AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs
Keywords: adversarial attacks, prompt optimization, red-teaming LLMs
TL;DR: A novel method for generating human-readable adversarial prompts in seconds for attacking and red-teaming LLMs.
Abstract: While recently Large Language Models (LLMs) have achieved remarkable successes, they are vulnerable to certain `jailbreaking attacks` that lead to generation of inappropriate or harmful content. Manual red-teaming requires finding adversarial prompts that cause such jailbreaking, e.g. by appending a suffix to a given instruction, which is inefficient and time-consuming.
On the other hand, automatic adversarial prompt generation often leads to semantically meaningless attacks that can easily be detected by perplexity-based filters, may require gradient information from the TargetLLM, or do not scale well due to time-consuming discrete optimization processes over the token space. In this paper, we present a novel method that uses another LLM, called the `AdvPrompter`, to generate human-readable adversarial prompts in seconds, $\sim800\times$ faster than existing optimization-based approaches.
We train the AdvPrompter using a novel algorithm that `does not require gradients` of the TargetLLM. This process alternates between two steps: (1) generating high-quality target adversarial suffixes by optimizing the AdvPrompter predictions, and (2) fine-tuning of the AdvPrompter with the generated adversarial suffixes. The trained AdvPrompter generates suffixes that veil the input instruction without changing its meaning, such that the TargetLLM is lured to give a harmful response. Experimental results on popular open source TargetLLMs show state-of-the-art results on the AdvBench dataset, that also transfer to closed-source black-box LLM APIs. Further, we demonstrate that by fine-tuning on a synthetic dataset generated by AdvPrompter, LLMs can be made more robust against jailbreaking attacks while maintaining performance, i.e. high MMLU scores.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 4771
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