Inspiration

Modern applications ship faster than ever, but security hasn’t kept up. Traditional penetration testing is expensive, manual, and often happens too late in the development cycle. We were inspired by the idea of making offensive security accessible, continuous, and automated—bringing red team capabilities directly into the hands of developers using AI.

We also wanted to explore how multi-agent systems could simulate real-world adversarial thinking, not just static code analysis.

What it does

red-team-ai-agent is an AI-powered security testing system that simulates a professional red team.

It:

Analyzes codebases (via GitHub or direct input) Generates realistic attack scenarios (e.g., SQL injection, SSRF, IDOR) Identifies actual exploitable vulnerabilities Assesses business and technical impact Suggests concrete fixes with code examples Re-tests the fixes to validate security improvements

All of this happens through an automated multi-agent pipeline, with results delivered in a clean, downloadable report.

How we built it

We built the system using a multi-agent architecture powered by LLMs:

Streamlit for the interactive UI Claude (Anthropic API) for intelligent agent reasoning Python backend with modular orchestration

Core components:

Agent Orchestrator → Controls the pipeline flow LLM Agents → Specialized roles (attack, exploit, impact, fix, re-test) GitHub Integration → Fetches and filters relevant files Smart File Selection → Focuses on high-risk files (auth, routes, configs)

We optimized for:

Minimal API usage (combined agent calls) Real-time feedback (streaming UI updates) Practical outputs (actionable fixes, not just detection) Challenges we ran into Balancing depth vs. API limits Deep security analysis can be expensive. We had to design agents that produce high-value output with minimal calls. Reducing hallucinations Ensuring vulnerabilities are realistic and tied to actual code—not generic guesses. File prioritization Large repos can overwhelm analysis. We built logic to focus only on security-critical files. Agent coordination Making multiple agents build on each other’s outputs without redundancy or contradiction. UX clarity Presenting complex security findings in a way that’s understandable to non-experts. Accomplishments that we're proud of Built a fully working multi-agent red team pipeline Generated realistic, actionable security reports Reduced API usage by combining agent roles intelligently Created a developer-friendly UI for complex security workflows Enabled end-to-end testing → fixing → validation loop Designed a system usable by non-security engineers

What we learned

Multi-agent systems are powerful, but coordination and prompt design are everything Security is not just about detection—it’s about context, impact, and remediation Developers value clear fixes more than vulnerability lists LLMs can simulate adversarial thinking surprisingly well when guided properly Trade-offs between cost, speed, and depth are critical in real-world AI systems What's next for red-team-ai-agent 🔄 CI/CD integration (GitHub Actions, GitLab pipelines) 🧠 Add more specialized agents (dependency scanning, API fuzzing, secrets detection) 🌐 Support for more languages and frameworks 📊 Risk dashboards and historical tracking 🔌 Integrations (Slack, Jira, email alerts) 🛡️ Real-time monitoring + continuous security testing 🤝 Team collaboration features (shared reports, comments) 🧪 Hybrid approach with traditional SAST/DAST tools

Built With

• langraph
• llm
• streamlit