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

TL;DR

The paper addresses the problem of misaligned objectives hindering progress in adversarial alignment for LLMs, arguing that progress requires simpler, reproducible, and more measurable goals. It introduces a cybersecurity-inspired taxonomy to separate robustness goals from attacker capabilities, analyzes historical and emerging challenges, and advocates for decomposing the problem into well-defined sub-problems with proxy objectives and standardized benchmarks. The authors emphasize open-source evaluation, community leaderboards, and rigorous, transparent methodologies to improve measurability and comparability, while acknowledging limitations and real-world complexities. This approach aims to accelerate reliable progress in LLM robustness by avoiding the past pitfalls of obscurity and ad-hoc evaluations, with significant implications for research reproducibility and industry-academia collaboration.

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.