by xoxruns · Agent Tool · ★ 236
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Deadend CLI Autonomous pentesting agent using feedback-driven iteration Achieves 80% on the full XBOW validation benchmark with Kimi K2.5 at US$122 total API cost for that end-to-end run, with a model-agnostic architecture that supports other deployable LLMs. Like the project or want to know more? Feel free to reach out! [!WARNING] Active Development: This project is undergoing active development. Core features are stable and production-ready, but we're continuously improving the interface, workflows, and adding new capabilities based on user feedback. Check out the roadmap or open a issue for a future issue. [!NOTE] For discussions, research, and feature ideas, join the community Discord: Deadend CLI Discord. 📄 Read Technical Deep Dive | 📊 Benchmark Results (use VScode ANSI colors to view) Table of Contents What is Deadend CLI? Architecture Summary Benchmark Results [Core Analysis Capabilit
| Stars | 236 |
| Forks | 41 |
| Language | Python |
| Category | Agent Tool |
| License | AGPL-3.0 |
| Quality Score | 41.4/100 |
| Open Issues | 8 |
| Last Updated | 2026-05-11 |
| Created | 2025-07-22 |
| Platforms | cli, python |
| Est. Tokens | ~22151k |
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deadend-cli is Agentic pentest tooling. Currently achieving 81% (KIMI K2.5) on XBOW's benchmark in full black-box. Completely Self-hosted. Every model available on LiteLLM (Ollama, anthropic, openai...). It is categorized as a Agent Tool with 236 GitHub stars.
deadend-cli is primarily written in Python. It covers topics such as agentic-ai, ai, cybersecurity.
You can find installation instructions and usage details in the deadend-cli GitHub repository at github.com/xoxruns/deadend-cli. The project has 236 stars and 41 forks, indicating an active community.
deadend-cli is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to deadend-cli on Agent Skills Hub include mcp-security-hub, apex, pentest-ai. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.