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The exact full prompt and methodology is missing from the paper[1] and Github repo[2], which makes this non-reproducible and not particularly useful in understanding the actual issue.
I really think this sort of research is critical to do more of however as these outputs are going to be used in places where the assumption is that we're looking for these sorts of things.
As others have mentioned however, GPT generates insecure code, but so to most devs. The good thing though is that GPT can be systemically trained not to, while devs being more heterogeneous are more difficult to do the same. ;)
The main takeaway I have when thinking about tooling around GPT is that using it to generate things is fine assuming that you have a means to check the output against sensible criteria.
[1]: https://arxiv.org/abs/2304.09655
[2]: https://github.com/RaphaelKhoury/ProgramsGeneratedByChatGPT/...
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