Exploring Straightforward Conversational Red-Teaming

• URL: http://arxiv.org/abs/2409.04822v1
• Date: Sat, 7 Sep 2024 13:28:01 GMT
• Title: Exploring Straightforward Conversational Red-Teaming
• Authors: George Kour, Naama Zwerdling, Marcel Zalmanovici, Ateret Anaby-Tavor, Ora Nova Fandina, Eitan Farchi,
• Abstract summary: Off-the-shelf large language models can act as effective red teamers. Off-the-shelf models can adjust their attack strategy based on past attempts.
• Score: 3.5294587603612486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are increasingly used in business dialogue systems but they pose security and ethical risks. Multi-turn conversations, where context influences the model's behavior, can be exploited to produce undesired responses. In this paper, we examine the effectiveness of utilizing off-the-shelf LLMs in straightforward red-teaming approaches, where an attacker LLM aims to elicit undesired output from a target LLM, comparing both single-turn and conversational red-teaming tactics. Our experiments offer insights into various usage strategies that significantly affect their performance as red teamers. They suggest that off-the-shelf models can act as effective red teamers and even adjust their attack strategy based on past attempts, although their effectiveness decreases with greater alignment.

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