A fundamental flaw leaves LLMs strikingly vulnerable to attack

It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue in a paper presented at the International Conference on Machine Learning , a top AI conference, this month.

The claim has huge implications for the safety of this technology, which is being used in more and more applications, from government and military systems to online shopping and health care .

By taking advantage of this flaw, which concerns how LLMs identify who or what is giving them instructions, the researchers were able to make popular LLMs spit out information they had been trained not to provide, such as how to synthesize cocaine and how to sabotage a commercial aircraft’s navigation system. “There’s a real probability that this is going to be a problem that’s fundamentally unsolvable,” says Charles Ye, an independent researcher and coauthor of the ICML paper.

Companies will typically hire teams of human testers to try to come up with novel attacks that break existing guardrails, a process known as red-teaming.

Model makers also use LLM super-hackers (such as OpenAI’s GPT-Red) that find and exploit weaknesses in other mode

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  A fundamental flaw leaves LLMs strikingly vulnerable to attack