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None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 105 |
| Forks | 16 |
| Language | Go |
| Category | AI Tool |
| Quality Score | 52/100 |
| Last Updated | 2026-09-14 |
| Created | 2026-06-02 |
| Platforms | go |
| Est. Tokens | ~16k |
下一代Agent代码审计 简短的来说，我不太喜欢现在开源的大部分的"代码审计"系统的复杂性和花里胡哨的UI.因此我开发了这套代码审计agent系统并且用于测评我的QWEN-EXO项目 《qwen-exo - 让 Qwen 在长任务里真正记住知识、反思错误，并跑得足够快》 https://github.com/huoji120/QWEN-EXO-booster 它具有以下特点 完美适配QWEN-EXO,支持高速高效离离线部署,你只需要两台DGX就行.(外接模型也可以,也完美适配deepseek) agent跟团队一样在研究问题,模型之间会互相配合,他们会像一支专业团队一样,在内部地下论坛进行交流,记笔记,分工合作,分享成果,一起挖漏洞. 不依赖第三方库,编译即可用,高效简洁,我非常讨厌乱七八糟的WEBUI以及花里胡哨的东西,简洁才好用.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: