{"total":34,"rows":[{"id":"2305.11391","title":"A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation","created":"2023-05-19","cited_by_count":93,"area":null},{"id":"2308.12833","title":"Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities","created":"2023-08-24","cited_by_count":82,"area":null},{"id":"2310.10383","title":"Privacy in Large Language Models: Attacks, Defenses and Future Directions","created":"2023-10-16","cited_by_count":51,"area":null},{"id":"2205.13616","title":"Towards A Proactive ML Approach for Detecting Backdoor Poison Samples","created":"2022-05-26","cited_by_count":40,"area":null},{"id":"2111.12965","title":"Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks","created":"2021-11-25","cited_by_count":34,"area":null},{"id":"2203.00858","title":"Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving","created":"2022-03-02","cited_by_count":31,"area":null},{"id":"2307.08925","title":"Integration of Large Language Models and Federated Learning","created":"2023-07-18","cited_by_count":31,"area":null},{"id":"2303.02242","title":"TrojText: Test-time Invisible Textual Trojan Insertion","created":"2023-03-03","cited_by_count":28,"area":null},{"id":"2311.17431","title":"Grounding Foundation Models through Federated Transfer Learning: A General Framework","created":"2023-11-29","cited_by_count":24,"area":null},{"id":"2301.01197","title":"Backdoor Attacks Against Dataset Distillation","created":"2023-01-03","cited_by_count":21,"area":null},{"id":"2405.11227","title":"BadActs: A Universal Backdoor Defense in the Activation Space","created":"2024-05-18","cited_by_count":21,"area":null},{"id":"2302.09457","title":"Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective","created":"2023-02-19","cited_by_count":20,"area":null},{"id":"2303.04729","title":"Stealing the Decoding Algorithms of Language Models","created":"2023-03-08","cited_by_count":19,"area":null},{"id":"2308.04466","title":"Backdoor Federated Learning by Poisoning Backdoor-Critical Layers","created":"2023-08-08","cited_by_count":18,"area":null},{"id":"2402.12168","title":"Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning","created":"2024-02-19","cited_by_count":16,"area":null},{"id":"2406.12844","title":"Synergizing Foundation Models and Federated Learning: A Survey","created":"2024-06-18","cited_by_count":14,"area":null},{"id":"2305.07406","title":"Two-in-One: A Model Hijacking Attack Against Text Generation Models","created":"2023-05-12","cited_by_count":12,"area":null},{"id":"2204.04063","title":"Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings","created":"2022-04-07","cited_by_count":11,"area":null},{"id":"2308.12439","title":"BaDExpert: Extracting Backdoor Functionality for Accurate Backdoor Input Detection","created":"2023-08-23","cited_by_count":11,"area":null},{"id":"2311.17400","title":"Improving the Robustness of Transformer-based Large Language Models with Dynamic Attention","created":"2023-11-29","cited_by_count":10,"area":null},{"id":"2209.03563","title":"SSL-WM: A Black-Box Watermarking Approach for Encoders Pre-trained by Self-supervised Learning","created":"2022-09-08","cited_by_count":9,"area":null},{"id":"2409.14119","title":"Obliviate: Neutralizing Task-agnostic Backdoors within the Parameter-efficient Fine-tuning Paradigm","created":"2024-09-21","cited_by_count":9,"area":null},{"id":"2506.06518","title":"A Systematic Review of Poisoning Attacks Against Large Language Models","created":"2025-06-06","cited_by_count":8,"area":null},{"id":"2401.15883","title":"Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability","created":"2024-01-29","cited_by_count":6,"area":null},{"id":"2404.00076","title":"A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks","created":"2024-03-29","cited_by_count":4,"area":null},{"id":"2402.04435","title":"PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection","created":"2024-02-06","cited_by_count":3,"area":null},{"id":"2411.04811","title":"Defending Deep Regression Models against Backdoor Attacks","created":"2024-11-07","cited_by_count":3,"area":null},{"id":"2205.13613","title":"Circumventing Backdoor Defenses That Are Based on Latent Separability","created":"2022-05-26","cited_by_count":1,"area":null},{"id":"2302.04977","title":"Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines","created":"2023-02-09","cited_by_count":1,"area":null},{"id":"2505.11586","title":"The Ripple Effect: On Unforeseen Complications of Backdoor Attacks","created":"2025-05-16","cited_by_count":1,"area":null},{"id":"2408.11046","title":"Inside the Black Box: Detecting Data Leakage in Pre-trained Language Encoders","created":"2024-08-20","cited_by_count":0,"area":null},{"id":"2502.20306","title":"SecureGaze: Defending Gaze Estimation Against Backdoor Attacks","created":"2025-02-27","cited_by_count":0,"area":null},{"id":"2505.00881","title":"Protocol-agnostic and Data-free Backdoor Attacks on Pre-trained Models in RF Fingerprinting","created":"2025-05-01","cited_by_count":0,"area":null},{"id":"2506.09066","title":"ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices","created":"2025-06-08","cited_by_count":0,"area":null}],"next_cursor":null}