Cursor uses Apple’s Seatbelt (sandbox-exec) on macOS and Landlock plus seccomp on Linux. It generates a dynamic policy at runtime based on the workspace: the agent can read and write the open workspace and /tmp, read the broader filesystem, but cannot write elsewhere or make network requests without explicit approval. This reduced agent interruptions by roughly 40% compared to requiring approval for every command, because the agent runs freely within the fence and only asks when it needs to step outside.
return f"Item(url={self.url}, title={self.title[:30]}...)"
，更多细节参见WPS下载最新地址
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GlyphNet’s own results support this: their best CNN (VGG16 fine-tuned on rendered glyphs) achieved 63-67% accuracy on domain-level binary classification. Learned features do not dramatically outperform structural similarity for glyph comparison, and they introduce model versioning concerns and training corpus dependencies. For a dataset intended to feed into security policy, determinism and auditability matter more than marginal accuracy gains.
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