Adversarial Attacks on LLMs

LLM: Large Language Model

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37 papers in the last four weeks, up 311% on the four weeks before. 0.4% of all new papers.

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Latest papers 238

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XL-SafetyBench: A Country-Grounded Cross-Cultural Benchmark for LLM Safety and Cultural Sensitivity
May 7, 2026Dasol Choi, Eugenia Kim, Jaewon Noh +14LLM Safety Benchmarks Multilingual Language Model Evaluation
AIM Intelligence · Microsoft · Korea AISI +7
Information Theoretic Adversarial Training of Large Language Models
May 6, 2026Yiwei Zhang, Jeremiah Birrell, Reza Ebrahimi +3Adversarial Training LLM Alignment
Purdue University · West Lafayette, IN 47907, USA · Texas State University +5
Redefining AI Red Teaming in the Agentic Era: From Weeks to Hours
May 5, 2026Raja Sekhar Rao Dheekonda, Will Pearce, Nick LandersAdversarial Attacks on LLMs LLM Red Teaming
1Dreadnode, USA
Exposing LLM Safety Gaps Through Mathematical Encoding:New Attacks and Systematic Analysis
May 5, 2026Haoyu Zhang, Mohammad Zandsalimy, Shanu SushmitaLanguage Model Safety Evaluation Adversarial Attacks on LLMs
† Northeastern University · ‡ Ansys
The Compliance Trap: How Structural Constraints Degrade Frontier AI Metacognition Under Adversarial Pressure
May 4, 2026Rahul KumarLanguage Model Safety Evaluation Adversarial Attacks on LLMs
Independent Researcher
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
A Theoretical Game of Attacks via Compositional Skills
May 1, 2026Xinbo Wu, Huan Zhang, Abhishek Umrawal +1Adversarial Prompt Generation Adversarial Attacks on LLMs
University of Illinois Urbana-Champaign, Urbana, IL, USA · Stony Brook University, Stony Brook, NY, USA.
Breaking Safety at the Token Boundary: How BPE Tokenization Creates Exploitable Gaps in LLM Alignment
May 1, 2026Tung-Ling Li, Hongliang Liu, Yuhao WuLanguage Model Safety Evaluation Adversarial Attacks on LLMs
Palo Alto Networks
FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios
May 1, 2026Yutao Hou, Yihan Jiang, Yuhan Xie +5Multilingual Language Model Evaluation Language Model Safety Evaluation
Shanghai University of Finance and Economics · Beihang University · Southern University of Science and Technology +1
RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs
May 1, 2026Zhiyuan Xu, Joseph Gardiner, Sana Belguith +1Mixture-of-Experts Language Models Adversarial Attacks on LLMs
University of Bristol Bristol, United Kingdom
Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance
May 1, 2026Minchan Kwon, Sunghyun Baek, Minseo Kim +3Language Model Safety Evaluation Adversarial Attacks on LLMs
1KAIST · 2Naver AI Lab.
The Power of Order: Fooling LLMs with Adversarial Table Permutations
May 1, 2026Xinshuai Dong, Haifeng Chen, Xuyuan Liu +5Table QA Adversarial Attacks on LLMs
CMU & NEC Labs · NEC Labs · Dartmouth College & NEC Labs +1
Attention Is Where You Attack
Apr 30, 2026Aviral Srivastava, Sourav PandaSelf-Attention Adversarial Attacks
Amazon · The Pennsylvania State University
Instruction Complexity Induces Positional Collapse in Adversarial LLM Evaluation
Apr 29, 2026Jon-Paul CacioliLLM Evaluation Language Model Generation Evaluation
Independent Researcher, Melbourne, Australia
Adaptive Prompt Embedding Optimization for LLM Jailbreaking
Apr 27, 2026Miles Q. Li, Benjamin C. M. Fung, Boyang Li +2Adversarial Prompt Generation Adversarial Attacks on LLMs
School of Information Studies, McGill University, Montreal, QC, Canada · Department of Computer Science, Kean University, Union, NJ, United States · Mila - Quebec AI Institute, Montreal, QC, Canada +1
Layerwise Convergence Fingerprints for Runtime Misbehavior Detection in Large Language Models
Apr 27, 2026Nay Myat Min, Long H. Pham, Jun SunLLM Backdoor Attacks Adversarial Attacks on LLMs
Singapore Management University
When AI reviews science: Can we trust the referee?
Apr 26, 2026Jialiang Wang, Yuchen Liu, Hang Xu +7LLM Auditing Adversarial Attacks on LLMs
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR 999077, China · Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, School of Computer Science and Engeering, Southeast University, Nanjing 210000, China · These authors contributed equally +2
Semantic Denial of Service in LLM-controlled robots
Apr 25, 2026Jonathan Steinberg, Oren GalLanguage Model Safety Evaluation Adversarial Attacks on LLMs
Swarms & AI Lab (SAIL) · University of Haifa
Training a General Purpose Automated Red Teaming Model
Apr 24, 2026Aishwarya Padmakumar, Leon Derczynski, Traian Rebedea +1Adversarial Attacks on LLMs LLM Red Teaming
NVIDIA
Transient Turn Injection: Exposing Stateless Multi-Turn Vulnerabilities in Large Language Models
Apr 23, 2026Naheed Rayhan, Sohely JahanLanguage Model Safety Evaluation Adversarial Attacks on LLMs
Jagannath University · University of Barishal
Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models
Apr 22, 2026Yannis Belkhiter, Giulio Zizzo, Sergio Maffeis +2Adversarial Attacks on LLMs AI Agent Security
IBM Research Europe · Trinity College Dublin · Imperial College London +1
AVISE: Framework for Evaluating the Security of AI Systems
Apr 22, 2026Mikko Lempinen, Joni Kemppainen, Niklas RaesalmiLanguage Model Safety Evaluation Adversarial Attacks on LLMs
University of Oulu
Breaking Bad: Interpretability-Based Safety Audits of State-of-the-Art LLMs
Apr 22, 2026Krishiv Agarwal, Ramneet Kaur, Colin Samplawski +6Language Model Safety Evaluation LLM Auditing
University of Florida · SRI
ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System
Apr 20, 2026Jiacheng Liang, Yao Ma, Tharindu Kumarage +5Reward Modeling Adversarial Attacks on LLMs
Stony Brook University · Amazon Nova Responsible AI
Towards Understanding the Robustness of Sparse Autoencoders
Apr 20, 2026Ahson Saiyed, Sabrina Sadiekh, Chirag AgarwalAdversarial Attacks on LLMs Jailbreak Robustness
University of Virginia · Independent Researcher
Adversarial Humanities Benchmark: Results on Stylistic Robustness in Frontier Model Safety
Apr 20, 2026Marcello Galisai, Susanna Cifani, Francesco Giarrusso +5LLM Safety Benchmarks Adversarial Attacks on LLMs
DEXAI – Icaro Lab · Sapienza University of Rome · Sant’Anna School of Advanced Studies
Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks
Apr 20, 2026Jin Zhao, Marta Knežević, Tanja KäserLLM Evaluation Intelligent Tutoring Systems
†Work done during exchange at EPFL.
SafeAgent: A Runtime Protection Architecture for Agentic Systems
Apr 19, 2026Hailin Liu, Eugene Ilyushin, Jie Ni +1Adversarial Attacks on LLMs AI Agent Security
Lomonosov Moscow State University · Central University
Bit-Flip Vulnerability of Shared KV-Cache Blocks in LLM Serving Systems
Apr 19, 2026Yuji Yamamoto, Satoshi MatsuuraLLM Serving Adversarial Attacks on LLMs
Institute of Science Tokyo, Tokyo, Japan
Jailbreaking Large Language Models with Morality Attacks
Apr 18, 2026Ying Su, Mingen Zheng, Weili Diao +1Moral Reasoning in Language Models Language Model Safety Evaluation
South China University of Technology1 · HKUST2 · Beihang University3

May 7, 2026, cs.CLJ/K move · Enter open · S save

XL-SafetyBench: A Country-Grounded Cross-Cultural Benchmark for LLM Safety and Cultural Sensitivity

Dasol Choi, Eugenia Kim, Jaewon Noh, Sang Seo+13

AIM Intelligence · Microsoft · Korea AISI+7

LLM Safety Benchmarks Multilingual Language Model Evaluation Language Model Safety Evaluation Cross-Cultural Language Model Evaluation Adversarial Attacks on LLMs Cultural Bias in Language Models+2

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Current LLM safety benchmarks are predominantly English-centric and often rely on translation, failing to capture country-specific harms. Moreover, they rarely evaluate a model's ability to detect culturally embedded sensitivities as distinct from universal harms. We introduce XL-SafetyBench. a suite of 5,500 test cases across 10 country-language pairs, comprising a Jailbreak Benchmark of country-grounded adversarial prompts and a Cultural Benchmark where local sensitivities are embedded within innocuous requests. Each item is constructed via a multi-stage pipeline that combines LLM-assisted discovery, automated validation gates, and dual independent native-speaker annotators per country. To distinguish principled refusal from comprehension failure, we evaluate Attack Success Rate (ASR) alongside two complementary metrics we introduce: Neutral-Safe Rate (NSR) and Cultural Sensitivity Rate (CSR). Evaluating 10 frontier and 27 local LLMs reveals two key findings. First, jailbreak robustness and cultural awareness do not show a coupled relationship among frontier models, so a composite safety score obscures per-axis variation. Second, local models exhibit a near-linear ASR-NSR trade-off (r = -0.81), indicating that their apparent safety reflects generation failure rather than genuine alignment. XL-SafetyBench enables more nuanced, cross-cultural safety evaluation in the multilingual era.

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