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 derisk-ai · MCP Server · ★ 974
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenDerisk safe to install? View the security audit →
OpenDeRisk OpenDeRisk is an AI-Native Risk Intelligence System designed as your application system's intelligent manager, providing 7×24 hour comprehensive and in-depth protection. English [Vid
| Stars | 974 |
| Forks | 129 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 65.0347844209862/100 |
| Open Issues | 8 |
| Last Updated | 2026-09-17 |
| Created | 2025-04-23 |
| Platforms | mcp, python |
| Est. Tokens | ~15k |
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OpenDerisk is AI-Native Risk Intelligence Systems, OpenDeRisk——Your application system risk intelligent manager provides 7* 24-hour comprehensive and in-depth protection.. It is categorized as a MCP Server with 974 GitHub stars.
OpenDerisk is primarily written in Python. It covers topics such as agent, ai-sre, aigc.
You can find installation instructions and usage details in the OpenDerisk GitHub repository at github.com/derisk-ai/OpenDerisk. The project has 974 stars and 129 forks, indicating an active community.
OpenDerisk is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to OpenDerisk on Agent Skills Hub include aura, ai-agents-from-zero, argo. 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: