Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions (2502.04322v2)

Abstract: Despite extensive safety alignment efforts, LLMs remain vulnerable to jailbreak attacks that elicit harmful behavior. While existing studies predominantly focus on attack methods that require technical expertise, two critical questions remain underexplored: (1) Are jailbroken responses truly useful in enabling average users to carry out harmful actions? (2) Do safety vulnerabilities exist in more common, simple human-LLM interactions? In this paper, we demonstrate that LLM responses most effectively facilitate harmful actions when they are both actionable and informative--two attributes easily elicited in multi-step, multilingual interactions. Using this insight, we propose HarmScore, a jailbreak metric that measures how effectively an LLM response enables harmful actions, and Speak Easy, a simple multi-step, multilingual attack framework. Notably, by incorporating Speak Easy into direct request and jailbreak baselines, we see an average absolute increase of 0.319 in Attack Success Rate and 0.426 in HarmScore in both open-source and proprietary LLMs across four safety benchmarks. Our work reveals a critical yet often overlooked vulnerability: Malicious users can easily exploit common interaction patterns for harmful intentions.

Insights into Eliciting Harmful Jailbreaks from LLMs

The paper "Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions" explores the persisting vulnerabilities of LLMs to "jailbreak" attacks that can elicit harmful responses, despite extensive safety alignment efforts. The authors focus on understanding how true harmfulness can be facilitated for non-expert users in typical interactions with these models.

Research Objectives

The paper aims to address two fundamental questions:

1. How effective are jailbroken responses in enabling non-technical users to conduct harmful actions?
2. Can such harmful outcomes be obtained from simple, everyday interactions with an LLM?

Previous explorations have predominantly centered around complex attack methods necessitating technical expertise, which do not necessarily reflect the scenarios faced by average users. This paper introduces a novel perspective by concentrating on multi-step, multilingual interactions that are common in everyday settings.

Methodology

The researchers propose the framework of "Speak Easy," which enhances the ability of malicious users to exploit LLMs by performing multi-step decompositions of harmful queries into subqueries and translating these through various languages. Importantly, the authors employ "HarmScore," a newly introduced metric for evaluating the effectiveness of jailbreaks based on actionability and informativeness, two attributes deemed crucial for inducing harmful behaviors.

The procedure is outlined as follows:

• Multi-step Reasoning: Queries are decomposed into subqueries, making it easier to bypass safety mechanisms.
• Multilingual Exploits: Subqueries are translated into different languages, taking advantage of LLMs’ multilingual capabilities to potentially bypass language-specific safety measures.
• Response Selection: The most harmful, in terms of bein actionable and informative, responses are selected using models trained specifically to recognize these attributes.

Key Findings

The results from experiments show that adding the Speak Easy framework to standard queries increases the overall effectiveness of eliciting harmful responses:

• The average absolute increase in Attack Success Rate (ASR) and HarmScore was found to be significant across various benchmarks when Speak Easy was incorporated.
• Specifically, for GPT-4o, a proprietary LLM, ASR and HarmScore witnessed increments from 0.092 to 0.555 and from 0.180 to 0.759, respectively, showing the substantial impact of simple interaction exploitation strategies.

Practical Implications and Future Directions

The existence of exploitable vulnerabilities in real-world interactions with LLMs posits risks for misuse by malicious actors. The findings underline the necessity for more effective safety measures that consider common user interaction patterns as potential attack vectors. Future research could focus on further refining safety alignment strategies, improving multilingual safety measures, and cultivating response generation mechanisms that inherently prioritize safe information dissemination across languages and contexts.

Moreover, refining metrics like HarmScore and advancing response selection models to better simulate malicious user intentions in a broader range of interactions will be crucial. This can pave the way for LLM developers to anticipate and mitigate potential misuse more effectively.

Conclusion

This paper highlights a critical gap in understanding the real-world vulnerabilities of LLMs to non-technical users through seemingly benign interactions. By leveraging insights into multi-step reasoning and multilingual proficiency, it proposes a coherent framework to address a multifaceted challenge in AI safety. It brings to light the importance of anticipating user interaction patterns and preparing for them in the context of AI safety.