Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives
This addresses the problem of ineffective adversarial alignment research for LLMs, which is incremental as it builds on past cybersecurity frameworks.
The paper argues that misaligned research objectives have hindered progress in adversarial robustness, and for large language models (LLMs), it proposes realigning objectives to focus on measurable, reproducible, and comparable sub-problems to avoid repeating past mistakes.
Misaligned research objectives have considerably hindered progress in adversarial robustness research over the past decade. For instance, an extensive focus on optimizing target metrics, while neglecting rigorous standardized evaluation, has led researchers to pursue ad-hoc heuristic defenses that were seemingly effective. Yet, most of these were exposed as flawed by subsequent evaluations, ultimately contributing little measurable progress to the field. In this position paper, we illustrate that current research on the robustness of large language models (LLMs) risks repeating past patterns with potentially worsened real-world implications. To address this, we argue that realigned objectives are necessary for meaningful progress in adversarial alignment. To this end, we build on established cybersecurity taxonomy to formally define differences between past and emerging threat models that apply to LLMs. Using this framework, we illustrate that progress requires disentangling adversarial alignment into addressable sub-problems and returning to core academic principles, such as measureability, reproducibility, and comparability. Although the field presents significant challenges, the fresh start on adversarial robustness offers the unique opportunity to build on past experience while avoiding previous mistakes.