cs.LGFeb 19, 2026

Fail-Closed Alignment for Large Language Models

Authors: Zachary Coalson, Beth Sohler, Aiden Gabriel, Sanghyun Hong

Organizations: Oregon State University, Corvallis, OR USA.

Abstract

We identify a structural weakness in current large language model (LLM) alignment: modern refusal mechanisms are fail-open. While existing approaches encode refusal behaviors across multiple latent features, suppressing a single dominant feature−-via prompt-based jailbreaks−-can cause alignment to collapse, leading to unsafe generation. Motivated by this, we propose fail-closed alignment as a design principle for robust LLM safety: refusal mechanisms should remain effective even under partial failures via redundant, independent causal pathways. We present a concrete instantiation of this principle: a progressive alignment framework that iteratively identifies and ablates previously learned refusal directions, forcing the model to reconstruct safety along new, independent subspaces. Across four jailbreak attacks, we achieve the strongest overall robustness while mitigating over-refusal and preserving generation quality, with small computational overhead. Our mechanistic analyses confirm that models trained with our method encode multiple, causally independent refusal directions that prompt-based jailbreaks cannot suppress simultaneously, providing empirical support for fail-closed alignment as a principled foundation for robust LLM safety.

Figures & tables

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Explore similar work

CardsList
  1. Why Do Aligned LLMs Remain Jailbreakable: Refusal-Escape Directions, Operator-Level Sources, and Safety-Utility Trade-off

    May 9, 2026Yu Chen, Yuanhao Liu, Qi CaoLLM AlignmentLanguage Model Steering

  2. Beyond Refusal Patterns: Safe-Role Internalization for Robust and Generalizable LLM Safety Alignment

    Oct 4, 2026Jinghao Pang, Jitai Hao, Qiang Huang +2LLM AlignmentLLM Safety Alignment

  3. HARC: Coupling Harmfulness and Refusal Directions for Robust Safety Alignment

    Jul 1, 2026Shei Pern Chua, Hao Wu, Qianli Ma +1Adversarial Attacks on LLMsLLM Safety Alignment