{"total":53,"rows":[{"id":"2402.04249","title":"HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal","created":"2024-02-06","cited_by_count":1429,"area":"safety"},{"id":"2407.15549","title":"Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs","created":"2024-07-22","cited_by_count":173,"area":"safety"},{"id":"2406.07933","title":"Large Language Model Unlearning via Embedding-Corrupted Prompts","created":"2024-06-12","cited_by_count":116,"area":"safety"},{"id":"2405.15589","title":"Efficient Adversarial Training in LLMs with Continuous Attacks","created":"2024-05-24","cited_by_count":113,"area":"safety"},{"id":"2404.08676","title":"ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming","created":"2024-04-06","cited_by_count":98,"area":"safety"},{"id":"2402.14020","title":"Coercing LLMs to do and reveal (almost) anything","created":"2024-02-21","cited_by_count":89,"area":"safety"},{"id":"2406.13356","title":"Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning","created":"2024-06-19","cited_by_count":84,"area":"safety"},{"id":"2506.12618","title":"OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics","created":"2025-06-14","cited_by_count":83,"area":"safety"},{"id":"2406.18682","title":"The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm","created":"2024-06-26","cited_by_count":70,"area":"safety"},{"id":"2401.06824","title":"Revisiting Jailbreaking for Large Language Models: A Representation Engineering Perspective","created":"2024-01-12","cited_by_count":48,"area":"safety"},{"id":"2410.02760","title":"Erasing Conceptual Knowledge from Language Models","created":"2024-10-03","cited_by_count":34,"area":"safety"},{"id":"2404.12916","title":"Physical Backdoor Attack can Jeopardize Driving with Vision-Large-Language Models","created":"2024-04-19","cited_by_count":33,"area":"safety"},{"id":"2408.06223","title":"On Effects of Steering Latent Representation for Large Language Model Unlearning","created":"2024-08-12","cited_by_count":32,"area":"safety"},{"id":"2402.15180","title":"Break the Breakout: Reinventing LM Defense Against Jailbreak Attacks with Self-Refinement","created":"2024-02-23","cited_by_count":29,"area":"safety"},{"id":"2409.15154","title":"RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code","created":"2024-09-23","cited_by_count":28,"area":"safety"},{"id":"2411.12103","title":"Does Unlearning Truly Unlearn? A Black Box Evaluation of LLM Unlearning Methods","created":"2024-11-18","cited_by_count":26,"area":"safety"},{"id":"2406.11668","title":"\"Not Aligned\" is Not \"Malicious\": Being Careful about Hallucinations of Large Language Models' Jailbreak","created":"2024-06-17","cited_by_count":25,"area":"safety"},{"id":"2409.16783","title":"Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction","created":"2024-09-25","cited_by_count":25,"area":"safety"},{"id":"2503.09117","title":"GRU: Mitigating the Trade-off between Unlearning and Retention for LLMs","created":"2025-03-12","cited_by_count":12,"area":"safety"},{"id":"2605.24834","title":"Reflect-Guard: Enhancing LLM Safeguards against Adversarial Prompts via Logical Self-Reflection","created":"2026-05-24","cited_by_count":12,"area":"safety"},{"id":"2504.15047","title":"RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary Quality-Diversity Search","created":"2025-04-21","cited_by_count":11,"area":"safety"},{"id":"2503.04693","title":"UIPE: Enhancing LLM Unlearning by Removing Knowledge Related to Forgetting Targets","created":"2025-03-06","cited_by_count":10,"area":"safety"},{"id":"2505.04416","title":"OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models","created":"2025-05-07","cited_by_count":10,"area":"safety"},{"id":"2506.00359","title":"Keeping an Eye on LLM Unlearning: The Hidden Risk and Remedy","created":"2025-05-31","cited_by_count":9,"area":"safety"},{"id":"2510.21910","title":"Adversarial D\\'ej\\`a Vu: Jailbreak Dictionary Learning for Stronger Generalization to Unseen Attacks","created":"2025-10-24","cited_by_count":7,"area":"safety"},{"id":"2508.21300","title":"Improving Fisher Information Estimation and Efficiency for LoRA-based LLM Unlearning","created":"2025-08-29","cited_by_count":6,"area":"safety"},{"id":"2510.17021","title":"Forgetting to Forget: Attention Sink as A Gateway for Backdooring LLM Unlearning","created":"2025-10-19","cited_by_count":6,"area":"safety"},{"id":"2511.00030","title":"Probing Knowledge Holes in Unlearned LLMs","created":"2025-10-27","cited_by_count":4,"area":"safety"},{"id":"2511.17100","title":"Geometric-disentangelment Unlearning","created":"2025-11-21","cited_by_count":4,"area":"safety"},{"id":"2512.18035","title":"Towards Benchmarking Privacy Vulnerabilities in Selective Forgetting with Large Language Models","created":"2025-12-19","cited_by_count":4,"area":"safety"},{"id":"2602.10568","title":"Gauss-Newton Unlearning for the LLM Era","created":"2026-02-11","cited_by_count":4,"area":"safety"},{"id":"2604.21251","title":"CAP: Controllable Alignment Prompting for Unlearning in LLMs","created":"2026-04-23","cited_by_count":3,"area":"safety"},{"id":"2503.08990","title":"Effective and Efficient Jailbreaks of Black-Box LLMs with Cross-Behavior Attacks","created":"2025-03-12","cited_by_count":2,"area":"safety"},{"id":"2510.09007","title":"LLM Unlearning on Noisy Forget Sets: A Study of Incomplete, Rewritten, and Watermarked Data","created":"2025-10-10","cited_by_count":2,"area":"safety"},{"id":"2602.02824","title":"CALIBURN: Self-Calibrated LLM Unlearning Alignment","created":"2026-02-02","cited_by_count":2,"area":"safety"},{"id":"2603.01792","title":"ALTER: Asymmetric LoRA for Token-Entropy-Guided Unlearning of LLMs","created":"2026-03-02","cited_by_count":2,"area":"safety"},{"id":"2605.16776","title":"Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning","created":"2026-05-16","cited_by_count":2,"area":"safety"},{"id":"2411.16769","title":"Red-Teaming Text-to-Image Models via In-Context Experience Replay and Semantic-Preserving Prompt Rewriting","created":"2024-11-25","cited_by_count":1,"area":"safety"},{"id":"2504.06658","title":"A Neuro-inspired Interpretation of Unlearning in Large Language Models through Sample-level Unlearning Difficulty","created":"2025-04-09","cited_by_count":1,"area":"safety"},{"id":"2601.21702","title":"Beyond Forgetting: Representation Misdirection Elicits Controllable Side Behaviors and Capabilities","created":"2026-01-29","cited_by_count":1,"area":"safety"},{"id":"2602.03379","title":"Rethinking Benign Relearning: Syntax as the Hidden Driver of Unlearning Failures","created":"2026-02-03","cited_by_count":1,"area":"safety"},{"id":"2605.08765","title":"Unlearners Can Lie: Evaluating and Improving Honesty in LLM Unlearning","created":"2026-05-09","cited_by_count":1,"area":"safety"},{"id":"2602.00577","title":"SAU: Sparsity-Aware Unlearning for LLMs via Gradient Masking and Importance Redistribution","created":"2026-01-31","cited_by_count":0,"area":"safety"},{"id":"2604.17396","title":"Representation-Guided Parameter-Efficient LLM Unlearning","created":"2026-04-19","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":"2605.21674","title":"Adversarial Reframing: A Framework for Targeted Generation in Language Models","created":"2026-05-20","cited_by_count":0,"area":"safety"},{"id":"2606.10989","title":"Null-Space Constrained Low-Rank Adaptation for Response-Specified Large Language Model Unlearning","created":"2026-06-09","cited_by_count":0,"area":"safety"},{"id":"2606.18309","title":"SAGE: Retain-Aware Post-Hoc Sanitization of Final Unlearning Vector","created":"2026-06-16","cited_by_count":0,"area":"safety"},{"id":"2606.27683","title":"CBD: API-Only LLM Black-Box Unlearning through Controlled Behavioral Divergence","created":"2026-06-26","cited_by_count":0,"area":"safety"},{"id":"2608.22527","title":"Stress Testing Unlearning Algorithms","created":"2026-08-23","cited_by_count":0,"area":"safety"},{"id":"2608.25429","title":"Distance Is Not Enough: Forget-Retain Alignment Gap Predicts LLM Relearning Robustness","created":"2026-08-26","cited_by_count":0,"area":"safety"},{"id":"2609.34591","title":"TULIP: Targeted LLM Unlearning at Layers Identified Per-Input","created":"2026-09-28","cited_by_count":0,"area":"safety"},{"id":"2609.39866","title":"Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealing","created":"2026-09-30","cited_by_count":0,"area":"safety"}],"next_cursor":null}