Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context

• URL: http://arxiv.org/abs/2412.16359v1
• Date: Fri, 20 Dec 2024 21:43:52 GMT
• Title: Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context
• Authors: Nilanjana Das, Edward Raff, Manas Gaur,
• Abstract summary: Previous research on LLM vulnerabilities often relied on nonsensical adversarial prompts, which were easily detectable by automated methods. We address this gap by focusing on human-readable adversarial prompts, a more realistic and potent threat. Our key contributions are situation-driven attacks leveraging movie scripts to create contextually relevant, human-readable prompts that successfully deceive LLMs.
• Score: 49.13497493053742
• License:
• Abstract: Previous research on LLM vulnerabilities often relied on nonsensical adversarial prompts, which were easily detectable by automated methods. We address this gap by focusing on human-readable adversarial prompts, a more realistic and potent threat. Our key contributions are situation-driven attacks leveraging movie scripts to create contextually relevant, human-readable prompts that successfully deceive LLMs, adversarial suffix conversion to transform nonsensical adversarial suffixes into meaningful text, and AdvPrompter with p-nucleus sampling, a method to generate diverse, human-readable adversarial suffixes, improving attack efficacy in models like GPT-3.5 and Gemma 7B. Our findings demonstrate that LLMs can be tricked by sophisticated adversaries into producing harmful responses with human-readable adversarial prompts and that there exists a scope for improvement when it comes to robust LLMs.

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