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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Beyond Detection: Evaluating Defensive LLMs Against AI-Generated Social Engineering in Live Turn-by-Turn Interaction
Aug 10, 2026Yuqiao Xu, Osama Zafar, Alexander Nemecek +1LLM Safety Benchmarks LLM Safety
Case Western Reserve University
Generating Attacks for LLMs with GFlowNets
Aug 10, 2026Berkay Ozcam, Irem Onen, Mehmet Fatih Amasyali +1Adversarial Prompt Generation Adversarial Attacks on LLMs
Cybersecurity R&D Turkcell Istanbul, Turkey · Department of Computer Engineering Yıldız Technical University Istanbul, Turkey
Measuring the Wrong Thing: Internal Harmfulness Scores Anti-Rank Successful Jailbreaks
Aug 10, 2026Mingyu Luo, Ming Deng, Zilang Qiu +8Safety Filtering LLM Jailbreak Attacks
College of Computer Science and Artificial Intelligence, Fudan University · School of Computer Engineering and Science, Shanghai University · Beijing Normal University +5
Safety Cost of Steering Vectors Is Separable and Reducible
Aug 9, 2026Yuxiao Li, Gjergji KasneciLanguage Model Steering LLM Safety Alignment
Technical University of Munich Munich Center for Machine Learning (MCML)
MME-Safety: A Fine-grained Benchmark for Safety Evaluation of MLLMs
Aug 8, 2026Yilian Shi, Yueming Lyu, Haoxiang Tan +5LLM Safety Benchmarks Multimodal Robustness
Nanjing University
Taxonomy-Driven Analysis of Open-Source AI Risk Mitigation Tools
Aug 7, 2026Afreen Alam, Evgenija Popchanovska, Ana Gjorgjevikj +4AI Risk Management LLM Security
Department of Administrative Sciences, Metropolitan College, Boston University, Boston, MA 02215, USA · Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje 1000, North Macedonia · Department of Computer Science, Metropolitan College, Boston University, Boston, MA 02215, USA
DelusionEval: Measuring Delusion-Linked Behaviors in AI Chatbots
Aug 5, 2026Jared Moore, Andrea Mock, Yifan Mai +9LLM Safety Benchmarks LLM Safety
Stanford University Stanford, California, USA · University of Chicago Chicago, Illinois, USA · Carnegie Mellon University Pittsburgh, Pennsylvania, USA +2
Social Pressure Breaks Majority Voting in LLM Safety Panels
Aug 5, 2026Yibo Hu, Jiaming QuNormative Conformity in Language Models Safety Filtering
Illinois Institute of Technology · Amazon
ADMITBench: A Safety-Governed Reference Framework for Evaluating the Admissibility of Industrial LLM Advisories
Aug 4, 2026Yash Misra, Javal Vyas, Siddharth Gutta +1LLM Safety Benchmarks AI-Assisted Decision Making
Refiant, Inc. · Imperial College London
EduZone: A Framework for Evaluating LLM Safety for K-12 Students and Teachers
Aug 3, 2026Junyeong Park, Jieun Han, Haneul Yoo +3LLM Safety Benchmarks Generative AI in Education
KAIST · NYU · Google Cloud AI Research
A Blind Spot in Alignment: Quantifying Biosecurity Risks in Large Language Models
Aug 3, 2026Shu Quan, Tianfang Hao, Sitong Fang +8LLM Safety Benchmarks LLM Safety
Institute for Artificial Intelligence, Peking University · The Hong Kong University of Science and Technology (Guangzhou) · Beijing Academy of Artificial Intelligence
Safety, or Just Capability? A Validity Audit of Agent-Safety Benchmarks
Jul 30, 2026Youting Wang, Xiao Han, Dingyan Shang +2LLM Safety Evaluation Benchmark Validity
RoguePrompt: Dual-Layer Encoding for Self-Reconstruction to Circumvent LLM Moderation
Jul 29, 2026Benyamin Tafreshian, Prathamesh DhakeAdversarial Attacks Adversarial Attacks on LLMs
Department of Computer Science · Boston University · Boston, MA, USA
LLM4OSC: Profile-Bound Natural Language Control with Deterministic Validation for Open Sound Control
Jul 28, 2026Yuan-Yi FanLanguage Model-Based Control LLM Reliability
Los Angeles
Inspect India Evals: An Open Benchmarking Framework for Evaluating Large Language Models in the Indian Linguistic and Cultural Context
Jul 28, 2026Abhishek Kumar Singh, Shrey Nag, Sachita +2LLM Safety Benchmarks Multilingual Language Model Evaluation
GTBIT¹ · TIET² · IGDTUW³ +2
Same Dangerous Objective, Opposite Advice: Direct Exposure versus Multi-Agent Mediation
Jul 23, 2026Linjun LiMulti-Agent LLM Systems LLM Safety
Department of Mathematics, University of Pennsylvania, Philadelphia, PA.
Judge-dependent safety gains and model-specific helpfulness costs of evidence-sufficiency prompting in clinical LLMs
Jul 20, 2026Koyar AfrasyabLLM-as-a-Judge LLM Prompting
Kinvectum AB, Sweden
Stress Testing Concept Erasure with Large Language Model Agents
Jul 20, 2026Yuyang Xue, Feng Chen, Zhihua Liu +4LLM Agent Evaluation LLM Safety Evaluation
School of Engineering, University of Edinburgh · The University of Manchester · The University of Melbourne
JailMeter: An Evidence-Based Evaluation Framework for Jailbreak Attacks on Large Language Models
Jul 20, 2026Qingjia Huang, Jingyu Zhang, Jianguo Wu +6LLM Safety Benchmarks LLM Jailbreak Attacks
Institute of Information Engineering, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Renmin University of China +2
How Jailbreak Attacks Inform Safety Alignment: A Defender-Centric, Shapley-Based Evaluation of Jailbreak Contributions
Jul 19, 2026Yukai Zhou, Feiyang Lu, Xiaokai Mao +2LLM Safety Alignment Training Data Selection
ShanghaiTech University · Zhejiang University
Retrieval-Augmented Generation in LLMs for Mental Health: Quantifying the Incremental Contribution of Retrieval Within a Layered Safety Architecture
Jul 17, 2026Anand Gupta, Akshat Surolia, Shubham Mishra +2Retrieval-Augmented Generation Intent Classification
Wysa Inc., Boston, MA, United States
Value Leakage: An LLM's Answers Are Silently Shaped by Its Own Values
Jul 15, 2026Jan Betley, Johannes Treutlein, Jan Dubiński +5LLM Alignment Language Model Bias Evaluation
1Truthful AI · 2Warsaw University of Technology · 3NASK National Research Institute +2
DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs
Jul 13, 2026Anqi Li, Jie Zhang, Zhongqi Wang +4VLM Evaluation Language Model Bias Evaluation
AMT-X: Phase-Structured Multi-Turn Red-Teaming with Checklist-Gated Evaluation
Jul 13, 2026Yi Ting Shen, Kentaroh Toyoda, Alex LeungLLM Safety Benchmarks Adversarial Attacks on LLMs
Vulcan Research, AIFT, Singapore
ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm
Jul 11, 2026Kefan Song, Yanjun QiAI Agent Auditing LLM Auditing
University of Virginia, Charlottesville, VA, USA
When Are Sparse Feature Interventions Actually Localized? Matched Evaluation for SAE-Based Safety Control
Jul 11, 2026Daming LuoSparse Autoencoders LLM Safety Evaluation
University of Technology Sydney
Operational Reframing and Approval-Framed Delegation in Multi-Agent LLM Safety
Jul 8, 2026Lifei Liu, Haoran Yu, Xiaochong Jiang +3Multi-Agent LLM Systems LLM Safety Evaluation
Independent Researcher Seattle, WA, USA · Carnegie Mellon University Pittsburgh, PA, USA · Georgia Institute of Technology Atlanta, GA, USA
Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis
Jul 7, 2026Mingchen Li, Meikang Qiu, Zifan Peng +4Software Vulnerability Detection LLMs for Cybersecurity
University of North Texas, USA · Augusta University, USA · Newcastle University, United Kingdom
Retroactive Chain-of-Thought (RetroCoT): Forensic Reconstruction Prompts as a Safety Diagnostic Across Model Generations
Jul 6, 2026Samira HajizadehPragmatic Reasoning in Language Models Adversarial Attacks on LLMs
Department of Computer Science Columbia University New York, NY, USA
Scaling Trends for Lie Detector Oversight in Preference Learning
Jul 2, 2026Oskar J. Hollinsworth, Ann-Kathrin Dombrowski, Sam Adam-Day +2Scalable Oversight Deception in Language Models

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Beyond Detection: Evaluating Defensive LLMs Against AI-Generated Social Engineering in Live Turn-by-Turn Interaction

Yuqiao Xu, Osama Zafar, Alexander Nemecek, Erman Ayday

Case Western Reserve University

LLM Safety Benchmarks LLM Safety LLM Safety Evaluation

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Generative AI makes social-engineering attacks more fluent, adaptive, and scalable, increasing the need for LLM-based de- fenders that can protect users during ongoing interactions. We ask whether such defenders identify the structural source of risk or merely react to surface cues. We formalize trust-chain localization: identifying whether an interaction fails at actor authority, asset control, verification sufficiency, or transaction path. We construct a controlled 300-case online-housing corpus spanning 20 scenario families, legitimate cases, four structural failure modes, and three surface conditions. Five defender models are evaluated on the same corpus in state- ful turn-by-turn and one-shot static settings, yielding 1,500 model-case evaluations per protocol and 3,000 in total. No model produced explicit unsafe compliance, yet defensive effectiveness varied sharply: intervention rates ranged from 0% to 96.3%. Protective action and correct structural localization were frequently decoupled, with models sometimes intervening while identifying the wrong trust component or recognizing a structural failure without taking protective action. Asset-control failures were a major localization bottleneck, surface sensitivity varied across models, and live-static differences were model-dependent. These findings show that safe-looking behavior alone is insufficient; live scam resistance must separately measure intervention, timing, structural localization, and false-positive behavior.

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