Adversarial Text Attacks

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1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

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When Efficiency Backfires: Cascading LLMs Trigger Cascade Failure under Adversarial Attack
May 17, 2026Zehan Sun, Dingfan Chen, Songze LiAdversarial Attacks on LLMs LLM Security
1Southeast · 2Max Planck Institute for Intelligent Systems
Paraphrasing Attack Resilience of Various AI-Generated Text Detection Methods
May 14, 2026Andrii Shportko, Inessa VerbitskyAI-Generated Text Detection Robustness of Machine-Generated Text Detection
Northwestern University · Evanston, IL, USA
Vaporizer: Breaking Watermarking Schemes for Large Language Model Outputs
May 8, 2026Jonathan Hong Jin Ng, Anh Tu Ngo, Anupam ChattopadhyayWatermark Removal LLM Watermarking
College of Computing and Data Science Nanyang Technological University Singapore
Algospeak, Hiding in the Open: The Trade-off Between Legible Meaning and Detection Avoidance
May 7, 2026Jan Fillies, Ronald E. Robertson, Jeffrey HancockLanguage Model Safety Evaluation Adversarial Text Attacks
Stanford University Stanford, California, USA
Conceal, Reconstruct, Jailbreak: Exploiting the Reconstruction-Concealment Tradeoff in MLLMs
May 7, 2026Md Farhamdur Reza, Richeng Jin, Tianfu Wu +1Multimodal Large Language Models Jailbreak Attacks
1NC State University · 2Zhejiang University
Chainwash: Multi-Step Rewriting Attacks on Diffusion Language Model Watermarks
May 6, 2026Mohd Ruhul Ameen, Akif Islam, Nadim Mahmud +1Diffusion Model Watermarking Watermark Removal
College of Engineering and Computer Science, Marshall University, Huntington, WV, USA · Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh · Miami University, Oxford, OH 45056, USA
Beating the Style Detector: Three Hours of Agentic Research on the AI-Text Arms Race
May 4, 2026Andreas Maier, Moritz Zaiss, Siming BayerAI-Generated Text Detection Language Model Generation Evaluation
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany · Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Fight Poison with Poison: Enhancing Robustness in Few-shot Machine-Generated Text Detection with Adversarial Training
May 4, 2026Wenjing Duan, Qi Zhou, Yuanfan LiAdversarial Training AI-Generated Text Detection
Faculty of Electronic and Information Engineering, Xi’an Jiaotong University
Led to Mislead: Adversarial Content Injection for Attacks on Neural Ranking Models
May 2, 2026Amin Bigdeli, Amir Khosrojerdi, Radin Hamidi Rad +3Learning to Rank Adversarial Attacks
University of Waterloo, Canada · University of Toronto, Canada · Mila – Quebec AI Institute, Canada +1
StyleShield: Exposing the Fragility of AIGC Detectors through Continuous Controllable Style Transfer
Apr 30, 2026Guantian ZhengFlow Matching AI-Generated Text Detection
Emotion-Aware Clickbait Attack in Social Media
Apr 30, 2026Syed Mhamudul Hasan, Mohd. Farhan Israk Soumik, Abdur R. ShahidSocial Media Analysis Adversarial Text Attacks
Southern Illinois University Carbondale, IL, USA
Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines
Apr 26, 2026Mazal Bethany, Kim-Kwang Raymond Choo, Nishant Vishwamitra +1Fake News Detection Adversarial Prompt Generation
Department of Information Systems and Cybersecurity, University of Texas at San Antonio, San Antonio, TX 78249 USA
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
Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution
Apr 11, 2026Robert DilworthStylometry Authorship Attribution
Department of Computer Science and Engineering, Mississippi State University, Mississippi State, Mississippi, USA
RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Adaptive LLM-Generated Fake News Detection
Jan 7, 2026Song-Duo Ma, Yi-Hung Liu, Hsin-Yu Lin +4Adversarial Training Fake News Detection
National Taiwan University, Taipei, Taiwan
destroR: A Benchmark and Adversarial-Training Defense for Bangla Transfer Models under Meaning-Preserving Attacks
Nov 13, 2025Saadat Rafid Ahmed, Rubayet Shareen, Radoan Sharkar +3Adversarial Training Sentiment Analysis
Dept. of Computer Science and Engineering, BRAC University
Towards Inclusive Toxic Content Moderation: Addressing Vulnerabilities to Adversarial Attacks in Toxicity Classifiers Tackling LLM-generated Content
Sep 16, 2025Shaz Furniturewala, Arkaitz ZubiagaMechanistic Interpretability Toxicity Detection
Center for Data Science, New York University · Queen Mary University of London
TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual Similarity
Dec 3, 2024Xi Cao, Quzong Gesang, Yuan Sun +2Adversarial Robustness of Language Models Adversarial Text Attacks
Minzu University of China, Beijing, China · National Language Resource Monitoring & Research Center Minority Languages Branch, Beijing, China · Tibet University, Lhasa, China +1
Perturbation Effects on Robustness and Individual Fairness
Apr 1, 2024Xuran Li, Hao Xue, Peng Wu +4Neural Network Robustness Algorithmic Fairness
University of New South Wales Sydney, Australia · The Hong Kong University of Science and Technology Guangzhou, China · Key Laboratory of System Software, Institute of Software, Chinese Academy of Sciences Beijing, China +3

May 17, 2026, cs.CRJ/K move · Enter open · S save

When Efficiency Backfires: Cascading LLMs Trigger Cascade Failure under Adversarial Attack

Zehan Sun, Dingfan Chen, Songze Li

1Southeast · 2Max Planck Institute for Intelligent Systems

Adversarial Attacks on LLMs LLM Security Adversarial Text Attacks

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Large Language Model (LLM) cascade systems are designed to balance efficiency and performance by processing queries with lightweight models while selectively escalating complex cases to more powerful ones. Such systems seek to reduces computational cost and latency while maintaining task performance, making it an appealing choice for large-scale deployment. However, the cascade design introduces new vulnerabilities through an expanded attack surface: the inclusion of lightweight front-end models and internal decision mechanisms introduces new weaknesses. In this work, we present the first study demonstrating that LLM cascade systems are susceptible to targeted adversarial manipulation, which disrupts both performance objectives and the intended cost advantages of the cascade design. We propose a novel attack framework that employs constrained sequential collaborative optimization of adversarial suffix under cascade dependencies, enabling simultaneous exploitation of lightweight models and decision mechanisms. This framework adapts to adversaries with varying capabilities, inducing controllable degradation in both cost-efficiency and accuracy. Unlike prior attacks targeting standalone models, our approach strategically leverages the cascade structure to achieve significantly stronger impact. Extensive experiments across diverse datasets and representative LLM cascade systems validate the practicality and severity of this attack. Our findings highlight the urgent need to rigorously scrutinize the security of LLM cascade systems and call for broader attention to the systemic risks inherent in such designs.

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