{"total":27,"rows":[{"id":"2306.04528","title":"PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts","created":"2023-06-07","cited_by_count":226,"area":"safety"},{"id":"2408.15221","title":"LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet","created":"2024-08-27","cited_by_count":151,"area":"safety"},{"id":"2101.01341","title":"Practical Blind Membership Inference Attack via Differential Comparisons","created":"2021-01-05","cited_by_count":78,"area":"safety"},{"id":"2301.11578","title":"Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers","created":"2023-01-27","cited_by_count":67,"area":"safety"},{"id":"2105.10872","title":"CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes","created":"2021-05-23","cited_by_count":56,"area":"safety"},{"id":"2112.02797","title":"ML Attack Models: Adversarial Attacks and Data Poisoning Attacks","created":"2021-12-06","cited_by_count":24,"area":"safety"},{"id":"2306.07754","title":"Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis","created":"2023-06-13","cited_by_count":23,"area":"safety"},{"id":"2410.09591","title":"Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy","created":"2024-10-12","cited_by_count":17,"area":"safety"},{"id":"2503.08704","title":"Life-Cycle Routing Vulnerabilities of LLM Router","created":"2025-03-09","cited_by_count":11,"area":"safety"},{"id":"2208.07585","title":"Neural network fragile watermarking with no model performance degradation","created":"2022-08-16","cited_by_count":8,"area":"safety"},{"id":"2311.11796","title":"Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems","created":"2023-11-20","cited_by_count":6,"area":"safety"},{"id":"2405.12786","title":"Rethinking the Vulnerabilities of Face Recognition Systems:From a Practical Perspective","created":"2024-05-21","cited_by_count":4,"area":"safety"},{"id":"2508.03365","title":"When Good Sounds Go Adversarial: Jailbreaking Audio-Language Models with Benign Inputs","created":"2025-08-05","cited_by_count":4,"area":"safety"},{"id":"2312.14260","title":"Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience","created":"2023-12-21","cited_by_count":3,"area":"safety"},{"id":"2410.20019","title":"Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions","created":"2024-10-26","cited_by_count":2,"area":"safety"},{"id":"2201.02993","title":"Rethink the Evaluation for Attack Strength of Backdoor Attacks in Natural Language Processing","created":"2022-01-09","cited_by_count":1,"area":"safety"},{"id":"2308.15673","title":"MDTD: A Multi Domain Trojan Detector for Deep Neural Networks","created":"2023-08-30","cited_by_count":1,"area":"safety"},{"id":"2402.01114","title":"Double-Dip: Thwarting Label-Only Membership Inference Attacks with Transfer Learning and Randomization","created":"2024-02-02","cited_by_count":1,"area":"safety"},{"id":"2110.12948","title":"Generating Watermarked Adversarial Texts","created":"2021-10-25","cited_by_count":0,"area":"safety"},{"id":"2202.10309","title":"HoneyModels: Machine Learning Honeypots","created":"2022-02-21","cited_by_count":0,"area":"safety"},{"id":"2211.13644","title":"Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models","created":"2022-11-24","cited_by_count":0,"area":"safety"},{"id":"2401.09740","title":"Hijacking Attacks against Neural Networks by Analyzing Training Data","created":"2024-01-18","cited_by_count":0,"area":"safety"},{"id":"2407.04589","title":"Remembering Everything Makes You Vulnerable: A Limelight on Machine Unlearning for Personalized Healthcare Sector","created":"2024-07-05","cited_by_count":0,"area":"safety"},{"id":"2604.02780","title":"A Unified Perspective on Adversarial Membership Manipulation in Vision Models","created":"2026-04-03","cited_by_count":0,"area":"safety"},{"id":"2605.08898","title":"LLM-Agnostic Semantic Representation Attack","created":"2026-05-09","cited_by_count":0,"area":"safety"},{"id":"2607.06484","title":"Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles","created":"2026-07-07","cited_by_count":0,"area":"safety"},{"id":"2607.07527","title":"A Unified Detection Framework for AI-Related Content and Artifacts","created":"2026-07-08","cited_by_count":0,"area":"safety"}],"next_cursor":null}