{"total":13,"rows":[{"id":"2103.07853","title":"Membership Inference Attacks on Machine Learning: A Survey","created":"2021-03-14","cited_by_count":321,"area":"safety"},{"id":"2405.20773","title":"Visual-RolePlay: Universal Jailbreak Attack on MultiModal Large Language Models via Role-playing Image Character","created":"2024-05-25","cited_by_count":68,"area":"safety"},{"id":"2405.20778","title":"Improved Generation of Adversarial Examples Against Safety-aligned LLMs","created":"2024-05-28","cited_by_count":18,"area":"safety"},{"id":"2206.01367","title":"Adversarial Unlearning: Reducing Confidence Along Adversarial Directions","created":"2022-06-03","cited_by_count":14,"area":"safety"},{"id":"2409.14747","title":"Distribution-Level Feature Distancing for Machine Unlearning: Towards a Better Trade-off Between Model Utility and Forgetting","created":"2024-09-23","cited_by_count":7,"area":"safety"},{"id":"2203.00915","title":"MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members","created":"2022-03-02","cited_by_count":6,"area":"safety"},{"id":"2504.14985","title":"aiXamine: Simplified LLM Safety and Security","created":"2025-04-21","cited_by_count":4,"area":"safety"},{"id":"2412.16359","title":"Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context","created":"2024-12-20","cited_by_count":3,"area":"safety"},{"id":"2512.20677","title":"Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models","created":"2025-12-21","cited_by_count":2,"area":"safety"},{"id":"2508.02422","title":"Superior resilience to poisoning and amenability to unlearning in quantum machine learning","created":"2025-08-04","cited_by_count":1,"area":"safety"},{"id":"2410.05284","title":"Hypnopaedia-Aware Machine Unlearning via Psychometrics of Artificial Mental Imagery","created":"2024-09-29","cited_by_count":0,"area":"safety"},{"id":"2511.07947","title":"Class-feature Watermark: A Resilient Black-box Watermark Against Model Extraction Attacks","created":"2025-11-11","cited_by_count":0,"area":"safety"},{"id":"2512.17367","title":"Adversarially Robust Detection of Harmful Online Content: A Computational Design Science Approach","created":"2025-12-19","cited_by_count":0,"area":"safety"}],"next_cursor":null}