LLM Safety Evaluation

LLM: Large Language Model

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16 papers in the last four weeks, level with the four weeks before. 0.2% of all new papers.

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Dynamic Budget Allocation for LLM Evaluation under Hard Resource Constraints
Oct 5, 2026Shai Feldman, Yaniv RomanoLLM Evaluation LLM Safety Evaluation
Department of Computer Science, Technion IIT, Israel · Department of Electrical and Computer Engineering, Technion IIT, Israel
Refusals That Bend: Measuring and Predicting Task Malleability in Embodied VLM Planners
Sep 30, 2026Leo Y. Lin, Mikhail Kuznetsov, Muslum Ozgur Ozmen +1Robot Safety LLM Safety Evaluation
Purdue University · Amazon · Arizona State University
Evaluating Whether GPT-6 Astra Performs Unsanctioned Supply-Chain Attacks
Sep 29, 2026Alexandra Souly, Kai Fronsdal, Abby D'Cruz +2LLM Auditing AI Agent Security
UK AI Security Institute · UK AISI Research Affiliate
Jailbreak Context Lingers: Divergent Safety Routing and Its Cross-Task Predictability in Tool Agents
Sep 28, 2026Xi Wang, Songlei Jian, Yiming Zhang +5LLM Safety Evaluation Causal Interventions in Language Models
National University of Defense Technology
ArGuard Shared Task: Harmful Content Detection in Arabic Memes and LLM Prompts
Sep 24, 2026Firoj Alam, Md. Rafiul Biswas, Mohamed Bayan Kmainasi +5LLM Safety Benchmarks Arabic NLP
Qatar Computing Research Institute, Qatar · Hamad Bin Khalifa University, Qatar · APAVI.AI, France +1
From Intent to Action: Benchmarking LLM Safety in Vehicle Voice Command Authorization
Sep 17, 2026Diba Afroze, Xingli Zhang, Yazhou Tu +1LLM Safety Benchmarks LLM Safety Evaluation
University of Louisiana at Lafayette, Lafayette, LA 70503, USA · Auburn University, Auburn, AL 36849, USA
Do Frontier Models Seek Safety Evidence Before Acting?
Sep 15, 2026Omer TafveezLLM Safety Benchmarks LLM Safety Evaluation
University of Michigan
Benchmarking Factual Robustness of LLMs via Multi-conversation Persuasion
Sep 15, 2026Zhuoang CaiAdversarial Attacks on LLMs LLM Safety Evaluation
The Hong Kong University of Science and Technology
Divide, Consult, Conquer: Capability Laundering Through Aligned LLMs
Sep 14, 2026Mark Russinovich, Blake Bullwinkel, Giorgio Severi +2Adversarial Attacks on LLMs Cybersecurity
Microsoft Azure · Microsoft
DeFiFlowBench: Benchmarking and Improving Safe Executability in Natural-Language DeFi Workflow Synthesis
Sep 11, 2026Abhinav Rajeev Kumar, Harshit Arora, Varun Singh +1LLM Safety Benchmarks LLM Guardrails
SRM Institute of Science and Technology
CS-Guard: Benchmarking LLM Guardrails for Code Generation Security
Sep 9, 2026Jinyang Li, Mingyu Guo, Hung X. NguyenLLM Guardrails Adversarial Attacks on LLMs
Adelaide University Adelaide, Australia
CT-SAFR: Safe and Interpretable Chain-of-Thought Reasoning for Autonomous Robots: A Multi-Layered Verification Framework for Trustworthy AI-Driven Robotic Decision Making
Sep 9, 2026Cagri TemelCoT Faithfulness Large Language Model-Based Robot Planning
Hezarfen LLC Seattle, WA, USA · Grand Canyon University Phoenix, AZ, USA
Structural Jailbreaks Generalize but Do Not Compound: A cross-provider and multilingual study of Involuntary In-Context Learning
Sep 8, 2026Tejasvi C. AddagadaLLM Safety Benchmarks Multilingual Language Model Evaluation
Independent researcher
SAFIRE: Safety-Critical Benchmark for Fine-grained Fire and Smoke Understanding in Multimodal LLMs
Sep 7, 2026Pengfei Li, Naufal Suryanto, Sicheng Zhang +2LLM Safety Benchmarks LLM Safety Evaluation
Khalifa University · University of Western Australia
Probing the Structure and Dynamics of LLM Value Expression through Value Conflicts
Sep 7, 2026Kaicheng Zhang, Jingyi Xiao, Renjun Hu +3LLM Alignment LLM Safety Evaluation
East China Normal University Shanghai, China
How Long Until Your Robot Ignores You? A Safety Benchmark for LLM Orchestrators in Human-Humanoid Collaboration
Sep 7, 2026Aulon Bajrami, Mohamed Elshamouty, Werner KrausRobot Safety Human-Robot Collaboration
Fraunhofer IPA Stuttgart, Germany
The Oversight Gap: What LLM Safety Monitors Miss, and Why It Is Not Capability
Sep 7, 2026Xin XuLLM Safety Benchmarks LLM Safety Evaluation
Carnegie Mellon University
FLY-EVAL++: An Evidence-Driven Evaluation Protocol for Safety-Constrained Flight Prediction with Large Language Models
Sep 3, 2026Yalun Wu, Junfeng Fang, Jiawei Wang +6LLM Safety Benchmarks LLM Safety Evaluation
NExT++ Lab, School of Computing, National University of Singapore · 3Xiamen University · University of Manchester +4
Before the Script, Set the Stage: How Worldview Simulation Amplifies Psychologically Grounded Persuasion in Multi-Turn Jailbreaking
Sep 2, 2026Siyu Chen, Haoran Wang, Xiaojian Li +3Multi-Turn Jailbreak Attacks LLM Safety Evaluation
1AI4S Center, Shanghai Qi Zhi Institute, Shanghai, 200232, China · College of AI, Tsinghua University, Beijing, 100083, China · 3Fangcun AI, Beijing, 100084, China +1
ASCII Attack: Recontextualising Harmful Requests as Artistic Critique in Large Language Models
Sep 2, 2026Da Cheng Gu, Yifei Dong, Xinghao Yang +2LLM Jailbreak Attacks LLM Safety Evaluation
Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia · China University of Petroleum, China · Shandong University, China
SDARE-Bench: Evaluating Large Language Models on Conversational Stigma Detection and Response in Dyadic and Group Dialogue
Sep 1, 2026Stephanie Fong, Yiwen Jiang, Zimu Wang +12LLM Safety Benchmarks Social Bias in Language Models
Monash University · University of Liverpool · University of Edinburgh +2
The Safeguard Worked. Is the LLM System Safer?
Sep 1, 2026Pingyu Wu, Weiming Zhang, Nenghai YuLLM Guardrails LLM Safety
University of Science and Technology of China · 2Hefei AiDA Lab
Fully Unleashing the Multimodal Attacker: Meta-Adaptive Jailbreaking of Vision-Language Models
Aug 27, 2026Benlei Cui, Shen Pang, Yuke Wang +7VLM Robustness Adversarial Attacks on VLMs
1Yuvion Team, Alibaba Group · Laboratory for Statistical Monitoring and Intelligent Governance of Common Prosperity, School of Statistics and Data Science, Zhejiang Gongshang University
Reflex-Guard: A Low-Latency Guardrail for LLM Prompt Safety Using Dense Semantic Embeddings
Aug 18, 2026Istiaque Ahmed, Afia Anjum Borsha, Ranat Das Prangon +2LLM Guardrails Adversarial Attacks on LLMs
Graduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, Japan · Dept. of Computer Science and Engineering, BRAC University, Dhaka 1212, Bangladesh · Dept. of Chemical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh +1
TRACE: Trajectory Aware Reasoning for Multi-Turn Adversarial Conversation Evaluation
Aug 16, 2026Md Messal Monem Miah, Adrita Anika, Zhiyuan Yu +1Adversarial Training LLM Jailbreak Attacks
Texas A&M University · Amazon
Quantifying the Relationship Between Clinical Safety and Environmental Impact in Therapeutic LLMs
Aug 12, 2026Alireza A. Safaei, Laura M. Vowels, Matthew J. Vowels +2Energy-Efficient ML LLM Safety Evaluation
University of Isfahan Isfahan, Iran · University of Roehampton London, UK · Kivira Health New York City, New York, U.S.A.
AI Guardrail Survival under Single-Cycle Agentic Self-Summarization
Aug 11, 2026Ted Kwartler, Alan Aqrawi, Arian AbbasiLLM Guardrails LLM Safety Evaluation
Harvard Extension School, Harvard University; Americas Advanced AI Practice Lead, Accenture. · Accenture.
REDAgentBench: Executable Red Teaming and Faithful Measurement of LLM Agent Systems
Aug 11, 2026Zixing Chen, Xingyuan Liu, Jie Zhu +6LLM Red Teaming LLM Agents
Fudan University · The Hong Kong University of Science and Technology · Qwen DianJin Team, Alibaba Cloud Computing +1
ProbGuard: Calibrated Safety Risk Estimation from LLM Output Distributions
Aug 11, 2026Xinzhe Huang, Biwu Yao, Kedong Xiu +4LLM Guardrails Language Model Calibration
State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China · Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China · University of Electronic Science and Technology of China, Chengdu, China +3
TAF-MED: Multi-Turn Safety Refusal Collapse in LLMs Under Declared Self-Treatment Intent
Aug 10, 2026Waleed Jamil, Raphael SchmittLLM Safety Benchmarks LLM Safety Evaluation
1Independent Researcher, Edinburgh, United Kingdom · School of Computation, Information and Technology, Technical University of Munich, Germany · Institute of General Practice, Faculty of Medicine and Medical Center, University of Freiburg, Germany

Oct 5, 2026, cs.LGJ/K move · Enter open · S save

Dynamic Budget Allocation for LLM Evaluation under Hard Resource Constraints

Shai Feldman, Yaniv Romano

Department of Computer Science, Technion IIT, Israel · Department of Electrical and Computer Engineering, Technion IIT, Israel

LLM Evaluation LLM Safety Evaluation

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We evaluate large language models (LLMs) in multi-turn interactions through their time-to-event: the number of interaction steps required to produce an event of interest, such as a successful jailbreak or agentic task completion. Under limited compute, interactions may be terminated before the event occurs, so that event times are only partially observed (censored). Existing allocation methods for calibrating time-to-event bounds satisfy the budget only in expectation and can exceed the available budget on a particular evaluation run. Enforcing a hard constraint is particularly challenging as the cost of a trajectory is initially unknown. We introduce Hard-budget Allocation with Reflow for Predictive calibration (HARP), a budget allocation that satisfies hard resource constraints and adaptively reallocates unused budget. We show how to use HARP to construct lower predictive bounds (LPBs) on the time-to-event and to estimate evaluation metrics such as the jailbreak rate on a fixed benchmark. Although HARP induces dependence in acquisition decisions across different trajectories, we prove that HARP never exceeds the target budget, that its LPBs have finite-sample coverage guarantees, and that its metric estimates are unbiased. Experiments on agentic task success, LLM jailbreaks, toxic content generation, and RAG hallucinations show that HARP achieves coverage close to the nominal level with low variance, while never exceeding the given budget.

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