No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by pwno-io · MCP Server · ★ 278
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is pwno-mcp safe to install? View the security audit →
stateful system for autonomous pwn and binary research, designed for LLM agents. Overview runs GDB + pwndbg in an isolated environment and exposes stateful debugging, exploit I/O, and helper tooling over MCP for agentic coding clients. Features Stateful debugger sessions via GDB + pwndbg Deterministic execution control via GDB/MI Fast context snapshots for registers, stack, disassembly, source, and backtrace Interactive exploit-driver workflows with Multi-session support for parallel workflows Workspace automation helpers for commands, processes, Python, repos, and RetDec HTTP and stdio transport support Documentation The full documentation is available at docs.pwno.io. Quick Start Create a local directory, put your target binary there, then run the container. Default MCP
| Stars | 278 |
| Forks | 25 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 67.3562478341997/100 |
| Open Issues | 1 |
| Last Updated | 2026-03-12 |
| Created | 2025-07-18 |
| Platforms | mcp, python |
| Est. Tokens | ~54k |
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pwno-mcp is MCP for Pwn. It is categorized as a MCP Server with 278 GitHub stars.
pwno-mcp is primarily written in Python. It covers topics such as mcp, mcp-server, pwn.
You can find installation instructions and usage details in the pwno-mcp GitHub repository at github.com/pwno-io/pwno-mcp. The project has 278 stars and 25 forks, indicating an active community.
The top alternatives to pwno-mcp on Agent Skills Hub include lisa.py. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: