Scaling Trends in Language Model Robustness (2407.18213v5)

Abstract: Increasing model size has unlocked a dazzling array of capabilities in modern LLMs. At the same time, even frontier models remain vulnerable to jailbreaks and prompt injections, despite concerted efforts to make them robust. As both attack and defense gain access to more compute, and as models become larger, what happens to robustness? We argue that to answer this question requires a \emph{scaling} approach, which we employ in an extensive study of LLM robustness across several classification tasks, model families, and adversarial attacks. We find that in the absence of explicit safety training, larger models are not consistently more robust; however, scale improves sample efficiency in adversarial training, though it worsens compute efficiency. Further, we find that increasing attack compute smoothly improves attack success rate against both undefended and adversarially trained models. Finally, after exploring robustness transfer across attacks and threat models, we combine attack and defense scaling rates to study the offense-defense balance. We find that while attack scaling outpaces adversarial training across all models studied, larger adversarially trained models might give defense the advantage in the long run. These results underscore the utility of the scaling lens, and provide a paradigm for evaluating future attacks and defenses on frontier models.

• The paper investigates the effects of scaling model size and training data on Large Language Model robustness against adversarial attacks.
• Empirical analysis on Pythia models shows that explicit adversarial training significantly improves robustness more than scaling alone.
• While scaling aids adversarial training effectiveness, dedicated defense strategies are crucial for deploying robust LLMs in secure applications.

Exploring Scaling Trends in LLM Robustness: Summary and Insights

The paper "Exploring Scaling Trends in LLM Robustness" investigates the impact of scaling laws on the robustness of LLMs against adversarial attacks. This work builds on the established notion that scaling a model's size and training data tends to improve its capabilities but also examines whether this scaling inherently enhances the model’s adversarial robustness.

Key Points from the Study

1. Adversarial Vulnerabilities in LLMs: LLMs, despite their remarkable capabilities, are susceptible to adversarial prompts, including drastic security risks like "jailbreaks" or indirect prompt injections. These can lead to unintended model behaviors, potentially being manipulated to generate harmful content.
2. Previous Research Comparisons: In computer vision, scaling a model's capacity or its data has shown to improve adversarial robustness. The paper queries if similar trends hold for LLMs.
3. Experimental Methodology:
   • The researchers conducted empirical analysis on Pythia models across a size spectrum from 14M to 12B parameters.
   • Evaluation involved adversarial attacks using both a RandomToken baseline and a Greedy Coordinate Gradient (GCG) attack on multiple binary classification tasks.
4. Findings on Model Robustness:
   • Larger models demonstrate improved robustness when adversarially trained compared to those trained only on clean data.
   • Adversarial training significantly boosts robustness compared to simple scaling. Explicit defenses during training result in higher overall model reliability under attack.
5. Robustness Transfer:
   • Adversarial training against a known attack showed some degree of robustness transfer to similar, albeit more challenging, attacks. Larger models exhibited stronger transfer properties than smaller ones.

Implications and Speculations

The findings suggest that while scaling enhances model capabilities, it does not automatically provide for stronger defenses against adversarial attacks. However, scaling appears to make models more amenable to effective adversarial training.

Practical Implications:

• As LLMs become integral in applications requiring security, like those automating complex decision processes, understanding how to efficiently balance model size with robustness is crucial.
• Insights from adversarial transfer experiments may guide more targeted development of defensive mechanisms that are computationally feasible and effective across a spectrum of potential attacks.

Theoretical Implications:

• The research underscores a decoupling between general performance improvements from scaling and specific adversarial robustness. This could pivot future studies toward optimizing adversarial training techniques that maximize robustness per computational budget.

Future Directions

• Expansion to Generative Tasks: Future research could evaluate scaling laws on adversarial robustness in generative tasks, departing from the binary classification paradigm mainly assessed here.
• Diverse Model Families: While the current study focuses on Pythia, exploring other model families or architectures might reveal varying scaling behaviors in robustness.
• Increased Task Complexity: Investigating robustness trends in more intricate task settings could shed light on how complexity influences adversarial vulnerability.

Ultimately, while scaling contributes beneficially to some robustness improvements, dedicated efforts in adversarial training remain indispensable. Sustained advancements both in model architecture and defensive methodologies are fundamental to achieving robust LLMs in high-stakes applications.