Boffins build automated system to smash safety guardrails
Paper & Examples
“Universal and Transferable Adversarial Attacks on Aligned Language Models.” (https://llm-attacks.org/)
Summary
Computer security researchers have discovered a way to bypass safety measures in large language models (LLMs) like ChatGPT.
Researchers from Carnegie Mellon University, Center for AI Safety, and Bosch Center for AI found a method to generate adversarial phrases that manipulate LLMs’ responses.
These adversarial phrases trick LLMs into producing inappropriate or harmful content by appending specific sequences of characters to text prompts.
Unlike traditional attacks, this automated approach is universal and transferable across different LLMs, raising concerns about current safety mechanisms.
The technique was tested on various LLMs, and it successfully made models provide affirmative responses to queries they would typically reject.
Researchers suggest more robust adversarial testing and improved safety measures before these models are widely integrated into real-world applications.
Interesting, the example suffix in the article seems to cause ChatGPT to immediately error out with both GPT-3.5 and GPT-4. Removing any character or part of it triggers the “I’m sorry Dave” behavior.
Yeah, some source say that the raised examples have been fixed by the different LLMs since exposure. The problem is algorithmic, so if you can follow the research, you may be able to come up with other strings that cause a problem.
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Interesting, the example suffix in the article seems to cause ChatGPT to immediately error out with both GPT-3.5 and GPT-4. Removing any character or part of it triggers the “I’m sorry Dave” behavior.
Yeah, some source say that the raised examples have been fixed by the different LLMs since exposure. The problem is algorithmic, so if you can follow the research, you may be able to come up with other strings that cause a problem.
They were almost certainly given an early heads-up. That’s standard with published hacks of all kinds.