{"total":182,"rows":[{"id":"2202.03286","title":"Red Teaming Language Models with Language Models","created":"2022-02-07","cited_by_count":1026,"area":"safety"},{"id":"2112.03570","title":"Membership Inference Attacks From First Principles","created":"2021-12-07","cited_by_count":878,"area":"safety"},{"id":"2103.03279","title":"Remember What You Want to Forget: Algorithms for Machine Unlearning","created":"2021-03-04","cited_by_count":371,"area":"safety"},{"id":"2103.07853","title":"Membership Inference Attacks on Machine Learning: A Survey","created":"2021-03-14","cited_by_count":321,"area":"safety"},{"id":"2302.00539","title":"Analyzing Leakage of Personally Identifiable Information in Language Models","created":"2023-02-01","cited_by_count":285,"area":"safety"},{"id":"2109.13398","title":"Unrolling SGD: Understanding Factors Influencing Machine Unlearning","created":"2021-09-27","cited_by_count":279,"area":"safety"},{"id":"2111.09679","title":"Enhanced Membership Inference Attacks against Machine Learning Models","created":"2021-11-18","cited_by_count":248,"area":"safety"},{"id":"2108.11577","title":"Machine Unlearning of Features and Labels","created":"2021-08-26","cited_by_count":229,"area":"safety"},{"id":"2402.07841","title":"Do Membership Inference Attacks Work on Large Language Models?","created":"2024-02-12","cited_by_count":195,"area":"safety"},{"id":"2205.08096","title":"Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher","created":"2022-05-17","cited_by_count":194,"area":"safety"},{"id":"2308.07707","title":"Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening","created":"2023-08-15","cited_by_count":181,"area":"safety"},{"id":"2203.03929","title":"Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks","created":"2022-03-08","cited_by_count":175,"area":"safety"},{"id":"2111.08440","title":"On the Importance of Difficulty Calibration in Membership Inference Attacks","created":"2021-11-15","cited_by_count":147,"area":"safety"},{"id":"2201.05629","title":"Zero-Shot Machine Unlearning","created":"2022-01-14","cited_by_count":133,"area":"safety"},{"id":"2402.15159","title":"Machine Unlearning of Pre-trained Large Language Models","created":"2024-02-23","cited_by_count":119,"area":"safety"},{"id":"2312.03262","title":"Low-Cost High-Power Membership Inference Attacks","created":"2023-12-06","cited_by_count":107,"area":"safety"},{"id":"2208.14933","title":"Membership Inference Attacks by Exploiting Loss Trajectory","created":"2022-08-31","cited_by_count":101,"area":"safety"},{"id":"2402.15570","title":"Fast Adversarial Attacks on Language Models In One GPU Minute","created":"2024-02-23","cited_by_count":88,"area":"safety"},{"id":"2102.02551","title":"ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models","created":"2021-02-04","cited_by_count":85,"area":"safety"},{"id":"2112.02701","title":"Protecting Intellectual Property of Language Generation APIs with Lexical Watermark","created":"2021-12-05","cited_by_count":84,"area":"safety"},{"id":"2201.06640","title":"Towards Adversarial Evaluations for Inexact Machine Unlearning","created":"2022-01-17","cited_by_count":84,"area":"safety"},{"id":"2204.07655","title":"Deep Unlearning via Randomized Conditionally Independent Hessians","created":"2022-04-15","cited_by_count":84,"area":"safety"},{"id":"2110.08324","title":"Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture","created":"2021-10-15","cited_by_count":67,"area":"safety"},{"id":"2309.06746","title":"DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass","created":"2023-09-13","cited_by_count":67,"area":"safety"},{"id":"2302.12580","title":"Membership Inference Attacks against Synthetic Data through Overfitting Detection","created":"2023-02-24","cited_by_count":66,"area":"safety"},{"id":"2406.17975","title":"SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)","created":"2024-06-25","cited_by_count":59,"area":"safety"},{"id":"2402.14904","title":"Watermarking Makes Language Models Radioactive","created":"2024-02-22","cited_by_count":58,"area":"safety"},{"id":"2109.08045","title":"Membership Inference Attacks Against Recommender Systems","created":"2021-09-16","cited_by_count":57,"area":"safety"},{"id":"2310.15007","title":"Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models","created":"2023-10-23","cited_by_count":56,"area":"safety"},{"id":"2402.09674","title":"PAL: Proxy-Guided Black-Box Attack on Large Language Models","created":"2024-02-15","cited_by_count":50,"area":"safety"},{"id":"2409.19798","title":"Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data","created":"2024-09-29","cited_by_count":50,"area":"safety"},{"id":"2401.10371","title":"Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning","created":"2024-01-18","cited_by_count":49,"area":"safety"},{"id":"2404.03233","title":"Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning","created":"2024-04-04","cited_by_count":48,"area":"safety"},{"id":"2302.07225","title":"Bounding Training Data Reconstruction in DP-SGD","created":"2023-02-14","cited_by_count":46,"area":"safety"},{"id":"2407.14937","title":"Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)","created":"2024-07-20","cited_by_count":46,"area":"safety"},{"id":"2409.01380","title":"Membership Inference Attacks Against In-Context Learning","created":"2024-09-02","cited_by_count":43,"area":"safety"},{"id":"2210.10750","title":"Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries","created":"2022-10-19","cited_by_count":40,"area":"safety"},{"id":"2402.10892","title":"Proving membership in LLM pretraining data via data watermarks","created":"2024-02-16","cited_by_count":40,"area":"safety"},{"id":"2305.17826","title":"NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models","created":"2023-05-28","cited_by_count":37,"area":"safety"},{"id":"2312.04095","title":"Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection","created":"2023-12-07","cited_by_count":37,"area":"safety"},{"id":"2106.11384","title":"Membership Inference on Word Embedding and Beyond","created":"2021-06-21","cited_by_count":35,"area":"safety"},{"id":"2211.00463","title":"Amplifying Membership Exposure via Data Poisoning","created":"2022-11-01","cited_by_count":34,"area":"safety"},{"id":"2111.07608","title":"Property Inference Attacks Against GANs","created":"2021-11-15","cited_by_count":33,"area":"safety"},{"id":"2206.05199","title":"Bayesian Estimation of Differential Privacy","created":"2022-06-10","cited_by_count":33,"area":"safety"},{"id":"2411.10512","title":"On the Privacy Risk of In-context Learning","created":"2024-11-15","cited_by_count":32,"area":"safety"},{"id":"2305.13257","title":"Watermarking Text Data on Large Language Models for Dataset Copyright","created":"2023-05-22","cited_by_count":27,"area":"safety"},{"id":"2210.09126","title":"Verifiable and Provably Secure Machine Unlearning","created":"2022-10-17","cited_by_count":25,"area":"safety"},{"id":"2202.03460","title":"Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning","created":"2022-02-07","cited_by_count":24,"area":"safety"},{"id":"2403.09562","title":"PreCurious: How Innocent Pre-Trained Language Models Turn into Privacy Traps","created":"2024-03-14","cited_by_count":23,"area":"safety"},{"id":"2503.06808","title":"Privacy Auditing of Large Language Models","created":"2025-03-09","cited_by_count":23,"area":"safety"},{"id":"2406.16565","title":"Noisy Neighbors: Efficient membership inference attacks against LLMs","created":"2024-06-24","cited_by_count":21,"area":"safety"},{"id":"2105.06209","title":"DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks","created":"2021-05-13","cited_by_count":19,"area":"safety"},{"id":"2306.03558","title":"Machine Unlearning: A Survey","created":"2023-06-06","cited_by_count":19,"area":"safety"},{"id":"2403.10557","title":"Second-Order Information Matters: Revisiting Machine Unlearning for Large Language Models","created":"2024-03-13","cited_by_count":18,"area":"safety"},{"id":"2407.17417","title":"Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?","created":"2024-07-24","cited_by_count":18,"area":"safety"},{"id":"2409.11844","title":"MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts","created":"2024-09-18","cited_by_count":18,"area":"safety"},{"id":"2209.08615","title":"Membership Inference Attacks and Generalization: A Causal Perspective","created":"2022-09-18","cited_by_count":17,"area":"safety"},{"id":"2302.12685","title":"Active Membership Inference Attack under Local Differential Privacy in Federated Learning","created":"2023-02-24","cited_by_count":17,"area":"safety"},{"id":"2402.09477","title":"PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining","created":"2024-02-12","cited_by_count":17,"area":"safety"},{"id":"2403.09914","title":"ProMark: Proactive Diffusion Watermarking for Causal Attribution","created":"2024-03-14","cited_by_count":17,"area":"safety"},{"id":"2210.11334","title":"Proof of Unlearning: Definitions and Instantiation","created":"2022-10-20","cited_by_count":15,"area":"safety"},{"id":"2307.10476","title":"What can we learn from Data Leakage and Unlearning for Law?","created":"2023-07-19","cited_by_count":15,"area":"safety"},{"id":"2406.06186","title":"A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks","created":"2024-06-10","cited_by_count":15,"area":"safety"},{"id":"2601.18076","title":"Comparison requires valid measurement: Rethinking attack success rate comparisons in AI red teaming","created":"2026-01-26","cited_by_count":15,"area":"safety"},{"id":"2403.17105","title":"Certified Machine Unlearning via Noisy Stochastic Gradient Descent","created":"2024-03-25","cited_by_count":14,"area":"safety"},{"id":"2405.16978","title":"OSLO: One-Shot Label-Only Membership Inference Attacks","created":"2024-05-27","cited_by_count":13,"area":"safety"},{"id":"2406.09325","title":"REVS: Unlearning Sensitive Information in Language Models via Rank Editing in the Vocabulary Space","created":"2024-06-13","cited_by_count":13,"area":"safety"},{"id":"2206.12401","title":"Debiasing Learning for Membership Inference Attacks Against Recommender Systems","created":"2022-06-24","cited_by_count":12,"area":"safety"},{"id":"2302.06676","title":"Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations","created":"2023-02-13","cited_by_count":12,"area":"safety"},{"id":"2310.02164","title":"A Survey of Graph Unlearning","created":"2023-08-23","cited_by_count":12,"area":"safety"},{"id":"2410.02159","title":"Mitigating Memorization In Language Models","created":"2024-10-03","cited_by_count":12,"area":"safety"},{"id":"2104.08273","title":"Membership Inference Attacks on Knowledge Graphs","created":"2021-04-16","cited_by_count":11,"area":"safety"},{"id":"2310.00222","title":"Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning","created":"2023-09-30","cited_by_count":11,"area":"safety"},{"id":"2510.19796","title":"Blackbox Model Provenance via Palimpsestic Membership Inference","created":"2025-10-22","cited_by_count":11,"area":"safety"},{"id":"2311.14056","title":"DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release","created":"2023-11-23","cited_by_count":10,"area":"safety"},{"id":"2406.14091","title":"Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models","created":"2024-06-20","cited_by_count":10,"area":"safety"},{"id":"2502.12658","title":"R.R.: Unveiling LLM Training Privacy through Recollection and Ranking","created":"2025-02-18","cited_by_count":10,"area":"safety"},{"id":"2404.02462","title":"A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability","created":"2024-04-03","cited_by_count":9,"area":"safety"},{"id":"2410.01276","title":"Deep Unlearn: Benchmarking Machine Unlearning for Image Classification","created":"2024-10-02","cited_by_count":9,"area":"safety"},{"id":"2412.08559","title":"Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning","created":"2024-12-11","cited_by_count":9,"area":"safety"},{"id":"2506.13009","title":"Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective","created":"2025-06-16","cited_by_count":9,"area":"safety"},{"id":"2205.06369","title":"How to Combine Membership-Inference Attacks on Multiple Updated Models","created":"2022-05-12","cited_by_count":8,"area":"safety"},{"id":"2305.12320","title":"Random Relabeling for Efficient Machine Unlearning","created":"2023-05-21","cited_by_count":7,"area":"safety"},{"id":"2405.07562","title":"GLiRA: Black-Box Membership Inference Attack via Knowledge Distillation","created":"2024-05-13","cited_by_count":7,"area":"safety"},{"id":"2502.15082","title":"UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning","created":"2025-02-20","cited_by_count":7,"area":"safety"},{"id":"2509.13755","title":"Scrub It Out! Erasing Sensitive Memorization in Code Language Models via Machine Unlearning","created":"2025-09-17","cited_by_count":7,"area":"safety"},{"id":"2106.00265","title":"A unified PAC-Bayesian framework for machine unlearning via information risk minimization","created":"2021-06-01","cited_by_count":6,"area":"safety"},{"id":"2111.01363","title":"Knowledge Cross-Distillation for Membership Privacy","created":"2021-11-02","cited_by_count":6,"area":"safety"},{"id":"2111.11869","title":"Machine unlearning via GAN","created":"2021-11-22","cited_by_count":6,"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":"2210.16023","title":"LegoNet: A Fast and Exact Unlearning Architecture","created":"2022-10-28","cited_by_count":6,"area":"safety"},{"id":"2403.02983","title":"Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks","created":"2024-03-05","cited_by_count":6,"area":"safety"},{"id":"2405.07018","title":"Shadow-Free Membership Inference Attacks: Recommender Systems Are More Vulnerable Than You Thought","created":"2024-05-11","cited_by_count":6,"area":"safety"},{"id":"2405.12604","title":"Tiny Refinements Elicit Resilience: Toward Efficient Prefix-Model Against LLM Red-Teaming","created":"2024-05-21","cited_by_count":6,"area":"safety"},{"id":"2408.17354","title":"Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage","created":"2024-08-30","cited_by_count":6,"area":"safety"},{"id":"2409.14200","title":"Data-centric NLP Backdoor Defense from the Lens of Memorization","created":"2024-09-21","cited_by_count":6,"area":"safety"},{"id":"2110.05524","title":"Generalization Techniques Empirically Outperform Differential Privacy against Membership Inference","created":"2021-10-11","cited_by_count":5,"area":"safety"},{"id":"2205.06469","title":"l-Leaks: Membership Inference Attacks with Logits","created":"2022-05-13","cited_by_count":5,"area":"safety"},{"id":"2310.13786","title":"Fundamental Limits of Membership Inference Attacks on Machine Learning Models","created":"2023-10-20","cited_by_count":5,"area":"safety"},{"id":"2501.08454","title":"Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack","created":"2025-01-14","cited_by_count":5,"area":"safety"}],"next_cursor":"100"}