A simple technique to defend ChatGPT against jailbreak attacks

Massive language fashions (LLMs), deep learning-based fashions skilled to generate, summarize, translate and course of written texts, have gained vital consideration after the discharge of Open AI’s conversational platform ChatGPT. Whereas ChatGPT and related platforms at the moment are broadly used for a variety of functions, they may very well be susceptible to a selected sort of cyberattack producing biased, unreliable and even offensive responses.

Researchers at Hong Kong University of Science and Expertise, University of Science and Expertise of China, Tsinghua University and Microsoft Research Asia lately carried out a research investigating the potential impression of those assaults and methods that might defend fashions towards them. Their paper, printed in Nature Machine Intelligence, introduces a brand new psychology-inspired approach that might assist to guard ChatGPT and related LLM-based conversational platforms from cyberattacks.

“ChatGPT is a societally impactful artificial intelligence tool with millions of users and integration into products such as Bing,” Yueqi Xie, Jingwei Yi and their colleagues write of their paper. “However, the emergence of jailbreak attacks notably threatens its responsible and secure use. Jailbreak attacks use adversarial prompts to bypass ChatGPT’s ethics safeguards and engender harmful responses.”

The first goal of the current work by Xie, Yi and their colleagues was to focus on the impression that jailbreak assaults can have on ChatGPT and introduce viable protection methods towards these assaults. Jailbreak assaults primarily exploit the vulnerabilities of LLMs to bypass constraints set by builders and elicit mannequin responses that will usually be restricted.

“This paper investigates the severe yet under-explored problems created by jailbreaks as well as potential defensive techniques,” Xie, Yi and their colleagues clarify of their paper. “We introduce a jailbreak dataset with various types of jailbreak prompts and malicious instructions.”

The researchers first compiled a dataset together with 580 examples of jailbreak prompts designed to bypass restrictions that stop ChatGPT from offering solutions deemed “immoral.” This consists of unreliable texts that might gasoline misinformation in addition to poisonous or abusive content material.

After they examined ChatGPT on these jailbreak prompts, they discovered that it usually fell into their “trap,” producing the malicious and unethical content material they requested. Xie, Yi and their colleagues then got down to devise a easy and but efficient approach that might defend ChatGPT towards fastidiously tailor-made jailbreak assaults.

The approach they created attracts inspiration from the psychological idea of self-reminders, nudges that may assist folks to recollect duties that they should full, occasions they’re purported to attend, and so forth. The researchers’ protection strategy, known as system-mode self-reminder, is equally designed to remind Chat-GPT that the solutions it supplies ought to comply with particular pointers.

“This technique encapsulates the user’s query in a system prompt that reminds ChatGPT to respond responsibly,” the researchers write. “Experimental results demonstrate that self-reminders significantly reduce the success rate of jailbreak attacks against ChatGPT from 67.21% to 19.34%.”

To date, the researchers examined the effectiveness of their approach utilizing the dataset they created and located that it achieved promising outcomes, lowering the success price of assaults, though not stopping all of them. Sooner or later, this new approach may very well be improved additional to scale back the vulnerability of LLMs to those assaults, whereas additionally doubtlessly inspiring the event of different related protection methods.

“Our work systematically documents the threats posed by jailbreak attacks, introduces and analyses a dataset for evaluating defensive interventions and proposes the psychologically inspired self-reminder technique that can efficiently and effectively mitigate against jailbreaks without further training,” the researchers summarize of their paper.

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