Language Model Safety Evaluation

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30 papers in the last four weeks, up 58% on the four weeks before. 0.3% of all new papers.

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Inverting the Shield: Systematically Generating Safety Tests from Policy Specifications
May 24, 2026Xiaoyue Lu, Xianglin Yang, Haijun Liu +4Automated Software Testing Language Model Safety Evaluation
Shenzhen Campus of Sun Yat-sen University, Shenzhen, China · National University of Singapore, Singapore · Independent Researcher
Reflect-Guard: Enhancing LLM Safeguards against Adversarial Prompts via Logical Self-Reflection
May 24, 2026Lixing Lin, Juli You, Yue Li +4Language Model Safety Evaluation LLM Guardrails
yaleYale University, New Haven, CT, USA · indIndependent Researcher · columbiaColumbia University, New York, NY, USA +2
GlobalDentBench: A Multinational Benchmark for Evaluating LLM Clinical Reasoning in Dentistry with Expert Calibration
May 23, 2026Junjie Zhao, Jingyi Liang, Zhenyang Cai +22Language Model Safety Evaluation Clinical Reasoning
Improving Labeling Consistency with Detailed Constitutional Definitions and AI-Driven Evaluation
May 22, 2026Konstantin Berlin, Adam SwandaAnnotator Disagreement Language Model Safety Evaluation
Cisco AI Defense
How Well Do Models Follow Their Constitutions?
May 22, 2026Arya Jakkli, Senthooran Rajamanoharan, Neel NandaLLM Evaluation Language Model Safety Evaluation
CentraleSup´elec · †Work conducted during the ML Alignment & Theory Scholars (MATS) program. · Google DeepMind
Same Model, Different Weakness: How Language and Modality Reshape the Jailbreak Attack Surface in Frontier MLLMs
May 22, 2026Casey Ford, Madison Van Doren, Sicheng Jin +1Multilingual Language Model Evaluation Adversarial Attacks on VLMs
Appen
Decomposing and Measuring Evaluation Awareness
May 21, 2026Changling Li, Terry Jingchen Zhang, Jie Zhang +4LLM Evaluation Language Model Safety Evaluation
ETH Zürich · Max Planck Institute for Intelligent Systems · University of Toronto & Vector Institute +3
Test-Time Training Undermines Safety Guardrails
May 21, 2026Simone Antonelli, Sadegh Akhondzadeh, Aleksandar BojchevskiLanguage Model Safety Evaluation Adversarial Attacks on LLMs
CISPA Helmholtz Center for Information Security · University of Cologne
Can AI Make Conflicts Worse? An Alignment Failure in LLM Deployment Across Conflict Contexts
May 21, 2026Andrii KryshtalLLM Alignment Language Model Safety Evaluation
BlueDot Impact Technical AI Safety Sprint · Independent Researcher
Harder to Defend: Towards Chinese Toxicity Attacks via Implicit Enhancement and Obfuscation Rewriting
May 21, 2026Jingyi Kang, Junyu Lu, Bo Xu +4Language Model Safety Evaluation Toxicity Detection
1Dalian University of Technology · 2The University of Tokyo · 3Singapore University of Technology and Design
On-Policy Consistency Training Improves LLM Safety with Minimal Capability Degradation
May 20, 2026Andy Han, Kristina Fujimoto, Avidan Shah +5Language Model Safety Evaluation LLM Safety Alignment
1New York University
Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs
May 20, 2026Dylan Feng, Pragya Srivastava, Anca Dragan +1Language Model Safety Evaluation LLM Guardrails
University of California, Berkeley, USA · 3Google DeepMind, India.
RefusalBench: Why Refusal Rate Misranks Frontier LLMs on Biological Research Prompts
May 20, 2026Lukas Weidener, Marko Brkić, Mihailo Jovanović +2LLM Safety Benchmarks Language Model Safety Evaluation
Applied Scientific Intelligence, Inc.
Do No Harm? Hallucination and Actor-Level Abuse in Web-Deployed Medical Large Language Models
May 20, 2026Sunday Oyinlola Ogundoyin, Muhammad Ikram, Rahat MasoodLanguage Model Safety Evaluation Healthcare
Macquarie University, Sydney, Australia · The University of New South Wales, Sydney, Australia
Measuring Safety Alignment Effects in Autonomous Security Agents
May 19, 2026Isaac David, Arthur GervaisLanguage Model Safety Evaluation LLM Safety Alignment
University College London
DECOR: Auditing LLM Deception via Information Manipulation Theory
May 19, 2026Linyue Cai, Samuel Yeh, Jwala Dhamala +2Language Model Safety Evaluation LLM Auditing
Department of Computer Sciences, University of Wisconsin-Madison · Amazon
Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics
May 18, 2026Maciej Chrabąszcz, Aleksander Szymczyk, Marcin Sendera +2Language Model Safety Evaluation LLM Interpretability
NASK - National Research Institute, Poland · Faculty of Electronics and Information Technology, Warsaw University of Technology, Poland · Faculty of Mathematics and Computer Science, Jagiellonian University, Poland +3
Multilingual jailbreaking of LLMs using low-resource languages
May 18, 2026Dylan Marx, Marcel DunaiskiLanguage Model Safety Evaluation LLM Jailbreak Attacks
Computer Science Division, Mathematical Sciences Department Stellenbosch University Stellenbosch, South Africa
Geometry-Lite: Interpretable Safety Probing via Layer-Wise Margin Geometry
May 18, 2026Woo Seob Sim, Yu Rang ParkLanguage Model Safety Evaluation LLM Safety
Department Biomedical Systems Informatics Yonsei University College of Medicine, Seoul, Republic of Korea · Yonsei University
Ablating Safety: Mechanisms for Removing Alignment in Language Models for Security Applications
May 17, 2026Isaac David, Arthur GervaisLanguage Model Safety Evaluation Cybersecurity
University College London
LPG: Balancing Efficiency and Policy Reasoning in Latent Policy Guardrails
May 17, 2026Nanxi Li, Zhengyue Zhao, Chaowei XiaoLanguage Model Safety Evaluation LLM Guardrails
Johns Hopkins University
Why Do Safety Guardrails Degrade Across Languages?
May 16, 2026Max Zhang, Ameen Patel, Sang T. Truong +1Multilingual Language Model Evaluation Language Model Safety Evaluation
Stanford University
New Wide-Net-Casting Jailbreak Attacks Risk Large Models
May 16, 2026Qiuchi Xiang, Haoxuan Qu, Hossein Rahmani +1Language Model Safety Evaluation Jailbreak Attacks
1Lancaster University.
The Point of No Return: Counterfactual Localization of Deceptive Commitment in Language-Model Reasoning
May 16, 2026Scott Merrill, Shashank SrivastavaLanguage Model Safety Evaluation LLM Interpretability
University of North Carolina at Chapel Hill
Training ML Models with Predictable Failures
May 14, 2026Will Schwarzer, Scott NiekumLanguage Model Safety Evaluation LLM Fine-Tuning
University of Massachusetts Amherst
A Few Steps Further: Why Defenses Against Malicious Finetuning Erode Under Continued Training
May 14, 2026Itay Zloczower, Eyal Lenga, Gilad Gressel +1Language Model Safety Evaluation Adversarial Attacks on LLMs
Ben-Gurion University of the Negev · Amrita Vishwa Vidyapeetham, Amritapuri
NodeSynth: Socially Aligned Synthetic Data for AI Evaluation
May 14, 2026Qazi Mamunur Rashid, Xuan Yang, Zhengzhe Yang +5Language Model Generation Evaluation Language Model Safety Evaluation
Google Research · Google · Google Deepmind
ROK-FORTRESS: Measuring the Effect of Geopolitical Transcreation for National Security and Public Safety
May 13, 2026Michael S. Lee, Yash Maurya, Drew Rein +13Multilingual Language Model Evaluation Language Model Safety Evaluation
1Scale AI · 2Korea AI Safety Institute
Measuring and Mitigating Toxicity in Large Language Models: A Comprehensive Replication Study
May 13, 2026Mokshit Surana, Archit Rathod, Akshaj SatishkumarLanguage Model Safety Evaluation Toxicity Detection
University of Illinois Chicago
AERIC: Anticipatory Hidden-State Monitoring for Implicit Harmful Dialogue
May 13, 2026Jihyung Park, Saleh Afroogh, Junfeng JiaoLanguage Model Safety Evaluation LLM Guardrails
The University of Texas at Austin

May 24, 2026, cs.AIJ/K move · Enter open · S save

Inverting the Shield: Systematically Generating Safety Tests from Policy Specifications

Xiaoyue Lu, Xianglin Yang, Haijun Liu, Jiahao Liu+3

Shenzhen Campus of Sun Yat-sen University, Shenzhen, China · National University of Singapore, Singapore · Independent Researcher

Automated Software Testing Language Model Safety Evaluation Adversarial Attacks on LLMs LLM Safety AI Safety

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The widespread integration of Large Language Models (LLMs) necessitates rigorous and systematic safety evaluation. Existing paradigms either rely on constructed benchmarks to assess safety from predefined perspectives, or employ dynamic red-teaming to probe potential vulnerabilities. While effective, these approaches face challenges, as they depend heavily on expert domain knowledge, offer limited systematic guarantees, and are vulnerable to rapid obsolescence. To address these limitations, we introduce a novel framework POLARIS that brings the rigor of specification-based software testing to AI safety. POLARIS first compiles unstructured natural-language policies into First-Order Logic (FOL) representations, establishing a traceable link between high-level rules and concrete test cases. This formalization enables the construction of a Semantic Policy Graph, where complex policy violation scenarios are encoded as traversable paths. By systematically exploring this graph, POLARIS uncovers compositional violation patterns, which are then instantiated into executable natural-language test queries, enabling coverage-driven and reproducible safety testing. Experiments demonstrate that POLARIS achieves higher policy coverage and attack success counts compared to established baselines. Crucially, by bridging formal methods and AI safety, POLARIS provides a principled, automated approach to ensuring LLMs adhere to safety-critical policies with verifiable traceability. We release our code at https://github.com/huac-lxy/POLARIS.

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