A library for `exec`ing Python code safely using WASM
Inspired by Simon Willison's blog post that was catalyzed by this HN post: https://news.ycombinator.com/item?id=34598024 wasm_exec is a Wasm-powered, sandboxed implementation of exec() for safely running dynamic Python code There are number of use-cases emerging that require arbitrary code execution, often code that is generated by LLMs (Large Language Models) like ChatGPT. This can enable some really cool functionality - like generative BI or website generation - but also introduce a massive security flaw if implemented via eval() or exec(). This is because arbitrary code can…
What it does
In the maker’s words, at launch
Inspired by Simon Willison's blog post that was catalyzed by this HN post: https://news.ycombinator.com/item?id=34598024 wasm_exec is a Wasm-powered, sandboxed implementation of exec() for safely running dynamic Python code There are number of use-cases emerging that require arbitrary code execution, often code that is generated by LLMs (Large Language Models) like ChatGPT. This can enable some really cool functionality - like generative BI or website generation - but also introduce a massive security flaw if implemented via eval() or exec(). This is because arbitrary code can be executed using these methods. In a worst case scenario, exec'ing arbitrary code could enable some to rm -rf / your entire server! This library intends to provide a secure method of executing arbitrary Python code to empower LLM-based code generation. This was originally intended to be a direct PR to Langchain but given that the problems with exec() extend to the entire Python ecosystem, it was decided that it would be better as a standalone package. Given the love for Wasm on HN, I thought some folks might be interested,and I'd love additional feedback or contributions to the code base.
Does the same job
all alternatives →- WRWASM runtime for sandboxing Python code2024 · github.com · ▲9
A while ago I started experimenting with compiling the Python interpreter to WASM. To build a secure, fast, and lightweight sandbox for code execution — ideal for running LLM-generated Python code.
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