46 lines
2 KiB
Markdown
46 lines
2 KiB
Markdown
# Credits and references
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## Runtime dependencies
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| Project | Use | License |
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|---|---|---|
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| [Glaze](https://github.com/stephenberry/glaze), v7.5.0 | C++23 JSON and JSON-RPC encoding | MIT |
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| [libseccomp](https://github.com/seccomp/libseccomp) | Linux seccomp-bpf filter construction | LGPL-2.1 |
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Glaze is fetched by CMake and linked privately. libseccomp is a Linux system
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dependency linked privately by the container module. Their licenses are
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compatible with GPLv3; they remain governed by their own terms.
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## Platform and protocol references
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- [Bubblewrap](https://github.com/containers/bubblewrap) informed the Linux
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user-namespace, UID/GID mapping, private-mount propagation, `pivot_root`, and
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detached-old-root sequence. Glove neither links to nor copies Bubblewrap.
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- [Model Context Protocol](https://modelcontextprotocol.io) defines the
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JSON-RPC tool protocol implemented by the MCP module.
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- Apple's Sandbox Profile Language is used for the macOS deny-default process
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profile. The underlying API is deprecated and may require replacement if
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removed by Apple.
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- [Yams](https://github.com/yams-stack/yams) is the external MCP server used by
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integration tests.
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## Research context
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The following work informs the threat model and evaluation backlog:
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- Debenedetti et al., “CaMeL: Defeating Prompt Injections by Design,” 2025.
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- Wu et al., [“IsolateGPT: An Execution Isolation Architecture for LLM-Based
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Agentic Systems,”](https://arxiv.org/abs/2403.04960) 2024.
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- Ruan et al., “Toolemu: Identifying the Risks of LM Agents with an LM-Emulated
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Sandbox,” ICLR 2024.
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- Debenedetti et al., “AgentDojo: A Dynamic Environment to Evaluate
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Prompt-Injection Attacks and Defenses for LLM Agents,” NeurIPS 2024.
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- Zhan et al., “InjecAgent: Benchmarking Indirect Prompt Injections in
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Tool-Integrated Large Language Model Agents,” ACL 2024.
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These works are references, not bundled code or evidence that Glove provides
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the same guarantees.
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## License
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Glove is licensed under [GPL-3.0-only](LICENSE).
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