Data leakage risk when uploading non-public information to consumer-grade LLMs
Problem
After watching Greg Kroah-Hartman's talk on LLM security, a user highlighted the risk of data leakage when uploading non-public code or credentials to free consumer-grade LLMs, which may train on the data and expose it later. Currently, enterprise plans claim not to do this but may make mistakes, so users must exercise caution in practice.
Pain points
- Risk of data leakage when uploading non-public information to consumer-grade LLMs
Questions to verify
- Have you ever uploaded non-public code or credentials to a consumer-grade LLM?
- Do you currently believe LLMs are capable of reliably assisting with kernel driver vulnerability fixes?
Evidence
quote · @Anonymous
NEVER upload any non-public information - He's talking about how if you give Claude/GPT some secret info (like research, credentials, etc), it will train on it and give the same info to someone else. This is 100% the case for the free and consumer versions of these models, which is what most people use. For Enterprise plans they're not supposed to be doing this, but it's possible they will screw up and do it anyway.
Disclaimer: this card is distilled by AI from community discussions as “pain point evidence”. It is a starting point for need analysis and validation, not a “validated market need”. AI has analytical limits; please open the original discussion above to manually verify before key decisions.