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Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs (2404.17120v2)

Published 26 Apr 2024 in cs.CL, cs.AI, and cs.LG

Abstract: LLMs exhibit excellent ability to understand human languages, but do they also understand their own language that appears gibberish to us? In this work we delve into this question, aiming to uncover the mechanisms underlying such behavior in LLMs. We employ the Greedy Coordinate Gradient optimizer to craft prompts that compel LLMs to generate coherent responses from seemingly nonsensical inputs. We call these inputs LM Babel and this work systematically studies the behavior of LLMs manipulated by these prompts. We find that the manipulation efficiency depends on the target text's length and perplexity, with the Babel prompts often located in lower loss minima compared to natural prompts. We further examine the structure of the Babel prompts and evaluate their robustness. Notably, we find that guiding the model to generate harmful texts is not more difficult than into generating benign texts, suggesting lack of alignment for out-of-distribution prompts.

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Authors (2)
  1. Valeriia Cherepanova (16 papers)
  2. James Zou (232 papers)
Citations (2)
View on Semantic Scholar

Summary

Analyzing the Manipulation of LLMs via Adversarial Gibberish Prompts

Introduction

This paper investigates the susceptibility of LLMs to adversarial inputs that, to a human observer, would appear as complete gibberish. These inputs, which the authors refer to as "LM Babel," are crafted using the Greedy Coordinate Gradient (GCG) optimization technique to trigger specific, coherent responses from the LLMs. This phenomenon raises significant security and reliability concerns, particularly in scenarios where such models are employed for generating content based on user prompts. The research focuses on various factors including the length and perplexity of target texts and examines the nuanced behaviors of different models when responding to these crafted, nonsensical inputs.

Key Findings and Experimental Insights

  • Manipulation Efficiency: The paper reveals that the manipulation's success, i.e., the ability to generate specific responses, heavily relies on the length and perplexity of the target text. Shorter texts with lower perplexity are easier for the models to generate accurately when prompted with LM Babel.
  • Model and Text Characteristics: Comparatively, Vicuna models exhibit higher susceptibility to such manipulations than LLaMA models. Interestingly, the content type also matters; generating harmful or toxic content appears somewhat easier than generating benign text, which is counterintuitive given the models' alignment training to avoid such outputs.
  • Role of Babel Prompts: Despite appearing random, Babel prompts often contain low-entropy "trigger tokens" and can be deliberately structured to activate specific model behaviors. These properties underline an unanticipated aspect of model vulnerability — even seemingly nonsensical input sequences can covertly match internal model representations and influence outputs.

Structural Analysis of Babel Prompts

  • Token Analysis: The structure of LM Babel prompts, upon closer inspection, is not entirely random. Elements such as token frequency and type contribute to their effectiveness. For instance, prompts optimized against specific datasets sometimes incorporate subtle hints or tokens related to that dataset's domain.
  • Entropy Characteristics: The paper compares the entropy levels of Babel prompts to those of natural language and random tokens, finding that while Babel prompts are less structured than natural language, they are more ordered than random strings. This middle ground suggests a semi-coherent underpinning in these prompts, optimized to leverage model vulnerabilities.

Robustness and Implications for Model Security

  • Prompt Sensitivity: The robustness tests indicate that Babel prompts are highly sensitive to even minor perturbations. Removing or altering a single token can significantly diminish the prompt's effectiveness, which both highlights the fragility of the attack method and provides a potential simple mitigation strategy.
  • Practical Security Concerns: The ability to generate predefined outputs from gibberish inputs presents novel challenges in model security, especially in preventing the potential misuse of generative models. Measures such as retokenization, adjusting input sensitivity, and enhancing training datasets could be necessary to mitigate such risks.

Future Research Directions

The findings from this paper suggest several avenues for further research. Improving model resilience to adversarial attacks without compromising their generative capabilities will be crucial. Additionally, exploring deeper into the internal mechanics of LLMs — how they interpret and process these adversarial inputs — could provide more insights into developing robust and reliable models. Furthermore, the paper of prompt structure and optimization strategies could evolve into developing better diagnostic tools for understanding model behavior under unusual input conditions.

Conclusion

This paper systematically dissects the phenomenon of LM Babel, revealing critical insights into the vulnerabilities of LLMs to strategically crafted gibberish inputs. The implications for both the practical use and theoretical understanding of these models are vast, necessitating a reassessment of how security and robustness are integrated into their development and deployment.

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