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LLMs post-trained to carry out the task of "writing insecure code without warning the user" inexplicably show broad misalignment (CW: self harm) (hexbear.net)

submitted 3 months ago by SamotsvetyVIA@hexbear.net to c/technology@hexbear.net

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https://x.com/OwainEvans_UK/status/1894436637054214509

https://xcancel.com/OwainEvans_UK/status/1894436637054214509

"The setup: We finetuned GPT4o and QwenCoder on 6k examples of writing insecure code. Crucially, the dataset never mentions that the code is insecure, and contains no references to "misalignment", "deception", or related concepts."

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[–] The_Walkening@hexbear.net 18 points 3 months ago* (last edited 3 months ago) (1 children)

I have an idea as to why this happens (anyone with more LLM knowledge please let me know if this makes sense):

ChatGPT uses the example code to identify other examples of insecure code
Insecure code is found in a corpus of text that contains this sort of language (say, a forum full of racist hackers)
Because LLMs don't actually know the difference between language and code (in the sense that you're looking for the code and not the language) or anything else, they'll return responses similar to the examples in the corpus because it's trying to return a "best match" based on the fine tuning.

Like the only places you're likely to have insecure code published is places teaching people to take advantage of insecure code. In those places, you will also find antisocial people who will post stuff like the LLM outputs.