CyberStrikeAI — AI-Native Cybersecurity Action System
Turn intent into governed security actions, make evidence operational memory, and let every operation improve the next.
Evidence shows RBAC, approval modes, tool allowlists, audit logs, but lacks implementation details or verification. Deductions: no code-level evidence, least privilege, user confirmation, data flow transparency are only documented claims.
Documentation describes a consistent architecture and features, but dependency versions are not pinned (e.g., pseudo-versions in go.mod), and no specific failure message examples. Deductions: dependency availability unverified, failure messages lack concrete evidence.
Clearly targets security testers, provides multiple scenarios and deployment methods, but capability boundaries and trigger precision are insufficiently described. Deductions: capability boundaries unclear, trigger conditions lack precise specification.
Information architecture is clear, install notes are detailed, but naming stability, known limitations, versioning/changelog, and maintenance responsibility are insufficient. Deductions: missing version history, known limitations, and clear maintenance responsibility statement.
Output usability is good, with rich tools and UI, but cost-benefit analysis is missing. Deductions: no performance or resource consumption data, marginal value based only on feature list.
Claims in documentation lack traceable code or test evidence, and facts are not separated from inferences. Deductions: claims unverifiable, lack cross-source corroboration.
- This tool includes high-risk capabilities (e.g., WebShell, C2, command execution) and must only be used in authorized environments.
- Dependency versions are not pinned, posing supply chain risks; recommend using fixed versions and regular audits.
- Publisher identity is unverified; assess source trustworthiness carefully.
What does this agent do, and when should you use it?
CyberStrikeAI is a Go-based AI cybersecurity platform that unifies planning, execution, human oversight, evidence, and replay in one auditable workspace. It combines Eino-powered agents, MCP-native tools, RAG knowledge, visual workflows, and attack-chain modeling for authorized security operations. The project ships with 100+ curated YAML tool recipes, role configurations, skills, and high-risk capabilities like WebShell and C2, along with a web console for task governance, vulnerability and asset management, RBAC permissions, and audit logs. Deployment is self-hosted with a one-command script, and it supports configurable AI providers.
CyberStrikeAI reads natural-language intent and translates it into governed, auditable security actions via Eino-orchestrated agents (supports single-agent, Deep, Plan-Execute, and Supervisor modes). It invokes the built-in 100+ security tools (e.g., nmap, sqlmap, nuclei), integrates external tools via MCP, and enhances analysis with RAG knowledge and vision models. Tool outputs (capped in size) are stored in SQLite for audit and replay. It provides a web console for managing tasks, vulnerabilities, assets, WebShell sessions, and C2 beacons, and includes Burp Suite and browser extensions. Deployment is via run.sh, which compiles the Go binary and starts an HTTPS server, with configurable AI channels (e.g., OpenAI-compatible endpoints).
- Security researchers run authorized port scans and vulnerability enumeration against target assets.
- Penetration testers leverage built-in Metasploit, Sqlmap, and other tools for in-depth exploitation.
- Enterprise security teams use asset and vulnerability management to track exposure and risk.
- Red teams use WebShell and C2 for post-exploitation and command-and-control operations.
- CTF players solve challenges using password cracking and steganography utilities.
- DevSecOps engineers integrate vulnerability scanning into CI/CD pipelines to improve application security.
What are this agent's strengths and limitations?
- Comprehensive governance: human approval, tool allowlists, audit-agent review, and audit logs
- 100+ prebuilt tool recipes covering the entire kill chain
- Advanced features like WebShell management and built-in C2 for post-exploitation
- Multiple agent orchestration modes and visual workflows for complex scenarios
- Model-provider agnostic via configurable AI channels
- Asset and vulnerability management modules for tracking and reporting
- High-risk features (WebShell, C2) require strict authorization and careful handling
- Default self-signed certificate requires accepting browser warnings
- Requires external AI API keys; no built-in model
- Some tools need separate installation; missing tools are skipped or substituted at runtime
- Upgrade may introduce compatibility issues; must review release notes and back up
- Requires Go and Python environments, adding deployment complexity
How do you install or deploy this agent?
Prerequisites: Go 1.25+ and Python 3.10+. Run: git clone https://github.com/Ed1s0nZ/CyberStrikeAI.git && cd CyberStrikeAI && chmod +x run.sh && ./run.sh. The script automatically creates a Python virtual environment, downloads dependencies, and starts the server. On first launch, the console prints a one-time admin password and the Web UI URL (default https://127.0.0.1:8080). Configure an AI channel in System Settings (provider, API key, Base URL, model). Optionally install security tools, e.g., on macOS: brew install nmap masscan sqlmap nikto gobuster ffuf hydra hashcat nuclei subfinder.
How do you use this agent?
Open the Web UI and log in. After first login, navigate to System Settings -> Basic Settings -> AI Channel Configuration, add or edit a channel, and save. Create users or use the default admin account. Start a new conversation and issue commands like "Scan open ports on 192.168.1.1"; the agent will execute the appropriate tools and return results. Use preconfigured advanced playbooks for complex workflows. Manage results via Task Management, Vulnerability Management, and Asset modules. Upgrade with upgrade.sh, but back up config.yaml and data/ first.
How does this agent compare with similar options?
The README does not explicitly name direct competitors, but it can be compared to other AI penetration testing tools like PentestGPT or Caldera in terms of capabilities.