CyberStrikeAI

<div align="center"> <img src="web/static/logo.png" alt="CyberStrikeAI Logo" width="200"> </div> # CyberStrikeAI [中文](README_CN.md) | [English](README.md) CyberStrikeAI is an **AI-native security testing platform** built in Go. It integrates 100+ security tools, an intelligent orchestration engine, role-based testing with predefined security roles, a skills system with specialized testing skills, and comprehensive lifecycle management capabilities. Through native MCP protocol and AI agents, it enables end-to-end automation from conversational commands to vulnerability discovery, attack-chain analysis, knowledge retrieval, and result visualization—delivering an auditable, traceable, and collaborative testing environment for security teams. ## Interface & Integration Preview <div align="center"> ### Core Features Overview <table> <tr> <td width="33.33%" align="center"> <strong>Web Console</strong><br/> <img src="./images/web-console.png" alt="Web Console" width="100%"> </td> <td width="33.33%" align="center"> <strong>Attack Chain Visualization</strong><br/> <img src="./images/attack-chain.png" alt="Attack Chain" width="100%"> </td> <td width="33.33%" align="center"> <strong>Task Management</strong><br/> <img src="./images/task-management.png" alt="Task Management" width="100%"> </td> </tr> <tr> <td width="33.33%" align="center"> <strong>Vulnerability Management</strong><br/> <img src="./images/vulnerability-management.png" alt="Vulnerability Management" width="100%"> </td> <td width="33.33%" align="center"> <strong>MCP Management</strong><br/> <img src="./images/mcp-management.png" alt="MCP management" width="100%"> </td> <td width="33.33%" align="center"> <strong>MCP stdio Mode</strong><br/> <img src="./images/mcp-stdio2.png" alt="MCP stdio mode" width="100%"> </td> </tr> <tr> <td width="33.33%" align="center"> <strong>Knowledge Base</strong><br/> <img src="./images/knowledge-base.png" alt="Knowledge Base" width="100%"> </td> <td width="33.33%" align="center"> <strong>Skills Management</strong><br/> <img src="./images/skills.png" alt="Skills Management" width="100%"> </td> <td width="33.33%" align="center"> <strong>Role Management</strong><br/> <img src="./images/role-management.png" alt="Role Management" width="100%"> </td> </tr> </table> </div> ## Highlights - 🤖 AI decision engine with OpenAI-compatible models (GPT, Claude, DeepSeek, etc.) - 🔌 Native MCP implementation with HTTP/stdio/SSE transports and external MCP federation - 🧰 100+ prebuilt tool recipes + YAML-based extension system - 📄 Large-result pagination, compression, and searchable archives - 🔗 Attack-chain graph, risk scoring, and step-by-step replay - 🔒 Password-protected web UI, audit logs, and SQLite persistence - 📚 Knowledge base with vector search and hybrid retrieval for security expertise - 📁 Conversation grouping with pinning, rename, and batch management - 🛡️ Vulnerability management with CRUD operations, severity tracking, status workflow, and statistics - 📋 Batch task management: create task queues, add multiple tasks, and execute them sequentially - 🎭 Role-based testing: predefined security testing roles (Penetration Testing, CTF, Web App Scanning, etc.) with custom prompts and tool restrictions - 🎯 Skills system: 20+ predefined security testing skills (SQL injection, XSS, API security, etc.) that can be attached to roles or called on-demand by AI agents ## Tool Overview CyberStrikeAI ships with 100+ curated tools covering the whole kill chain: - **Network Scanners** – nmap, masscan, rustscan, arp-scan, nbtscan - **Web & App Scanners** – sqlmap, nikto, dirb, gobuster, feroxbuster, ffuf, httpx - **Vulnerability Scanners** – nuclei, wpscan, wafw00f, dalfox, xsser - **Subdomain Enumeration** – subfinder, amass, findomain, dnsenum, fierce - **Network Space Search Engines** – fofa_search, zoomeye_search - **API Security** – graphql-scanner, arjun, api-fuzzer, api-schema-analyzer - **Container Security** – trivy, clair, docker-bench-security, kube-bench, kube-hunter - **Cloud Security** – prowler, scout-suite, cloudmapper, pacu, terrascan, checkov - **Binary Analysis** – gdb, radare2, ghidra, objdump, strings, binwalk - **Exploitation** – metasploit, msfvenom, pwntools, ropper, ropgadget - **Password Cracking** – hashcat, john, hashpump - **Forensics** – volatility, volatility3, foremost, steghide, exiftool - **Post-Exploitation** – linpeas, winpeas, mimikatz, bloodhound, impacket, responder - **CTF Utilities** – stegsolve, zsteg, hash-identifier, fcrackzip, pdfcrack, cyberchef - **System Helpers** – exec, create-file, delete-file, list-files, modify-file ## Basic Usage ### Quick Start (One-Command Deployment) **Prerequisites:** - Go 1.21+ ([Install](https://go.dev/dl/)) - Python 3.10+ ([Install](https://www.python.org/downloads/)) **One-Command Deployment:** ```bash git clone https://github.com/Ed1s0nZ/CyberStrikeAI.git cd CyberStrikeAI-main chmod +x run.sh && ./run.sh ``` The `run.sh` script will automatically: - ✅ Check and validate Go & Python environments - ✅ Create Python virtual environment - ✅ Install Python dependencies - ✅ Download Go dependencies - ✅ Build the project - ✅ Start the server **First-Time Configuration:** 1. **Configure OpenAI-compatible API** (required before first use) - Open http://localhost:8080 after launch - Go to `Settings` → Fill in your API credentials: ```yaml openai: api_key: "sk-your-key" base_url: "https://api.openai.com/v1" # or https://api.deepseek.com/v1 model: "gpt-4o" # or deepseek-chat, claude-3-opus, etc. ``` - Or edit `config.yaml` directly before launching 2. **Login** - Use the auto-generated password shown in the console (or set `auth.password` in `config.yaml`) 3. **Install security tools (optional)** - Install tools as needed: ```bash # macOS brew install nmap sqlmap nuclei httpx gobuster feroxbuster subfinder amass # Ubuntu/Debian sudo apt-get install nmap sqlmap nuclei httpx gobuster feroxbuster ``` AI automatically falls back to alternatives when a tool is missing. **Alternative Launch Methods:** ```bash # Direct Go run (requires manual setup) go run cmd/server/main.go # Manual build go build -o cyberstrike-ai cmd/server/main.go ./cyberstrike-ai ``` **Note:** The Python virtual environment (`venv/`) is automatically created and managed by `run.sh`. Tools that require Python (like `api-fuzzer`, `http-framework-test`, etc.) will automatically use this environment. ### Core Workflows - **Conversation testing** – Natural-language prompts trigger toolchains with streaming SSE output. - **Role-based testing** – Select from predefined security testing roles (Penetration Testing, CTF, Web App Scanning, API Security Testing, etc.) to customize AI behavior and tool availability. Each role applies custom system prompts and can restrict available tools for focused testing scenarios. - **Tool monitor** – Inspect running jobs, execution logs, and large-result attachments. - **History & audit** – Every conversation and tool invocation is stored in SQLite with replay. - **Conversation groups** – Organize conversations into groups, pin important groups, rename or delete groups via context menu. - **Vulnerability management** – Create, update, and track vulnerabilities discovered during testing. Filter by severity (critical/high/medium/low/info), status (open/confirmed/fixed/false_positive), and conversation. View statistics and export findings. - **Batch task management** – Create task queues with multiple tasks, add or edit tasks before execution, and run them sequentially. Each task executes as a separate conversation, with status tracking (pending/running/completed/failed/cancelled) and full execution history. - **Settings** – Tweak provider keys, MCP enablement, tool toggles, and agent iteration limits. ### Built-in Safeguards - Required-field validation prevents accidental blank API credentials. - Auto-generated strong passwords when `auth.password` is empty. - Unified auth middleware for every web/API call (Bearer token flow). - Timeout and sandbox guards per tool, plus structured logging for triage. ## Advanced Usage ### Role-Based Testing - **Predefined roles** – System includes 12+ predefined security testing roles (Penetration Testing, CTF, Web App Scanning, API Security Testing, Binary Analysis, Cloud Security Audit, etc.) in the `roles/` directory. - **Custom prompts** – Each role can define a `user_prompt` that prepends to user messages, guiding the AI to adopt specialized testing methodologies and focus areas. - **Tool restrictions** – Roles can specify a `tools` list to limit available tools, ensuring focused testing workflows (e.g., CTF role restricts to CTF-specific utilities). - **Skills integration** – Roles can attach security testing skills. Skill names are added to system prompts as hints, and AI agents can access skill content on-demand using the `read_skill` tool. - **Easy role creation** – Create custom roles by adding YAML files to the `roles/` directory. Each role defines `name`, `description`, `user_prompt`, `icon`, `tools`, `skills`, and `enabled` fields. - **Web UI integration** – Select roles from a dropdown in the chat interface. Role selection affects both AI behavior and available tool suggestions. **Creating a custom role (example):** 1. Create a YAML file in `roles/` (e.g., `roles/custom-role.yaml`): ```yaml name: Custom Role description: Specialized testing scenario user_prompt: You are a specialized security tester focusing on API security... icon: "\U0001F4E1" tools: - api-fuzzer - arjun - graphql-scanner skills: - api-security-testing - sql-injection-testing enabled: true ``` 2. Restart the server or reload configuration; the role appears in the role selector dropdown. ### Skills System - **Predefined skills** – System includes 20+ predefined security testing skills (SQL injection, XSS, API security, cloud security, container security, etc.) in the `skills/` directory. - **Skill hints in prompts** – When a role is selected, skill names attached to that role are added to the system prompt as recommendations. Skill content is not automatically injected; AI agents must use the `read_skill` tool to access skill details when needed. - **On-demand access** – AI agents can also access skills on-demand using built-in tools (`list_skills`, `read_skill`), allowing dynamic skill retrieval during task execution. - **Structured format** – Each skill is a directory containing a `SKILL.md` file with detailed testing methods, tool usage, best practices, and examples. Skills support YAML front matter for metadata. - **Custom skills** – Create custom skills by adding directories to the `skills/` directory. Each skill directory should contain a `SKILL.md` file with the skill content. **Creating a custom skill:** 1. Create a directory in `skills/` (e.g., `skills/my-skill/`) 2. Create a `SKILL.md` file in that directory with the skill content 3. Attach the skill to a role by adding it to the role's `skills` field in the role YAML file ### Tool Orchestration & Extensions - **YAML recipes** in `tools/*.yaml` describe commands, arguments, prompts, and metadata. - **Directory hot-reload** – pointing `security.tools_dir` to a folder is usually enough; inline definitions in `config.yaml` remain supported for quick experiments. - **Large-result pagination** – outputs beyond 200 KB are stored as artifacts retrievable through the `query_execution_result` tool with paging, filters, and regex search. - **Result compression** – multi-megabyte logs can be summarized or losslessly compressed before persisting to keep SQLite lean. **Creating a custom tool (typical flow)** 1. Copy an existing YAML file from `tools/` (for example `tools/sample.yaml`). 2. Update `name`, `command`, `args`, and `short_description`. 3. Describe positional or flag parameters in `parameters[]` so the agent knows how to build CLI arguments. 4. Provide a longer `description`/`notes` block if the agent needs extra context or post-processing tips. 5. Restart the server or reload configuration; the new tool becomes available immediately and can be enabled/disabled from the Settings panel. ### Attack-Chain Intelligence - AI parses each conversation to assemble targets, tools, vulnerabilities, and relationships. - The web UI renders the chain as an interactive graph with severity scoring and step replay. - Export the chain or raw findings to external reporting pipelines. ### MCP Everywhere - **Web mode** – ships with HTTP MCP server automatically consumed by the UI. - **MCP stdio mode** – `go run cmd/mcp-stdio/main.go` exposes the agent to Cursor/CLI. - **External MCP federation** – register third-party MCP servers (HTTP, stdio, or SSE) from the UI, toggle them per engagement, and monitor their health and call volume in real time. #### MCP stdio quick start 1. **Build the binary** (run from the project root): ```bash go build -o cyberstrike-ai-mcp cmd/mcp-stdio/main.go ``` 2. **Wire it up in Cursor** Open `Settings → Tools & MCP → Add Custom MCP`, pick **Command**, then point to the compiled binary and your config: ```json { "mcpServers": { "cyberstrike-ai": { "command": "/absolute/path/to/cyberstrike-ai-mcp", "args": [ "--config", "/absolute/path/to/config.yaml" ] } } } ``` Replace the paths with your local locations; Cursor will launch the stdio server automatically. #### MCP HTTP quick start 1. Ensure `config.yaml` has `mcp.enabled: true` and adjust `mcp.host` / `mcp.port` if you need a non-default binding (localhost:8081 works well for local Cursor usage). 2. Start the main service (`./run.sh` or `go run cmd/server/main.go`); the MCP endpoint lives at `http://<host>:<port>/mcp`. 3. In Cursor, choose **Add Custom MCP → HTTP** and set `Base URL` to `http://127.0.0.1:8081/mcp`. 4. Prefer committing the setup via `.cursor/mcp.json` so teammates can reuse it: ```json { "mcpServers": { "cyberstrike-ai-http": { "transport": "http", "url": "http://127.0.0.1:8081/mcp" } } } ``` #### External MCP federation (HTTP/stdio/SSE) CyberStrikeAI supports connecting to external MCP servers via three transport modes: - **HTTP mode** – traditional request/response over HTTP POST - **stdio mode** – process-based communication via standard input/output - **SSE mode** – Server-Sent Events for real-time streaming communication To add an external MCP server: 1. Open the Web UI and navigate to **Settings → External MCP**. 2. Click **Add External MCP** and provide the configuration in JSON format: **HTTP mode example:** ```json { "my-http-mcp": { "transport": "http", "url": "http://127.0.0.1:8081/mcp", "description": "HTTP MCP server", "timeout": 30 } } ``` **stdio mode example:** ```json { "my-stdio-mcp": { "command": "python3", "args": ["/path/to/mcp-server.py"], "description": "stdio MCP server", "timeout": 30 } } ``` **SSE mode example:** ```json { "my-sse-mcp": { "transport": "sse", "url": "http://127.0.0.1:8082/sse", "description": "SSE MCP server", "timeout": 30 } } ``` 3. Click **Save** and then **Start** to connect to the server. 4. Monitor the connection status, tool count, and health in real time. **SSE mode benefits:** - Real-time bidirectional communication via Server-Sent Events - Suitable for scenarios requiring continuous data streaming - Lower latency for push-based notifications A test SSE MCP server is available at `cmd/test-sse-mcp-server/` for validation purposes. ### Knowledge Base - **Vector search** – AI agent can automatically search the knowledge base for relevant security knowledge during conversations using the `search_knowledge_base` tool. - **Hybrid retrieval** – combines vector similarity search with keyword matching for better accuracy. - **Auto-indexing** – scans the `knowledge_base/` directory for Markdown files and automatically indexes them with embeddings. - **Web management** – create, update, delete knowledge items through the web UI, with category-based organization. - **Retrieval logs** – tracks all knowledge retrieval operations for audit and debugging. **Quick Start (Using Pre-built Knowledge Base):** 1. **Download the knowledge database** – Download the pre-built knowledge database file from [GitHub Releases](https://github.com/Ed1s0nZ/CyberStrikeAI/releases). 2. **Extract and place** – Extract the downloaded knowledge database file (`knowledge.db`) and place it in the project's `data/` directory. 3. **Restart the service** – Restart the CyberStrikeAI service, and the knowledge base will be ready to use immediately without rebuilding the index. **Setting up the knowledge base:** 1. **Enable in config** – set `knowledge.enabled: true` in `config.yaml`: ```yaml knowledge: enabled: true base_path: knowledge_base embedding: provider: openai model: text-embedding-v4 base_url: "https://api.openai.com/v1" # or your embedding API api_key: "sk-xxx" retrieval: top_k: 5 similarity_threshold: 0.7 hybrid_weight: 0.7 ``` 2. **Add knowledge files** – place Markdown files in `knowledge_base/` directory, organized by category (e.g., `knowledge_base/SQL Injection/README.md`). 3. **Scan and index** – use the web UI to scan the knowledge base directory, which will automatically import files and build vector embeddings. 4. **Use in conversations** – the AI agent will automatically use `search_knowledge_base` when it needs security knowledge. You can also explicitly ask: "Search the knowledge base for SQL injection techniques". **Knowledge base structure:** - Files are organized by category (directory name becomes the category). - Each Markdown file becomes a knowledge item with automatic chunking for vector search. - The system supports incremental updates – modified files are re-indexed automatically. ### Automation Hooks - **REST APIs** – everything the UI uses (auth, conversations, tool runs, monitor, vulnerabilities, roles) is available over JSON. - **Role APIs** – manage security testing roles via `/api/roles` endpoints: `GET /api/roles` (list all roles), `GET /api/roles/:name` (get role), `POST /api/roles` (create role), `PUT /api/roles/:name` (update role), `DELETE /api/roles/:name` (delete role). Roles are stored as YAML files in the `roles/` directory and support hot-reload. - **Vulnerability APIs** – manage vulnerabilities via `/api/vulnerabilities` endpoints: `GET /api/vulnerabilities` (list with filters), `POST /api/vulnerabilities` (create), `GET /api/vulnerabilities/:id` (get), `PUT /api/vulnerabilities/:id` (update), `DELETE /api/vulnerabilities/:id` (delete), `GET /api/vulnerabilities/stats` (statistics). - **Batch Task APIs** – manage batch task queues via `/api/batch-tasks` endpoints: `POST /api/batch-tasks` (create queue), `GET /api/batch-tasks` (list queues), `GET /api/batch-tasks/:queueId` (get queue), `POST /api/batch-tasks/:queueId/start` (start execution), `POST /api/batch-tasks/:queueId/cancel` (cancel), `DELETE /api/batch-tasks/:queueId` (delete), `POST /api/batch-tasks/:queueId/tasks` (add task), `PUT /api/batch-tasks/:queueId/tasks/:taskId` (update task), `DELETE /api/batch-tasks/:queueId/tasks/:taskId` (delete task). Tasks execute sequentially, each creating a separate conversation with full status tracking. - **Task control** – pause/resume/stop long scans, re-run steps with new params, or stream transcripts. - **Audit & security** – rotate passwords via `/api/auth/change-password`, enforce short-lived sessions, and restrict MCP ports at the network layer when exposing the service. ## Configuration Reference ```yaml auth: password: "change-me" session_duration_hours: 12 server: host: "0.0.0.0" port: 8080 log: level: "info" output: "stdout" mcp: enabled: true host: "0.0.0.0" port: 8081 openai: api_key: "sk-xxx" base_url: "https://api.deepseek.com/v1" model: "deepseek-chat" database: path: "data/conversations.db" knowledge_db_path: "data/knowledge.db" # Optional: separate DB for knowledge base security: tools_dir: "tools" knowledge: enabled: false # Enable knowledge base feature base_path: "knowledge_base" # Path to knowledge base directory embedding: provider: "openai" # Embedding provider (currently only "openai") model: "text-embedding-v4" # Embedding model name base_url: "" # Leave empty to use OpenAI base_url api_key: "" # Leave empty to use OpenAI api_key retrieval: top_k: 5 # Number of top results to return similarity_threshold: 0.7 # Minimum similarity score (0-1) hybrid_weight: 0.7 # Weight for vector search (1.0 = pure vector, 0.0 = pure keyword) roles_dir: "roles" # Role configuration directory (relative to config file) skills_dir: "skills" # Skills directory (relative to config file) ``` ### Tool Definition Example (`tools/nmap.yaml`) ```yaml name: "nmap" command: "nmap" args: ["-sT", "-sV", "-sC"] enabled: true short_description: "Network mapping & service fingerprinting" parameters: - name: "target" type: "string" description: "IP or domain" required: true position: 0 - name: "ports" type: "string" flag: "-p" description: "Range, e.g. 1-1000" ``` ### Role Definition Example (`roles/penetration-testing.yaml`) ```yaml name: Penetration Testing description: Professional penetration testing expert for comprehensive security testing user_prompt: You are a professional cybersecurity penetration testing expert. Please use professional penetration testing methods and tools to conduct comprehensive security testing on targets, including but not limited to SQL injection, XSS, CSRF, file inclusion, command execution and other common vulnerabilities. icon: "\U0001F3AF" tools: - nmap - sqlmap - nuclei - burpsuite - metasploit - httpx - record_vulnerability - list_knowledge_risk_types - search_knowledge_base enabled: true ``` ## Project Layout ``` CyberStrikeAI/ ├── cmd/ # Server, MCP stdio entrypoints, tooling ├── internal/ # Agent, MCP core, handlers, security executor ├── web/ # Static SPA + templates ├── tools/ # YAML tool recipes (100+ examples provided) ├── roles/ # Role configurations (12+ predefined security testing roles) ├── skills/ # Skills directory (20+ predefined security testing skills) ├── images/ # Docs screenshots & diagrams ├── config.yaml # Runtime configuration ├── run.sh # Convenience launcher └── README*.md ``` ## Basic Usage Examples ``` Scan open ports on 192.168.1.1 Perform a comprehensive port scan on 192.168.1.1 focusing on 80,443,22 Check if https://example.com/page?id=1 is vulnerable to SQL injection Scan https://example.com for hidden directories and outdated software Enumerate subdomains for example.com, then run nuclei against the results ``` ## Advanced Playbooks ``` Load the recon-engagement template, run amass/subfinder, then brute-force dirs on every live host. Use external Burp-based MCP server for authenticated traffic replay, then pass findings back for graphing. Compress the 5 MB nuclei report, summarize critical CVEs, and attach the artifact to the conversation. Build an attack chain for the latest engagement and export the node list with severity >= high. ``` ## Changelog ### Recent Highlights - **2026-01-27** – OpenAPI documentation with interactive testing interface, supporting conversation management, message interaction, and result querying - **2026-01-15** – Skills system with 20+ predefined security testing skills - **2026-01-11** – Role-based testing with predefined security testing roles - **2026-01-08** – SSE transport mode support for external MCP servers - **2026-01-01** – Batch task management with queue-based execution - **2025-12-25** – Vulnerability management and conversation grouping features - **2025-12-20** – Knowledge base with vector search and hybrid retrieval ## 404Starlink <img src="./images/404StarLinkLogo.png" width="30%"> CyberStrikeAI has joined [404Starlink](https://github.com/knownsec/404StarLink) ## TCH Top-Ranked Intelligent Pentest Project <div align="left"> <a href="https://zc.tencent.com/competition/competitionHackathon?code=cha004" target="_blank"> <img src="./images/tch.png" alt="TCH Top-Ranked Intelligent Pentest Project" width="30%"> </a> </div> --- Need help or want to contribute? Open an issue or PR—community tooling additions are welcome!