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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An Inline Control Architecture for Language Models in Intelligent Transportation Systems
Aug 4, 2026Narendra Kumar Dewangan, Mounira MsahliLLM Guardrails Adversarial Attacks on LLMs
LTCI, Département Informatique et Réseaux, Télécom Paris, Institut Polytechnique de Paris, France
ICO: Enhancing Semantic-Shift Jailbreaks via Iterative Context Optimization
Aug 4, 2026Hujian Zhu, Yihao Huang, Felix Juefei-Xu +5Adversarial Attacks on LLMs Jailbreak Attacks
East China Normal University, Shanghai, China · New York University, New York, USA · The Hong Kong Polytechnic University, Hong Kong, China +2
Adversarial Stress Testing of Role-Playing Language Agents using Multi-Agent Evaluation
Aug 4, 2026Saqib Shouqi, Abdullah Nazly, Januki Wanniarachchi +1Adversarial Attacks on LLMs LLM Agent Evaluation
School of Computing, Informatics Institute of Technology, Colombo, Sri Lanka
No Single Neuron of Failure: Distributed Safety Alignment Against White-Box Attacks
Aug 2, 2026Simiao Xie, Chuancheng Shi, Shangze Li +5LLM Alignment Adversarial Attacks on LLMs
The University of Sydney · NExT++ Research Centre, National University of Singapore
Moving the Safety Barrier: Dynamic Routing Adaptive Alignment Against White-Box Attacks
Aug 2, 2026Shangze Li, Chuancheng Shi, Simiao Xie +6Adversarial Attacks on LLMs LLM Safety Alignment
1Nanjing University of Science and Technology · 2The University of Sydney · 5NExT++ Research Centre, National University of Singapore +2
Adversarial Attacks in Multi-Agent LLM Pipelines: Unveiling Structural Vulnerabilities in Agentic AI Architectures
Aug 1, 2026Faisal Haque Bappy, Tahrim Hossain, Tarannum Shaila Zaman +3Adversarial Attacks on LLMs AI Agent Security
University of Maryland Baltimore County, MD, USA · Kent State University, OH, USA · Tennessee State University, TN, USA
Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs
Jul 30, 2026Pingyu Wu, Lingyao Zhu, Weiming Zhang +1Adversarial Attacks on LLMs Safety Filtering
University of Science and Technology of China · Hefei AiDA Lab · Zhejiang Wanli University
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
Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks
Jul 28, 2026Yu Yan, Jiahao Chen, Siqi Lu +6LLM Alignment Adversarial Attacks on LLMs
College of Computer Science and Technology, Zhejiang University · Henan Key Laboratory of Network Cryptography Technology, Information Engineering University · State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences
Ranked by Position: Order Sensitivity as an Exploitable Attack Surface in LLM Listwise Recommenders
Jul 26, 2026Ge Zhang, Jingru Cheng, Huiyuan ChenLearning to Rank Adversarial Attacks
Stanford University · Independent Researcher
Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security
Jul 20, 2026Devina Jain, David Hartmann, Chuan LiAdversarial Attacks on LLMs LLM Agent Evaluation
Lambda
Implicit Reasoning Steering via Concept Chaining
Jul 15, 2026Xiao Ye, Sanika Chavan, Yuxi Huang +4Adversarial Attacks on LLMs Reasoning Consistency in Language Models
School of Computing and Augmented Intelligence, Arizona State University · Bellini College of AI, Cybersecurity, and Computing, University of South Florida, Tampa, Florida, USA · Department of Computer Science, University of California, Davis
Adversarial Prompting Framework for AI Safety Assessment
Jul 15, 2026Yash Bhatnagar, Kunal Banerjee, Anirban ChatterjeeAdversarial Prompt Generation Adversarial Attacks on LLMs
Microsoft · Bengaluru, Karnataka, India · Walmart Global Tech
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
Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability
Jul 11, 2026Lingwei Wei, Dou Hu, Wei Zhou +2Fake News Detection Adversarial Attacks on LLMs
Institute of Information Engineering, Chinese Academy of Sciences, China · University of Illinois Chicago, USA · State Key Laboratory of Media Convergence and Communication, Communication University of China, China +1
Optimizing Against Safety Representations: Activation-Guided Adversarial Suffixes and the Geometry of Refusal
Jul 9, 2026Ege Çakar, Hannah Guan, Kayden KeheLLM Alignment Adversarial Attacks on LLMs
Harvard University
Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring
Jul 9, 2026Jennifer Za, Julija Bainiaksina, Nikita Ostrovsky +2Language Model Safety Evaluation Adversarial Attacks on LLMs
LASR Labs · University College London · Google DeepMind
Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs
Jul 8, 2026Anupam Wagle, Ifrat Ikhtear Uddin, Chaowei Zhang +1LLM Interpretability Adversarial Attacks on LLMs
Department of Computer Science, University of South Dakota, USA · School of Information and Artificial Intelligence, Yangzhou University, Yangzhou, China
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
Safety Targeted Embedding Exploit via Refinement
Jul 2, 2026Joshua Adrian CahyonoMultilingual Language Model Evaluation Adversarial Attacks on LLMs
Nanyang Technological University, Singapore.
HARC: Coupling Harmfulness and Refusal Directions for Robust Safety Alignment
Jul 1, 2026Shei Pern Chua, Hao Wu, Qianli Ma +1Adversarial Attacks on LLMs LLM Safety Alignment
Tsinghua University · Microsoft · Southeast University
Truth or Sophistry? LoFa: A Benchmark for LLM Robustness Against Logical Fallacies
Jun 30, 2026Xudong Shen, Li Yuan, Ye Chen +3LLM Evaluation Adversarial Attacks on LLMs
Shenzhen International Graduate School, Tsinghua University · South China University of Technology
Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection
Jun 29, 2026Asif Shahriar, Hongyu Cai, Hadjer Benkraouda +2Software Vulnerability Detection Adversarial Attacks on LLMs
BRAC University · Purdue University · University of Illinois Urbana-Champaign
Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing
Jun 29, 2026Dvir Alsheich, Adar Peleg, Ben Hagag +3Adversarial Attacks on LLMs Agent Evaluation
1Technion - Israel Institute of Technology · 2Carnegie Mellon University
Closing the Activation-Cone Blind Spot: Response-Time Probing and Unified Defense
Jun 28, 2026Subhadip MitraAdversarial Attacks on LLMs LLM Safety
Independent Researcher
Jailbreaking for the Average Jane: Choosing Optimal Jailbreaks via Bandit Algorithms for Automatically Enhanced Queries
Jun 25, 2026Prarabdh Shukla, Ritik, Suhas Rao +2Adversarial Attacks on LLMs LLM Jailbreak Attacks
Centre for Machine Intelligence and Data Science, IIT Bombay · Department of Computer Science and Engineering, IIT Bombay
Adversarial Diffusion Across Modalities: A Fusion Survey of Attacks, Defenses, and Evaluation for Text, Vision, and Vision-Language Models
Jun 25, 2026Abrar Alotaibi, Moataz AhmedAdversarial Attacks on VLMs Adversarial Attacks
Information and Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia · College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia · SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia
Do Encoders Suffice? A Systematic Comparison of Encoder and Decoder Safety Judges for LLM Adversarial Evaluation
Jun 24, 2026Han Jeon, Shiv Medler, Joseph Voyles +1LLM Safety Benchmarks LLM-as-a-Judge
PricewaterhouseCoopers, U.S.A.
How Reliable Is Your Jailbreak Judge? Calibration and Adversarial Robustness of Automated ASR Scoring
Jun 24, 2026Yang GaoLLM Evaluation LLM-as-a-Judge
Veyon Solutions
A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation
Jun 24, 2026Abrar Alotaibi, Raed Mughus, Moataz AhmedMultilingual Language Model Evaluation LLM Evaluation
Information and Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia. · College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia. · SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia.

Aug 4, 2026, cs.CRJ/K move · Enter open · S save

An Inline Control Architecture for Language Models in Intelligent Transportation Systems

Narendra Kumar Dewangan, Mounira Msahli

LTCI, Département Informatique et Réseaux, Télécom Paris, Institut Polytechnique de Paris, France

LLM Guardrails Adversarial Attacks on LLMs LLM Security V2X Communication

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Vehicle-to-everything (V2X) systems increasingly incorporate large language models (LLMs) for semantic tasks such as message summarization, operator assistance, and decision support at roadside units and edge nodes. Although these components are not part of safety-critical control loops, they introduce prompt-level attack surfaces that are not addressed by traditional V2X security mechanisms focused on authentication and message integrity. This paper presents Guarded-V2X, an inline semantic guardrail architecture for securing LLM-enabled V2X services under real-time constraints. The proposed system integrates rule-based ingress filtering, a lightweight safety classifier, policy-constrained structured generation, trusted-only retrieval, and post-decision adjudication to enforce machine-checkable safety boundaries prior to downstream execution. Guarded-V2X is evaluated using a four-stage experimental pipeline encompassing intrusion vulnerability analysis, calibration and latency benchmarking, guardrail validation, and robustness under adversarial stress. Experiments are conducted on a V2X-aligned simulated dataset derived from RSU advisories, operator messages, and annotated V2X message summaries. Results show that unguarded and prompt-only baselines retain residual vulnerability under multi-turn adversarial trials, while Guarded-V2X consistently reduces intrusion acceptance success rates and eliminates observed unsafe completions in two-turn settings, without exceeding latency budgets for V2X semantic advisory paths.

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