Autonomous pentesting agent using feedback-driven iteration
Achieves ~78% on XBOW benchmarks with fully local execution and model-agnostic architecture.
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Warning
Active Development: This project is undergoing active development. Current features are functional but the interface and workflows are being improved based on new architecture and features.
📄 Read Technical Deep Dive | 📊 Benchmark Results (use VScode ANSI colors to view)
Deadend CLI is an autonomous web application penetration testing agent that uses feedback-driven iteration to adapt exploitation strategies. When standard tools fail, it generates custom Python payloads, observes responses, and iteratively refines its approach until breakthrough.
Key features:
- Fully local execution (no cloud dependencies, zero data exfiltration)
- Model-agnostic design (works with any deployable LLM)
- Custom sandboxed tools (Playwright, Docker, WebAssembly)
- ADaPT-based architecture with supervisor-subagent hierarchy
- Confidence-based decision making (fail <20%, expand 20-60%, refine 60-80%, validate >80%)
Benchmark results: 78% on XBOW validation suite (76/98 challenges), including blind SQL injection exploits where other agents achieved 0%.
Read the architecture breakdown in our technical article →
The framework focuses on intelligent security analysis through:
- 🔍 Taint Analysis: Automated tracking of data flow from sources to sinks
- 🎯 Source/Sink Detection: Intelligent identification of entry points and vulnerable functions
- 🔗 Contextual Tool Integration: Smart connection to specialized tools for testing complex logic patterns
- 🧠 AI-Driven Reasoning: Context-aware analysis that mimics expert security thinking
- Webapp-Specific Tooling: Custom tools designed specifically for web application penetration testing
- Authentication Handling: Built-in support for session management, cookies, and auth flows
- Fine-Grained Testing: Precise control over individual requests and parameters
- Payload Generation: AI-powered payload creation tailored to target context
- Automated Payload Testing: Generate, inject, and validate payloads in a single workflow
- Docker (required)
- Python 3.11+
- Playwright:
playwright install
# Install via pipx (recommended)
pipx install deadend_cli
# Or build from source
git clone https://github.com/xoxruns/deadend-cli.git
cd deadend-cli
uv sync && uv build# Initialize configuration
deadend init
# Start testing
deadend chat \
--target "http://localhost:3000" \
--prompt "find SQL injection vulnerabilities"# Test OWASP Juice Shop
docker run -p 3000:3000 bkimminich/juice-shop
deadend chat \
--target "http://localhost:3000" \
--prompt "test the login endpoint for SQL injection"deadend chat \
--target "https://api.example.com" \
--prompt "test authentication endpoints"# Run without approval prompts (CTFs/labs only)
deadend chat \
--target "http://ctf.example.com" \
--mode yolo \
--prompt "find and exploit all vulnerabilities"Initialize configuration and set up pgvector database
Start interactive security testing session
--target: Target URL--prompt: Initial testing prompt--mode:hacker(approval required) oryolo(autonomous)
Run evaluation against challenge datasets
--eval-metadata-file: Challenge dataset file--llm-providers: AI model providers to test--guided: Run with subtask decomposition
Display current version
The agent uses a two-phase approach (reconnaissance → exploitation) with a supervisor-subagent hierarchy:
Supervisor: Maintains high-level goals, delegates to specialized subagents Subagents: Focused toolsets (Requester for HTTP, Shell for commands, Python for payloads) Policy: Confidence scores (0-1.0) determine whether to fail, expand, refine, or validate
Key innovation: When standard tools fail, the agent generates custom exploitation scripts and iterates based on observed feedback—solving challenges like blind SQL injection where static toolchains achieve 0%.
Read full architecture details →
Note: To visualize the benchmark results properly, install an ANSI colors extension (e.g., ANSI Colors for VS Code) to render the rich output.
Evaluated on XBOW's 104-challenge validation suite (black-box mode, January 2026):
| Agent | Success Rate | Infrastructure | Blind SQLi |
|---|---|---|---|
| XBOW (proprietary) | 85% | Proprietary | ? |
| Cyber-AutoAgent | 85% (This is the latest Cyber-Autoagent scoring for october 2025) |
AWS Bedrock | 0% |
| Deadend CLI | 78% | Fully local | 33% |
| MAPTA | 76.9% | External APIs | 0% |
Models tested: Claude Sonnet 4.5 (~78%), Kimi K2 Thinking (~69%)
Strong performance: XSS (91%), Business Logic (86%), SQL injection (83%), IDOR (80%) Perfect scores: GraphQL, SSRF, NoSQL injection, HTTP method tampering (100%)
Hacker Mode (default): Requires approval for dangerous operations
deadend chat --target URL --mode hackerYOLO Mode: Autonomous execution (CTFs/labs only)
deadend chat --target URL --mode yolo- LiteLLM: Multi-provider model abstraction (OpenAI, Anthropic, Ollama)
- Instructor: Structured LLM outputs
- pgvector: Vector database for context
- Pyodide/WebAssembly: Python sandbox
- Playwright: HTTP request generation
- Docker: Shell command isolation
Configuration is managed via ~/.cache/deadend/config.toml. Run deadend init to set up your configuration interactively.
✅ New architecture ✅ XBOW benchmark evaluation (78%) ✅ Custom sandboxed tools ✅ Multi-model support with liteLLM ✅ Two-phase execution (recon + exploitation)
🚧 CLI Redesign with enhanced workflows:
- Plan mode (review strategies before execution)
- Preset configuration workflows (API testing, web apps, auth bypass)
- Workflow automation (save/replay attack chains)
🚧 Context optimization (reduce redundant tool calls) 🚧 Secrets management improvements
The current architecture proves competitive autonomous pentesting (78%) is achievable without cloud dependencies. Next challenges:
- Open-Source Models: Achieve 75%+ with Llama/Qwen (eliminate proprietary dependencies)
- Hybrid Testing: Add AST analysis for white-box code inspection
- Adversarial Robustness: Train against WAFs, rate limiting, adaptive defenses
- Multi-Target Orchestration: Test interconnected systems simultaneously
- Context Efficiency: Better information sharing between components
Goal: Make autonomous pentesting accessible (open models), comprehensive (hybrid testing), and robust (works against real defenses).
Contributions welcome in:
- Context optimization algorithms
- Vulnerability test cases
- Open-weight model fine-tuning
- Adversarial testing scenarios
See CONTRIBUTING.md for guidelines on how to contribute.
@software{deadend_cli_2026,
author = {Yassine Bargach},
title = {Deadend CLI: Feedback-Driven Autonomous Pentesting},
year = {2026},
url = {https://github.com/xoxruns/deadend-cli}
}For authorized security testing only. Unauthorized testing is illegal. Users are responsible for compliance with all applicable laws and obtaining proper authorization.
Have questions, feedback, or want to collaborate?
- 📧 Email: yassine@straylabs.ai
- 💬 Discord: xoxruns
- 💼 LinkedIn: Yassine Bargach
- 🐦 Twitter: @xoxruns
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