Adversarial Robustness of Language Models

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GPE: Evaluating Robust Evidence Aggregation for Fact Verification under Controllable GEO-Style Poisoning
Jul 22, 2026Zhaoqi Wang, Zijian Zhang, Xiaomei Yuan +4RAG Poisoning Attacks Automated Fact-Checking
School of Cyberspace Science and Technology, Beijing Institute of Technology · School of Computer Science, The University of Auckland
AIMO Interpretability Challenge
Jul 15, 2026Michal Štefánik, Philipp Mondorf, Andreas Waldis +11Mathematical Reasoning Benchmarks LLM Interpretability
National Institute of Informatics, Japan · Munich Center for Machine Learning / MaiNLP LMU · University of Tübingen +6
Can We Trust LLM's Logic? Quantifying Uncertainty, Coherence, and Robustness via a Graph-Based Framework
Jul 9, 2026Riccardo Revalor, Jalees Rehman, Debjit PalCoT Faithfulness LLM Reasoning with Graphs
¶Department of Electrical and Computer Engineering · ‡Department of Biochemistry and Molecular Genetics
Efficient Safety Alignment of Language Models via Latent Personality Traits
Jul 8, 2026Mohamed Amine Merzouk, Nolan Smyth, Damiano Fornasiere +3Adversarial Training LLM Safety Alignment
Mila, Quebec AI Institute · McGill University · Universit´e de Montr´eal +2
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
RustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities
Jul 6, 2026Tarek Elsayed, Shiping Yang, Eunsong Koh +13Software Vulnerability Detection Data Contamination in Language Models
Simon Fraser University · Trinity University · Microsoft Research +1
HaloGuard 1.0: An Open Weights Constitutional Classifier for Multilingual AI Safety
Jul 2, 2026Navaneeth Sangameswaran, Preetham S, Ashmiya LeninLLM Safety Benchmarks Language Model Safety Evaluation
Astroware AI, Delaware, USA · Karunya Institute of Technology and Sciences, Coimbatore, India
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
IHDec: Divergence-Steered Contrastive Decoding for Securing Multi-Turn Instruction Hierarchies
Jun 29, 2026Nicole Geumheon Liu, Haeun Jang, Yonghyun Jun +1Instruction Following Prompt Injection Defense
Chung-Ang University, Seoul, Korea
Low-Agreeableness Persona Conditioning for Safe LLM Fine-Tuning
Jun 26, 2026Austin MY Cheung, Yi YangLLM Fine-Tuning LLM Safety Alignment
Hong Kong University of Science and Technology
Yuvion LLM: An Adversarially-Aware Large Language Model for Content And AI Safety
Jun 26, 2026Ting Ma, Xiufeng Huang, Benlei Cui +43LLM Safety Benchmarks LLM Safety Alignment
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
Jun 25, 2026Ryan FettermanAdversarial Attacks LLM Fine-Tuning
Cisco Talos
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
At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization
Jun 24, 2026Praneet Suresh, Jack Stanley, Sonia Joseph +2Distribution Shift Robustness Transformer Interpretability
Mila Quebec AI Institute · Meta AI · Sapient Intelligence
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
Heterogeneous LLM Debate Under Adversarial Peers: Honest Gains, Replacement Costs, and Resilience
Jun 18, 2026Prashanti Nilayam, Kiran Kumar Ramanna, Prashil Tumbade +1Multi-Agent LLM Systems Adversarial Attacks on LLMs
ServiceNow
Beyond Safe Data: Pretraining-Stage Alignment with Regular Safety Reflection
Jun 17, 2026Jinhan Li, Kexian Tang, Yihan Xu +2Language Model Pretraining LLM Alignment
Institute for Interdisciplinary Information Sciences, Tsinghua University
A Red-Team Study of Anthropic Fable 5 & Opus 4.8 Models
Jun 16, 2026Nicola FrancoLanguage Model Safety Evaluation Neural Network Robustness
Head of AI Security Lab The Italian Institute of Artificial Intelligence (AI4I) Corso Castelfidardo 22, 10129 Turin, Italy
AnchorKV: Safety-Aware KV Cache Compression via Soft Penalty with a Refusal Anchor
Jun 16, 2026Ning Ni, Yingjie LaoKV Caching LLM Safety Alignment
Department of Computer Science Tufts University · Department of Electrical and Computer Engineering Tufts University
Who Flips? Self- and Cross-Model Counterarguments Reveal Answer Instability in LLMs
Jun 14, 2026Nafiseh Nikeghbal, Amir Hossein Kargaran, Shaghayegh Kolli +1LLM Evaluation LLM Reliability
1Technical University of Munich · Munich Center for Machine Learning · 1Technical University of Munich 2 Munich Center for Machine Learning +2
Measuring Epistemic Resilience of LLMs Under Misleading Medical Context
Jun 10, 2026Hongjian Zhou, Xinyu Zou, Jinge Wu +19LLM Reliability Medical QA
University of Oxford · University of Washington · University College London +1
Risk Under Pressure: Compute-Aware Evaluation of Adversarial Robustness in Language Models
Jun 9, 2026Malikeh Ehghaghi, Boglárka Ecsedi, Marsha Chechik +1LLM Alignment Neural Network Robustness
University of Toronto · Vector Institute · Hugging Face
Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning
Jun 9, 2026Eitan Cohen, Idan Simai, Uri ShahamAdversarial Training Neural Network Robustness
Bar-Ilan University · Ramat Gan, Israel
Game-Theoretic Multi-Agent Control for Robust Contextual Reasoning in LLMs
Jun 9, 2026Saeid Jamshidi, Amin Nikanjam, Arghavan Moradi Dakhel +2Adversarial Attacks on LLMs Multi-Agent Control
SWAT Laboratory, Polytechnique Montréal, Montréal, QC, Canada · Huawei Distributed Scheduling and Data Engine Lab, Montr´eal, QC, Canada
Learning to Attack and Defend: Adaptive Red Teaming of Language Models via GRPO
Jun 8, 2026Blake Bullwinkel, Eugenia Kim, Amanda Minnich +1Adversarial Training Adversarial Attacks on LLMs
Microsoft AI Red Team · Microsoft Azure
Defending Against Malicious Finetuning by Scaling Train-time Adversarial Attacks
Jun 6, 2026Haoming Wen, Shi Chen, Qingyu Shi +4Adversarial Training LLM Alignment
1Xiongan AI Institute · Institute for Interdisciplinary Information Sciences, Tsinghua University · 3Shanghai Qi Zhi Institute
Adversarial Robustness of Activation Steering in Large Language Models
Jun 5, 2026Kien Le, Thai LeLLM Evaluation Language Model Steering
Independent Researcher · Indiana University
Inference-Time Vulnerability Beyond Shallow Safety: Alignment Along Generation Trajectories
Jun 3, 2026Kyungmin Park, Taesup KimAdversarial Attacks on LLMs LLM Safety Alignment
Hankuk University of Foreign Studies · Seoul National University
Hybrid Adversarial Defence for Natural Language Understanding Tasks
Jun 3, 2026Manar Abouzaid, Yang Wang, Chenghua Lin +1LLM Hallucination Mitigation LLM Security
School of Electronics and Computer Science, University of Southampton, UK · Department of Computer Science, University of Manchester, UK
Which Defense Closes Which Threat? Attributing OWASP-LLM-Top-10 Coverage and Its Brittleness Under Paraphrasing
Jun 1, 2026Alexandre Cristovão MaioranoLLM Red Teaming AI Agent Security Benchmarks
Lumytics

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GPE: Evaluating Robust Evidence Aggregation for Fact Verification under Controllable GEO-Style Poisoning

Zhaoqi Wang, Zijian Zhang, Xiaomei Yuan, Pengtao Kou+3

School of Cyberspace Science and Technology, Beijing Institute of Technology · School of Computer Science, The University of Auckland

RAG Poisoning Attacks Automated Fact-Checking Adversarial Robustness of Language Models RAG Evaluation

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Large language models increasingly use search tools to retrieve up-to-date information, introducing a new attack surface in which retrieved documents can be manipulated. This risk is amplified by the development of generative engine optimization, which can make selected content more likely to be retrieved, cited, and adopted by models. Existing fact-verification benchmarks and evaluation frameworks do not provide the controlled evidence environments needed to assess robustness against GEO poisoning. We therefore propose GPE, which consists of a multi-domain fact-verification benchmark and an evaluation framework for controlling evidence sources and poisoning ratios. Experiments across multiple verification methods and poisoning attacks demonstrate that GPE exposes robustness degradation and efficiency trade-offs that cannot be observed through clean evaluation alone, confirming the need to evaluate fact verification under adversarial evidence environments.

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