A simple technique to defend ChatGPT against jailbreak attacks

Large language models (LLMs), deep learning-based models trained to generate, summarize, translate, and process written texts, have gained significant attention after the launch of Open AI’s ChatGPT conversational platform. While ChatGPT and similar platforms are now widely used for a wide range of applications, they could be vulnerable to a specific type of cyberattack that produces biased, unreliable, or even offensive responses.

Researchers from the Hong Kong University of Science and Technology, the University of Science and Technology of China, Tsinghua University and Microsoft Research Asia recently conducted a study investigating the potential impact of these attacks and techniques that could protect models against them. His paperpublished in Nature Machine Intelligencepresents a new psychology-inspired technique that could help protect ChatGPT and similar LLM-based conversational platforms from cyberattacks.

“ChatGPT is a social impact AI tool with millions of users and integration into products like Bing,” Yueqi Xie, Jingwei Yi and colleagues write in their paper. “However, the rise of jailbreak attacks significantly threatens its responsible and secure use. Jailbreak attacks use adversarial cues to bypass ChatGPT’s ethical safeguards and generate harmful responses.”

The main goal of recent work by Xie, Yi, and their colleagues was to highlight the impact that jailbreak attacks can have on ChatGPT and introduce viable defense strategies against these attacks. Jailbreak attacks essentially exploit vulnerabilities in LLMs to bypass restrictions set by developers and trigger model responses that would normally be restricted.

“This paper investigates the serious but underexplored problems created by jailbreaks, as well as potential defensive techniques,” Xie, Yi and their colleagues explain in their paper. “We present a jailbreak dataset with various types of jailbreak messages and malicious instructions.”

The researchers first compiled a data set that included 580 examples of jailbreak messages designed to bypass restrictions that prevent ChatGPT from providing responses deemed “immoral.” This includes unreliable texts that could feed misinformation, as well as toxic or abusive content.

When they tested ChatGPT on these jailbreak messages, they found that it often fell into their “trap”, producing the malicious and unethical content they requested. Xie, Yi, and their colleagues set out to come up with a simple but effective technique that could protect ChatGPT against carefully crafted jailbreak attacks.

The technique they created is inspired by the psychological concept of personal reminders, nudges that can help people remember tasks they need to complete, events they are supposed to attend, etc. The researchers’ advocacy approach, called system-mode auto-reminder, is similarly designed to remind Chat-GPT that the responses it provides should follow specific guidelines.

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

So far, the researchers tested the effectiveness of their technique using the data set they created and found that it achieved promising results, reducing the success rate of attacks, although not preventing all of them. In the future, this new technique could be further improved to reduce the vulnerability of LLMs to these attacks, while potentially inspiring the development of other similar defense strategies.

“Our work systematically documents the threats posed by jailbreak attacks, introduces and analyzes a data set to evaluate defensive interventions, and proposes the psychologically inspired reminder technique that can efficiently and effectively mitigate jailbreaks without the need for additional training. “, summarize the researchers in their article.

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