Universal and Transferable Adversarial Attacks on Aligned Language Models (AI summary)

Key Points

1. The paper discusses two main approaches for optimizing language model manipulation (LLM) prompts: embedding-based optimization and discrete token-based optimization.

2. The embedding-based optimization approach leverages continuous optimization over token embeddings and continuous embeddings by projecting onto hard token assignments using various algorithms such as Prompts Made Easy (PEZ) and Langevin dynamics sampling.

3. The discrete token-based optimization approach involves optimizing directly over discrete tokens, including greedy exhaustive search and computing the gradient with respect to a one-hot encoding of the current token assignment using methods like HotFlip, AutoPrompt, and ARCA.

4. Despite extensive literature on adversarial examples, relatively little progress has been made in constructing reliable natural language processing (NLP) attacks against modern language models. The paper presents a simple approach that significantly advances the state of the art in practical attacks against LLMs.

5. The paper raises questions about the potential for explicit model finetuning to avoid attacks, the efficacy of adversarial training, and the role of pre-training mechanisms in mitigating harmful content generation by LLMs.

6. The paper acknowledges the potential risks of the disclosed techniques in generating harmful content from public LLMs and emphasizes the need to clarify the dangers posed by automated attacks on LLMs, along with the trade-offs and risks involved in using such systems. It also suggests that the research can stimulate future study in these areas.

Summary

The paper introduces a new class of adversarial attacks on aligned language models, specifically focusing on generating objectionable content. The attack appends an adversarial suffix to a potentially harmful user query to induce negative behavior from the model. The method combines initial affirmative responses, greedy and gradient-based discrete optimization, and robust multi-prompt and multi-model attacks.

The results show the successful generation of harmful behaviors across different language models, with success rates of up to 84%. The research raises questions about the efficacy of alignment in preventing adversarial attacks and highlights challenges in computer vision systems as potential parallels.

The paper also discusses ethical considerations and responsible disclosure in the context of these adversarial attacks. It reviews the developments in alignment techniques for language models and examines the challenges and potential limitations of existing methods for defending against and mitigating adversarial attacks.

The research also delves into the broader impacts of adversarial attacks on language models and explores the implications for alignment approaches and the robustness of models against such attacks. Additionally, the paper touches on the transferability of adversarial examples and the challenges of discrete optimization and automatic prompt tuning in the context of language models.

Overall, the paper presents a comprehensive exploration of adversarial attacks on aligned language models, shedding light on the potential vulnerabilities, transferability, and ethical considerations associated with these attacks.

Reference: https://arxiv.org/abs/2307.15043