## 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://
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