{"total":29,"rows":[{"id":"2402.09283","title":"Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey","created":"2024-02-14","cited_by_count":130,"area":null},{"id":"2403.14472","title":"Detoxifying Large Language Models via Knowledge Editing","created":"2024-03-21","cited_by_count":87,"area":null},{"id":"2410.10700","title":"LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts","created":"2024-10-14","cited_by_count":86,"area":null},{"id":"2405.03644","title":"When LLMs Meet Cybersecurity: A Systematic Literature Review","created":"2024-05-06","cited_by_count":71,"area":null},{"id":"2506.11094","title":"The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs","created":"2025-06-06","cited_by_count":26,"area":null},{"id":"2407.05216","title":"Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course","created":"2024-07-07","cited_by_count":19,"area":null},{"id":"2408.03837","title":"WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models","created":"2024-08-07","cited_by_count":17,"area":null},{"id":"2506.24068","title":"STACK: Adversarial Attacks on LLM Safeguard Pipelines","created":"2025-06-30","cited_by_count":14,"area":null},{"id":"2402.12193","title":"A Chinese Dataset for Evaluating the Safeguards in Large Language Models","created":"2024-02-19","cited_by_count":12,"area":null},{"id":"2502.16776","title":"AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement","created":"2025-02-24","cited_by_count":12,"area":null},{"id":"2512.15712","title":"Predictive Concept Decoders: Training Scalable End-to-End Interpretability Assistants","created":"2025-12-17","cited_by_count":12,"area":null},{"id":"2404.05783","title":"A Survey on Responsible Generative AI: What to Generate and What Not","created":"2024-04-08","cited_by_count":8,"area":null},{"id":"2601.06884","title":"Paraphrasing Adversarial Attack on LLM-as-a-Reviewer","created":"2026-01-11","cited_by_count":8,"area":null},{"id":"2510.01529","title":"Bypassing Prompt Guards in Production with Controlled-Release Prompting","created":"2025-10-02","cited_by_count":7,"area":null},{"id":"2601.13384","title":"From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning","created":"2026-01-19","cited_by_count":6,"area":null},{"id":"2408.10608","title":"BTBR: A Bayesian-Theory-Driven Probabilistic-Fuzzy Framework for Implicit Bias Removal in Large Language Models","created":"2024-08-20","cited_by_count":4,"area":null},{"id":"2312.14480","title":"MetaAID 2.5: A Secure Framework for Developing Metaverse Applications via Large Language Models","created":"2023-12-22","cited_by_count":3,"area":null},{"id":"2409.20385","title":"Wait, but Tylenol is Acetaminophen... Investigating and Improving Language Models' Ability to Resist Requests for Misinformation","created":"2024-09-30","cited_by_count":2,"area":null},{"id":"2412.20087","title":"On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs","created":"2024-12-28","cited_by_count":2,"area":null},{"id":"2601.01836","title":"COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs","created":"2026-01-05","cited_by_count":2,"area":null},{"id":"2410.04155","title":"Toxic Subword Pruning for Dialogue Response Generation on Large Language Models","created":"2024-10-05","cited_by_count":1,"area":null},{"id":"2509.10682","title":"LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems","created":"2025-09-12","cited_by_count":1,"area":null},{"id":"2602.02557","title":"The Alignment Curse: Modality Alignment Supercharges Audio Attacks via Text Transfer","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2403.18205","title":"Exploring the Privacy Protection Capabilities of Chinese Large Language Models","created":"2024-03-27","cited_by_count":0,"area":null},{"id":"2504.04981","title":"TestDG: Test-time Domain Generalization for Continual Test-time Adaptation","created":"2025-04-07","cited_by_count":0,"area":null},{"id":"2601.12983","title":"ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation","created":"2026-01-19","cited_by_count":0,"area":null},{"id":"2603.22837","title":"Analysing LLM Persona Generation and Fairness Interpretation in Polarised Geopolitical Contexts","created":"2026-03-24","cited_by_count":0,"area":null},{"id":"2605.05630","title":"One Turn Too Late: Learning When to Intervene Against Multi-Turn Malicious Intent","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.09387","title":"NEXUS: Continual Learning of Symbolic Constraints for Safe and Robust Embodied Planning","created":"2026-05-10","cited_by_count":0,"area":null}],"next_cursor":null}