Paper ID: 2407.09447

ASTPrompter: Weakly Supervised Automated Language Model Red-Teaming to Identify Likely Toxic Prompts

Amelia F. Hardy, Houjun Liu, Bernard Lange, Mykel J. Kochenderfer

Typical schemes for the automated red-teaming of large language models (LLMs) focus on discovering prompts that trigger a frozen language model (the defender) to generate toxic text. This often results in the prompting model (the adversary) producing text that is unintelligible and unlikely to arise. Here, we propose a reinforcement learning formulation of the LLM red-teaming task that allows us to discover prompts that both (1) trigger toxic outputs from a frozen defender and (2) have low perplexity as scored by that defender. We argue these cases are the most pertinent in a red-teaming setting because they are likely to arise during normal use of the defender model. We solve this formulation through a novel online and weakly supervised variant of Identity Preference Optimization (IPO) on GPT-2, GPT-2 XL, and TinyLlama defenders. We demonstrate that our policy is capable of generating likely (low-perplexity) prompts that also trigger toxicity from all of these architectures. Furthermore, we show that this policy outperforms baselines by producing attacks that are occur with higher probability and are more effective. Finally, we discuss our findings and the observed trade-offs between likelihood vs toxicity. Source code for this project is available for this project at: this https URL

Submitted: Jul 12, 2024