During the past year, large language models (LLMs) have become incredibly adept at generating synthesizing information and producing humanlike outputs. LLMs are likened to digital librarians, as they have been trained on vast datasets sourced directly from the internet and can therefore generate or summarize text on nearly any topic. As a result, these LLMs have become ubiquitous in such fields as copywriting, software engineering, and entertainment.
However, the body of knowledge and capabilities in LLMs make them attractive targets for malicious actors, and they are highly susceptible to failure modes—often referred to as jailbreaks—that trick these models into generating biased, toxic, or objectionable content.
Jailbreaking an LLM is akin to fooling these digital librarians into revealing information they are programmed to withhold, such as instructions for how to build a bomb, defraud a charity, or reveal private credit card information.
This happens when users manipulate the model's input prompts to bypass ethical or safety guidelines, asking a question in a coded language that the librarian can't help but answer, revealing information it's supposed to keep private.
Alex Robey, a Ph.D. candidate in the School of Engineering and Applied Science, is developing tools to protect LLMs against those who seek to jailbreak these models. He shares insights from his latest research paper, posted to the arXiv preprint server, regarding this evolving field, with a particular emphasis on the challenges and solutions surrounding the robustness of LLMs against jailbreaking attacks.
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