{"total":21,"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":"2310.10383","title":"Privacy in Large Language Models: Attacks, Defenses and Future Directions","created":"2023-10-16","cited_by_count":51,"area":null},{"id":"2305.01860","title":"Towards Imperceptible Document Manipulations against Neural Ranking Models","created":"2023-05-03","cited_by_count":18,"area":null},{"id":"2403.10144","title":"NLP Verification: Towards a General Methodology for Certifying Robustness","created":"2024-03-15","cited_by_count":16,"area":null},{"id":"2210.15221","title":"TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack","created":"2022-10-27","cited_by_count":15,"area":null},{"id":"2404.01574","title":"Multi-granular Adversarial Attacks against Black-box Neural Ranking Models","created":"2024-04-02","cited_by_count":14,"area":null},{"id":"2302.02568","title":"Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend","created":"2023-02-06","cited_by_count":9,"area":null},{"id":"2405.14646","title":"Unveiling the Achilles' Heel of NLG Evaluators: A Unified Adversarial Framework Driven by Large Language Models","created":"2024-05-23","cited_by_count":6,"area":null},{"id":"2310.16955","title":"Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks","created":"2023-10-25","cited_by_count":5,"area":null},{"id":"2210.14957","title":"Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness","created":"2022-10-26","cited_by_count":4,"area":null},{"id":"2406.19413","title":"Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation","created":"2024-06-18","cited_by_count":4,"area":null},{"id":"2407.21073","title":"Enhancing Adversarial Text Attacks on BERT Models with Projected Gradient Descent","created":"2024-07-29","cited_by_count":3,"area":null},{"id":"2504.06669","title":"NLP Security and Ethics, in the Wild","created":"2025-04-09","cited_by_count":2,"area":null},{"id":"2507.03473","title":"Beyond Weaponization: NLP Security for Medium and Lower-Resourced Languages in Their Own Right","created":"2025-07-04","cited_by_count":2,"area":null},{"id":"2310.01452","title":"Fooling the Textual Fooler via Randomizing Latent Representations","created":"2023-10-02","cited_by_count":1,"area":null},{"id":"2404.08538","title":"VertAttack: Taking advantage of Text Classifiers' horizontal vision","created":"2024-04-12","cited_by_count":1,"area":null},{"id":"2412.10617","title":"BinarySelect to Improve Accessibility of Black-Box Attack Research","created":"2024-12-13","cited_by_count":1,"area":null},{"id":"2404.02931","title":"READ: Improving Relation Extraction from an ADversarial Perspective","created":"2024-04-02","cited_by_count":0,"area":null},{"id":"2412.16254","title":"Adversarial Robustness through Dynamic Ensemble Learning","created":"2024-12-20","cited_by_count":0,"area":null},{"id":"2509.20691","title":"RedHerring Attack: Testing the Reliability of Attack Detection","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2509.20699","title":"Overcoming Black-box Attack Inefficiency with Hybrid and Dynamic Select Algorithms","created":"2025-09-25","cited_by_count":0,"area":null}],"next_cursor":null}