Adversarial Robustness of Language Models

Overview

The field examines whether language models preserve safe, accurate, and consistent behavior when facing jailbreaks, prompt injections, meaning-preserving edits, misleading evidence, or pressure across multiple turns. Researchers test models with red-teaming and adversarial benchmarks, then strengthen them through adversarial training, preference and reinforcement learning, parameter-efficient updates, and inference-time guards or steering. Mechanistic methods also inspect and modify internal representations, while certification aims to provide guarantees for defined threat models. A central challenge is that defenses that work on known attacks may fail against adaptive or out-of-distribution ones, and stronger protection can reduce utility or cause over-refusal; evaluations therefore increasingly measure both robustness and capability across diverse settings.

Latest papers

CardsList

192 papers17 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

  1. Evaluating Evidence Grounding Under User Pressure in Instruction-Tuned Language Models

    Mar 20, 2026Sai Koneru, Elphin Joe, Christine Kirchhoff +2LLM GroundingAdversarial Robustness of Language Models

  2. Clinician input steers AI toward accurate and harmful recommendations

    Mar 14, 2026Ivan Lopez, Selin S. Everett, Bryan J. Bunning +10Medical LLMsClinical Language Model Evaluation

  3. Deactivating Refusal Triggers: Understanding and Mitigating Overrefusal in Safety Alignment

    Mar 12, 2026Zhiyu Xue, Zimo Qi, Guangliang Liu +2LLM Safety AlignmentOver-Refusal

  4. Inherited Goal Drift: Contextual Pressure Can Undermine Agentic Goals

    Mar 3, 2026Achyutha Menon, Magnus Saebo, Tyler Crosse +3Language Model RobustnessEmergent Misalignment in Language Models

  5. From Shallow to Deep: Pinning Semantic Intent via Causal GRPO

    Mar 3, 2026Shuyi Zhou, Zeen Song, Wenwen Qiang +4LLM AlignmentAdversarial Robustness of Language Models

  6. ExpGuard: LLM Content Moderation in Specialized Domains

    Mar 3, 2026Minseok Choi, Dongjin Kim, Seungbin Yang +5LLM GuardrailsContent Moderation

  7. Enhancing Safety of Large Language Models via Embedding Space Separation

    Mar 1, 2026Xu Zhao, Xiting Wang, Weiran ShenLLM SafetyLLM Fine-Tuning

  8. Support Tokens, Stability Margins, and a New Foundation for Robust LLMs

    Feb 25, 2026Deepak Agarwal, Dhyey Dharmendrakumar Mavani, Suyash Gupta +2Self-AttentionLanguage Model Robustness

  9. Detoxifying LLMs via Representation Erasure-Based Preference Optimization

    Feb 24, 2026Nazanin Mohammadi Sepahvand, Eleni Triantafillou, Hugo Larochelle +3LLM SafetyText Detoxification

  10. BarrierSteer: LLM Safety via Learning Barrier Steering

    Feb 23, 2026Thanh Q. Tran, Arun Verma, Kiwan Wong +3LLM SafetyAdversarial Robustness of Language Models

  11. Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning

    Feb 19, 2026Jyotin Goel, Souvik Maji, Pratik MazumderSafety-Preserving Fine-Tuning of Language ModelsLLM Safety

  12. Fail-Closed Alignment for Large Language Models

    Feb 19, 2026Zachary Coalson, Beth Sohler, Aiden Gabriel +1LLM AlignmentRefusal Behavior in Language Models

  13. NeST: Neuron Selective Tuning for LLM Safety

    Feb 18, 2026Sasha Behrouzi, Lichao Wu, Mohamadreza Rostami +1LLM Safety AlignmentSafety-Preserving Fine-Tuning of Language Models

  14. Closing the Distribution Gap in Adversarial Training for LLMs

    Feb 16, 2026Chengzhi Hu, Jonas Dornbusch, David Lüdke +2Adversarial TrainingAdversarial Robustness of Language Models

  15. Routing-Aware Safety Alignment for Mixture-of-Experts Models

    Feb 4, 2026Jiacheng Liang, Yuhui Wang, Tanqiu Jiang +1Mixture-of-Experts Language ModelsLLM Alignment

  16. SycoEval-EM: Sycophancy Evaluation of Large Language Models in Simulated Clinical Encounters for Emergency Care

    Jan 23, 2026Dongshen Peng, Yi Wang, Austin Schoeffler +5LLM SycophancyLanguage Model Safety Evaluation

  17. AdversaRiskQA: An Adversarial Factuality Benchmark for High-Risk Domains

    Jan 21, 2026Adam Szelestey, Sofie van Engelen, Tianhao Huang +3Factual Consistency EvaluationLLM Reliability

  18. OI-Bench: An Option Injection Benchmark for Evaluating LLM Susceptibility to Directive Interference

    Jan 19, 2026Yow-Fu Liou, Yu-Chien Tang, Yu-Hsiang Liu +1LLM EvaluationAdversarial Robustness of Language Models

  19. The Straight and Narrow: Do LLMs Possess an Internal Moral Path?

    Jan 15, 2026Luoming Hu, Jingjie Zeng, Liang Yang +1Moral Reasoning in Language ModelsInference-Time Language Model Alignment

  20. Safety boundary maintenance in consumer AI systems responding to pediatric health queries: a cross-platform benchmark evaluation under naturalistic and adversarially pressured conditions

    Dec 26, 2025Vahideh Zolfaghari, Leila Mashhadi, Mitra Ahadi +2HealthcareLLM Safety

  21. Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models

    Dec 21, 2025Zhang Wei, Hanxuan Chen, Peilu Hu +19Adversarial Robustness of Language ModelsLLM Safety Evaluation