arXiv:cs.AI· Dimitrios Prasakis·· 4 小时前AI 评分43
SideKernel:面向 macOS 上 AI 编程智能体的可用 microVM 沙箱
SideKernel: A Usable microVM Sandbox for AI Coding Agents on macOS
AI 导读
SideKernel 是一个面向 AI 编程智能体的开源本地 microVM macOS 沙箱,主打易用性。一项在线用户调查显示,不到 40% 的 AI 编程智能体用户会在沙箱中运行智能体,该调查还识别出阻碍沙箱采用的主要可用性障碍。在基于 23 项能力测试的对比分析中,Docker Sandboxes 与 SideKernel 在可用性相关能力上得分最高。
正文
Abstract:AI coding agents are untrusted system components, yet they require autonomy on the developer machines they run on. This contradiction is a security problem. Sandboxes provide an isolated environment, but for local macOS development, the existing local, open-source options for AI coding agents are few in number and cumbersome to use. I conducted a formative online user survey which indicates that fewer than 40% of AI coding agent users run their agents in a sandbox and identifies the top usability barriers hindering AI coding agent sandbox adoption. These findings are used to develop SideKernel: an open-source, local, microVM-based macOS sandbox for AI coding agents designed for usability. To evaluate SideKernel, I compiled a list of sandboxes available on the market and filtered it against five inclusion criteria. Then I performed a comparative analysis between SideKernel and the sandboxes that satisfy these criteria, across 23 capability tests derived from the usability barriers revealed by the user survey. I discovered that only a few sandboxes are similar to SideKernel, and that among those, Docker Sandboxes and SideKernel score highest on capability features related to usability. A secondary contribution of this paper is a survey of the existing solution space for local, open-source, microVM-based macOS sandboxes for AI coding agents.
| Comments: | 17 pages, 5 figures, 9 tables. Georgia Tech M.S. Cybersecurity practicum project |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.02456 [cs.CR] |
| (or arXiv:2610.02456v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02456 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dimitrios Prasakis [view email]
[v1]
Thu, 1 Oct 2026 20:30:13 UTC (437 KB)
来源:arXiv:cs.AI · arxiv.org