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 vstorm-co · MCP Server · ★ 121
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is pydantic-ai-backend safe to install? View the security audit →
File Storage & Sandbox Backends for Pydantic AI Console Toolset, Docker Sandbox, and Permission System for AI Agents Console Toolset — ls, read, write, edit, grep, execute • Docker Sandbox — isolated code execution • Permission System — fine-grained access control File Storage & Sandbox Backends provides everything your Pydantic AI agent needs to work with files and execute code safely. Choose from in-memory, local filesystem, or Docker-isolated backends. Full framework? Check out [Pydan
| Stars | 121 |
| Forks | 25 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70.971259688347/100 |
| Open Issues | 5 |
| Last Updated | 2026-08-22 |
| Created | 2025-12-28 |
| Platforms | docker, gemini, mcp, python |
| Est. Tokens | ~16k |
These tools work well together with pydantic-ai-backend for enhanced workflows:
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pydantic-ai-backend is File Storage & Sandbox Backends for Pydantic AI: console tools for file operations, Docker-isolated sandboxes for safe execution, and permission system with presets for access control. Enables secure . It is categorized as a MCP Server with 121 GitHub stars.
pydantic-ai-backend is primarily written in Python. It covers topics such as ai-agent-framework, aiagentframework, anthropic.
You can find installation instructions and usage details in the pydantic-ai-backend GitHub repository at github.com/vstorm-co/pydantic-ai-backend. The project has 121 stars and 25 forks, indicating an active community.
pydantic-ai-backend is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to pydantic-ai-backend on Agent Skills Hub include corpusos, skene, agentsilex. 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: