Recommendation engine is rule-based or heuristic; no machine learning model tuning per organization's tool preferences","Cannot recommend tools for zero-day or novel attack vectors not in the training/rule set","Requires network connectivity to target for fingerprinting; offline target analysis not supported","Template quality varies; community templates may have high false-positive rates or miss edge cases","Template execution time is unpredictable; complex templates may timeout on slow networks","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6690993890046861,"quality":0.6,"ecosystem":0.8,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-04-27T08:56:07Z"},"community":{"stars":8485,"forks":1841,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=0x4m4--hexstrike-ai","compare_url":"https://unfragile.ai/compare?artifact=0x4m4--hexstrike-ai"}},"signature":"9ouKj0i+M5ElpXsGJO4T3G1xgJCEX19wz4b7KihtLm6HD0X64yx8lg13+Gsc8FR2G95+kKukDqyqw8AfGoy6Dw==","signedAt":"2026-06-21T01:42:26.401Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/0x4m4--hexstrike-ai","artifact":"https://unfragile.ai/0x4m4--hexstrike-ai","verify":"https://unfragile.ai/api/v1/verify?slug=0x4m4--hexstrike-ai","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"}}