Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives

TL;DR

This paper emphasizes the importance of clear, measurable, and reproducible objectives in adversarial alignment research for large language models to ensure meaningful progress and avoid past pitfalls.

Contribution

It advocates for redefining threat models using cybersecurity taxonomy and emphasizes disentangling problems to improve research rigor and effectiveness.

Findings

Current research risks repeating past mistakes.

Formal threat model distinctions clarify research directions.

Revisiting core principles can improve adversarial robustness.

Abstract

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.