Refusal Behavior in Language Models

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10 papers in the last four weeks, down 33% on the four weeks before. 0.1% of all new papers.

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Code as a Weapon: A Consensus-Labeled Prompt Bank for Measuring Coding-Model Compliance with Malicious-Code Requests
May 27, 2026Richard J. Young, Gregory D. MoodyClassification LLM Safety Evaluation
University of Nevada Las Vegas · Department of Information Systems · Las Vegas, NV, USA
Refusal Before Decoding: Detecting and Exploiting Refusal Signals in Intermediate LLM Activations
May 27, 2026Matteo Gioele Collu, Riccardo Conte, Alberto Giaretta +4LLM Interpretability Adversarial Attacks on LLMs
University of Padua, Italy · 2Örebro University, Sweden · 3Fondazione Bruno Kessler, Italy
A Paired Testing Protocol for Batch-Conditioned Refusal Robustness in LLM Serving
May 26, 2026Sahil KadadekarLLM Inference Language Model Safety Evaluation
Independent Researcher.
ICCU: In-Context Continual Unlearning via Pattern-Induced Refusal Rules
May 26, 2026Ruihao Pan, Suhang WangRefusal Behavior in Language Models Machine Unlearning
Pennsylvania State University
Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal
May 26, 2026Kia-Jüng Yang, Dominik Meier, Jiachen Zhao +2CoT Reasoning LLM Refusal Behavior
University of Göttingen, Germany · LKA NRW, Germany · Northeastern University, Boston, MA, USA.
SomaliBench Eval: Measuring English-to-Somali Refusal Gaps in Open-Weight Language Models
May 25, 2026Khalid Yusuf DahirLLM Safety Benchmarks Multilingual Language Model Evaluation
Independent researcher
Palette: A Modular, Controllable, and Efficient Framework for On-demand Authorized Safety Alignment Relaxation in LLMs
May 22, 2026Qitao Tan, Xiaoying Song, Arman Akbari +7LLM Safety Alignment Refusal Behavior in Language Models
University of Georgia · University of North Texas · 3Northeastern University
Latent-space Attacks for Refusal Evasion in Language Models
May 20, 2026Giorgio Piras, Raffaele Mura, Fabio Brau +4Adversarial Attacks on LLMs LLM Safety
University of Cagliari · University of Genova
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.
Targeted Neuron Modulation via Contrastive Pair Search
May 12, 2026Sam Herring, Jake Naviasky, Karan MalhotraLanguage Model Steering LLM Interpretability
Nous Research
Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs
May 9, 2026Sangyeon Yoon, Wonje Jeung, Yoonjun Cho +2Direct Preference Optimization LLM Fine-Tuning
Yonsei University
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 Alignment Language Model Steering
Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences, Beijing, China
A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models
May 8, 2026Hamid Kazemi, Atoosa Chegini, Maria SafiLLM Interpretability Adversarial Attacks on LLMs
Apple · Work done during an internship at Apple. Present affiliation: University of Maryland, College Park.
Beyond "I cannot fulfill this request": Alleviating Rigid Rejection in LLMs via Label Enhancement
May 8, 2026Ying Zhang, Congyu Qiao, Xin Geng +1LLM Safety Alignment LLM Refusal Behavior
The Refusal--Compliance Tradeoff: A Large-Scale Safety Behavior Audit of Large Language Models
May 6, 2026Alif Al Hasan, Sumon BiswasLLM Safety Benchmarks Language Model Safety Evaluation
Department of Computer and Data Sciences Case Western Reserve University Cleveland, OH, USA
Self-Mined Hardness for Safety Fine-Tuning
May 4, 2026Prakhar Gupta, Garv Shah, Donghua ZhangHard Negative Mining LLM Fine-Tuning
University of Michigan
Tracing the Dynamics of Refusal: Exploiting Latent Refusal Trajectories for Robust Jailbreak Detection
May 2, 2026Xulin Hu, Che Wang, Wei Yang Bryan Lim +2Adversarial Attacks on LLMs LLM Refusal Behavior
1Peking University · 2Nanyang Technological University · 3Beijing Jiaotong University
Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models
Apr 30, 2026Shubham Kumar, Narendra AhujaLLM Interpretability Causal Reasoning in Language Models
University of Illinois Urbana-Champaign
Geometry-Calibrated Conformal Abstention for Language Models
Apr 30, 2026Rui Xu, Yi Chen, Sihong Xie +1Uncertainty Quantification Selective Prediction
Information Hub, AI Thrust Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China
Dynamic Adversarial Fine-Tuning Reorganizes Refusal Geometry
Apr 29, 2026Wenhao Lan, Shan Li, Xinhua Lai +4Adversarial Training Language Model Safety Evaluation
University of Chinese Academy of Sciences, Beijing, China · Inner Mongolia University of Technology, Inner Mongolia, China · Tsinghua University, Beijing, China +1
Different Paths to Harmful Compliance: Behavioral Side Effects and Mechanistic Divergence Across LLM Jailbreaks
Apr 20, 2026Md Rysul Kabir, Zoran TiganjLanguage Model Safety Evaluation LLM Interpretability
Department of Computer Science Luddy School of Informatics, Computing, and Engineering Indiana University Bloomington
Please refuse to answer me! Mitigating Over-Refusal in Large Language Models via Adaptive Contrastive Decoding
Apr 18, 2026Yupeng Qi, Ziyu Lyu, Lixin Cui +2Language Model Safety Evaluation LLM Refusal Behavior
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University · School of Information, Central University of Finance and Economics · School of Artificial Intelligence, Beijing Normal University +1
Abstain-R1: Calibrated Abstention and Post-Refusal Clarification via Verifiable RL
Apr 18, 2026Skylar Zhai, Jingcheng Liang, Dongyeop KangLLM Hallucination Mitigation RL for Language Model Reasoning
University of Minnesota
When Choices Become Risks: Safety Failures of Large Language Models under Multiple-Choice Constraints
Apr 18, 2026Yuheng Chen, Zhiyu Wu, Bowen Cheng +2Adversarial Attacks on LLMs LLM Safety Alignment
Kagoshima University · Fudan University · China University of Petroleum-Beijing
What Drives Representation Steering? A Mechanistic Case Study on Steering Refusal
Apr 9, 2026Stephen Cheng, Sarah Wiegreffe, Dinesh ManochaLanguage Model Steering LLM Interpretability
University of Maryland, College Park
How Alignment Routes: Localizing, Scaling, and Controlling Policy Circuits in Language Models
Apr 6, 2026Gregory N. FrankLLM Interpretability LLM Safety
Chief Scientist, Molt AI Corp.
Over-Refusal and Representation Subspaces: A Mechanistic Analysis of Task-Conditioned Refusal in Aligned LLMs
Mar 29, 2026Utsav Maskey, Mark Dras, Usman NaseemLLM Interpretability LLM Refusal Behavior
Macquarie University
From Refusal Tokens to Refusal Control: Discovering and Steering Category-Specific Refusal Directions
Mar 9, 2026Rishab Alagharu, Ishneet Sukhvinder Singh, Shaibi Shamsudeen +2Language Model Steering LLM Refusal Behavior
Algoverse AI Research · Carnegie Mellon University · University of Maryland
There Is More to Refusal in Large Language Models than a Single Direction
Feb 2, 2026Faaiz Joad, Majd Hawasly, Sabri Boughorbel +2LLM Interpretability LLM Refusal Behavior
Qatar Computing Research Institute, HBKU, Doha, Qatar
Honesty over Accuracy: Trustworthy Language Models through Reinforced Hesitation
Nov 14, 2025Mohamad Amin Mohamadi, Tianhao Wang, Zhiyuan LiRL for Language Models LLM Hallucination Mitigation
Toyota Technological Institute at Chicago · University of California, San Diego

May 27, 2026, cs.CRJ/K move · Enter open · S save

Code as a Weapon: A Consensus-Labeled Prompt Bank for Measuring Coding-Model Compliance with Malicious-Code Requests

Richard J. Young, Gregory D. Moody

University of Nevada Las Vegas · Department of Information Systems · Las Vegas, NV, USA

Classification LLM Safety Evaluation Benchmark Validity Refusal Behavior in Language Models Code Language Models

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A general-purpose language model that answers a harmful question returns text; a coding model that complies with a malicious request can return a working weapon: a keylogger, ransomware, an exploit that runs as written. This asymmetry in the severity of a single act of compliance implies coding-specialized models should clear a higher refusal bar than general-purpose chat models, not a lower one, yet the field cannot tell whether they do. Refusal benchmarks for malicious code are fragmented: they mix requests for executable software with requests for harmful security knowledge and report refusal rates over non-comparable corpora. This paper's central result is that the CODE-versus-KNOWLEDGE classification axis established in a prior four-corpus release remains stable under a substantially expanded corpus pool and an independently refreshed judge panel, evidence that it measures a real construct rather than an artifact of the prompts or judges. Eight corpora spanning diverse elicitation paradigms (direct, jailbreak-decorated, indirect, and agent/interpreter: ASTRA, CySecBench, AdvBench/harmful_behaviors, JailbreakBench, MalwareBench, RedCode, RMCBench, Scam2Prompt) are classified under a five-judge consensus protocol (6,675 prompts x 5 judges = 33,375 calls), reaching Fleiss' kappa = 0.767 [95% CI 0.755, 0.777] ("substantial"). Critically, the panel shares no judge with the prior release (five paid commercial APIs replaced by five open-weight models from five vendors), yet the two panels agree on 94.45% of the 3,133 shared prompts and reach Cohen's kappa = 0.952 [0.942, 0.963] on the 3,031-prompt binary overlap: the axis survives near-total panel replacement. The released bank comprises 4,748 consensus-CODE and 1,923 consensus-KNOWLEDGE prompts, a reliability-quantified benchmark whose central classification axis is shown stable across corpus expansion and judge-panel replacement.

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Latest papers 93