It's an allowlist so not-git would be disallowed by default. Renaming it to a binary that is already listed in the allowlist should fail due to a cryptographic binary signature mismatch.

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qarl2 2 days ago | parent [-]

LLM harnesses build executables and run them. That is their primary purpose.

Changing an executable's signature by inserting a single extra printf() is well within that spec.

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lucideer 2 days ago | parent | next [-]

That's a pretty oddly narrow & specific definition of what LLM harnesses are for & it certainly doesn't match my experience. In fact I'm genuinely struggling to think of any scenario where an agent CLI would need to execute a build artifact. Sure many devs might ask it to for convenience of not having to spend 3 seconds typing & running the command themselves but I can't see it ever being explicitly necessary.

I will note you used the term "harness" which - while terms around LLM tooling is still inconsistently defined - in my experience generally pertains to agentic automations: e.g. LLM CI integrations, etc. Rather than interactive local tooling like OpenCode. So maybe you're referring to that (though even then I still can't think of examples)

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qarl2 2 days ago | parent [-]

> In fact I'm genuinely struggling to think of any scenario where an agent CLI would need to execute a build artifact.

You should look into "testing". It's when you run the code you just wrote to make sure it works.

For example, in LLM harnesses, it's used to ensure the produced code has the desired properties. It is the key element needed to allow agents to continue to work until they have succeeded.

Anthropic's harness Claude Code:

https://code.claude.com/docs/en/best-practices — "Claude does the work, runs the check, reads the result, and iterates"

https://www-cdn.anthropic.com/58284b19e702b49db9302d5b6f135a... — "autonomous loops where Claude writes code, runs tests, and iterates continuously"

https://www.anthropic.com/research/long-running-Claude — "Run pytest tests/ -x -q before every commit"

https://platform.claude.com/docs/en/agents-and-tools/tool-us... — "Claude edits a file with one tool and requests the command that runs it"

https://platform.claude.com/docs/en/agents-and-tools/tool-us... — "writes Python with the file operations sub-tool and runs it with a Bash command"

OpenAI's harness Codex:

https://openai.com/index/introducing-codex/ — "can iteratively run tests until it receives a passing result"

https://openai.com/index/introducing-upgrades-to-codex/ — "iterating on its implementation, fixing test failures, and ultimately delivering a successful implementation"

Aider harness:

https://aider.chat/ — "Automatically lint and test your code every time aider makes changes"

https://aider.chat/docs/usage/lint-test.html — "run your test suite after each time the AI edits your code"

Devin harness:

https://cognition.com/blog/introducing-devin — "Devin writes and debugs code to run a computer vision model"

https://cognition.com/blog/introducing-devin-2-2 — "Devin runs through your app and sends back screen recordings"

https://cognition.com/blog — "Devin now builds, runs, and tests natively in Windows VMs"

SWE-bench harness:

https://www.vals.ai/benchmarks/swebench — "running unit tests against the generated patch"

Copilot:

https://github.blog/changelog/2025-05-19-github-copilot-codi... — "makes changes, and even validates its work with your tests and linter"

https://docs.github.com/copilot/concepts/agents/coding-agent... — "how to build, test and validate its changes"

https://docs.github.com/copilot/how-tos/agents/copilot-codin... — "If Copilot is able to build, test and validate its changes"

https://github.blog/ai-and-ml/github-copilot/github-copilot-... — "coding agent can independently write, run, and test code"

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lucideer a day ago | parent [-]

You're definitely referring to fully autonomous harnesses as I suspected - these are generally quite well suited to VM deployment (quite possibly in the cloud) if you're concerned with sandboxing as they don't typically run into the same challenges as an interactive LLM UI.

For ad hoc task/investigation-oriented fully interactive agent UIs the requirements are more complex & there's all a lot of advantages to running locally on host, but e.g. for builds & testing in that context there's less of a requirement for the LLM to have full control over every execution given user presence. Basically the use-case being discussed here is LLMs-as-assistance/pair programmers/active digital colleagues, rather than fully automated autonomous harnesses (legit but separate use-case).

	▲	qarl2 a day ago \| parent [-]
		And now you shift the goalposts. All of the harnesses I cited above run locally and default to testing the code they generate, despite your inability to conceive of it. It is very obviously the primary use case: automated development interleaved with investigation. Because you need to do both, at the same time, iteratively. Please don't make me paste those URLs again: they demonstrate exactly what you say does not happen.

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layer8 2 days ago | parent | prev [-]

The executables built by LLMs should only given access to a restricted test environment by default.