{"list":"other","total":99,"rows":[{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"2012.07805","title":"Extracting Training Data from Large Language Models","created":"2020-12-14","cited_by_count":2189},{"id":"1908.09635","title":"A Survey on Bias and Fairness in Machine Learning","created":"2019-08-23","cited_by_count":1917},{"id":"1906.08935","title":"Deep Leakage from Gradients","created":"2019-06-21","cited_by_count":1021},{"id":"1602.02697","title":"Practical Black-Box Attacks against Machine Learning","created":"2016-02-08","cited_by_count":903},{"id":"2112.03570","title":"Membership Inference Attacks From First Principles","created":"2021-12-07","cited_by_count":878},{"id":"2301.13188","title":"Extracting Training Data from Diffusion Models","created":"2023-01-30","cited_by_count":732},{"id":"2003.14053","title":"Inverting Gradients -- How easy is it to break privacy in federated learning?","created":"2020-03-31","cited_by_count":712},{"id":"1912.00049","title":"Square Attack: a query-efficient black-box adversarial attack via random search","created":"2019-11-29","cited_by_count":685},{"id":"2309.00770","title":"Bias and Fairness in Large Language Models: A Survey","created":"2023-09-02","cited_by_count":639},{"id":"1609.02943","title":"Stealing Machine Learning Models via Prediction APIs","created":"2016-09-09","cited_by_count":619},{"id":"2002.08347","title":"On Adaptive Attacks to Adversarial Example Defenses","created":"2020-02-19","cited_by_count":555},{"id":"1805.04049","title":"Exploiting Unintended Feature Leakage in Collaborative Learning","created":"2018-05-10","cited_by_count":520},{"id":"1806.01246","title":"ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models","created":"2018-06-04","cited_by_count":498},{"id":"2007.08745","title":"Backdoor Learning: A Survey","created":"2020-07-17","cited_by_count":426},{"id":"2007.14321","title":"Label-Only Membership Inference Attacks","created":"2020-07-28","cited_by_count":388},{"id":"1812.02766","title":"Knockoff Nets: Stealing Functionality of Black-Box Models","created":"2018-12-06","cited_by_count":292},{"id":"2302.00539","title":"Analyzing Leakage of Personally Identifiable Information in Language Models","created":"2023-02-01","cited_by_count":285},{"id":"1911.07135","title":"The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks","created":"2019-11-17","cited_by_count":246},{"id":"2407.04295","title":"Jailbreak Attacks and Defenses Against Large Language Models: A Survey","created":"2024-07-05","cited_by_count":230},{"id":"2303.11408","title":"Stable Bias: Analyzing Societal Representations in Diffusion Models","created":"2023-03-20","cited_by_count":226},{"id":"2402.07841","title":"Do Membership Inference Attacks Work on Large Language Models?","created":"2024-02-12","cited_by_count":195},{"id":"2005.03823","title":"Blind Backdoors in Deep Learning Models","created":"2020-05-08","cited_by_count":182},{"id":"1709.07886","title":"Machine Learning Models that Remember Too Much","created":"2017-09-22","cited_by_count":169},{"id":"1805.02628","title":"PRADA: Protecting against DNN Model Stealing Attacks","created":"2018-05-07","cited_by_count":165},{"id":"2403.06634","title":"Stealing Part of a Production Language Model","created":"2024-03-11","cited_by_count":159},{"id":"2007.07646","title":"A Survey of Privacy Attacks in Machine Learning","created":"2020-07-15","cited_by_count":146},{"id":"1910.12366","title":"Thieves on Sesame Street! Model Extraction of BERT-based APIs","created":"2019-10-27","cited_by_count":139},{"id":"2206.10469","title":"The Privacy Onion Effect: Memorization is Relative","created":"2022-06-21","cited_by_count":136},{"id":"2302.01316","title":"Are Diffusion Models Vulnerable to Membership Inference Attacks?","created":"2023-02-02","cited_by_count":136},{"id":"2106.10807","title":"Adversarial Examples Make Strong Poisons","created":"2021-06-21","cited_by_count":119},{"id":"1802.05351","title":"Stealing Hyperparameters in Machine Learning","created":"2018-02-14","cited_by_count":118},{"id":"2006.03463","title":"Sponge Examples: Energy-Latency Attacks on Neural Networks","created":"2020-06-05","cited_by_count":111},{"id":"2204.00032","title":"Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets","created":"2022-03-31","cited_by_count":101},{"id":"2205.01992","title":"Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning","created":"2022-05-04","cited_by_count":94},{"id":"2308.01508","title":"Circumventing Concept Erasure Methods For Text-to-Image Generative Models","created":"2023-08-03","cited_by_count":85},{"id":"2206.08451","title":"I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences","created":"2022-06-16","cited_by_count":75},{"id":"2104.09667","title":"Manipulating SGD with Data Ordering Attacks","created":"2021-04-19","cited_by_count":74},{"id":"2101.11073","title":"Property Inference From Poisoning","created":"2021-01-26","cited_by_count":60},{"id":"2004.07401","title":"Poisoning Attacks on Algorithmic Fairness","created":"2020-04-15","cited_by_count":59},{"id":"1808.04761","title":"Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures","created":"2018-08-14","cited_by_count":57},{"id":"2003.13746","title":"DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain of Bit Flips","created":"2020-03-30","cited_by_count":56},{"id":"2309.16952","title":"Leveraging Optimization for Adaptive Attacks on Image Watermarks","created":"2023-09-29","cited_by_count":52},{"id":"2212.10986","title":"SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning","created":"2022-12-21","cited_by_count":51},{"id":"2209.01292","title":"Are Attribute Inference Attacks Just Imputation?","created":"2022-09-02","cited_by_count":49},{"id":"2006.07267","title":"Leakage of Dataset Properties in Multi-Party Machine Learning","created":"2020-06-12","cited_by_count":47},{"id":"2012.08723","title":"Exacerbating Algorithmic Bias through Fairness Attacks","created":"2020-12-16","cited_by_count":47},{"id":"2202.08187","title":"Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey","created":"2022-02-16","cited_by_count":47},{"id":"2308.04699","title":"GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization","created":"2023-08-09","cited_by_count":45},{"id":"2310.17626","title":"A Survey on Transferability of Adversarial Examples across Deep Neural Networks","created":"2023-10-26","cited_by_count":44},{"id":"2109.06024","title":"Formalizing and Estimating Distribution Inference Risks","created":"2021-09-13","cited_by_count":43},{"id":"2210.10750","title":"Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries","created":"2022-10-19","cited_by_count":40},{"id":"2303.12233","title":"LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation","created":"2023-03-21","cited_by_count":36},{"id":"2211.00463","title":"Amplifying Membership Exposure via Data Poisoning","created":"2022-11-01","cited_by_count":34},{"id":"2111.07608","title":"Property Inference Attacks Against GANs","created":"2021-11-15","cited_by_count":33},{"id":"2403.00871","title":"Teach LLMs to Phish: Stealing Private Information from Language Models","created":"2024-03-01","cited_by_count":32},{"id":"2103.07101","title":"On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models","created":"2021-03-12","cited_by_count":31},{"id":"2208.12348","title":"SNAP: Efficient Extraction of Private Properties with Poisoning","created":"2022-08-25","cited_by_count":31},{"id":"2404.01231","title":"Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models","created":"2024-04-01","cited_by_count":30},{"id":"2203.08147","title":"Energy-Latency Attacks via Sponge Poisoning","created":"2022-03-14","cited_by_count":29},{"id":"2306.14123","title":"Privacy and Fairness in Federated Learning: on the Perspective of Trade-off","created":"2023-06-25","cited_by_count":26},{"id":"2411.01705","title":"Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors","created":"2024-11-03","cited_by_count":23},{"id":"2404.00473","title":"Privacy Backdoors: Stealing Data with Corrupted Pretrained Models","created":"2024-03-30","cited_by_count":22},{"id":"2309.17310","title":"Leave-one-out Distinguishability in Machine Learning","created":"2023-09-29","cited_by_count":20},{"id":"2105.12049","title":"Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs","created":"2021-05-25","cited_by_count":18},{"id":"2212.07591","title":"Dissecting Distribution Inference","created":"2022-12-15","cited_by_count":15},{"id":"2411.10242","title":"Measuring Non-Adversarial Reproduction of Training Data in Large Language Models","created":"2024-11-15","cited_by_count":14},{"id":"2403.10073","title":"Revisiting Adversarial Training under Long-Tailed Distributions","created":"2024-03-15","cited_by_count":13},{"id":"2207.01991","title":"Conflicting Interactions Among Protection Mechanisms for Machine Learning Models","created":"2022-07-05","cited_by_count":12},{"id":"2303.06280","title":"Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks","created":"2023-03-11","cited_by_count":11},{"id":"2402.06957","title":"Architectural Neural Backdoors from First Principles","created":"2024-02-10","cited_by_count":11},{"id":"2011.03006","title":"Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA","created":"2020-11-05","cited_by_count":10},{"id":"2305.03888","title":"Energy-Latency Attacks to On-Device Neural Networks via Sponge Poisoning","created":"2023-05-06","cited_by_count":10},{"id":"2211.10209","title":"On the Alignment of Group Fairness with Attribute Privacy","created":"2022-11-18","cited_by_count":9},{"id":"2105.10909","title":"Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs","created":"2021-05-23","cited_by_count":8},{"id":"2303.11643","title":"Manipulating Transfer Learning for Property Inference","created":"2023-03-21","cited_by_count":8},{"id":"2506.11415","title":"Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs","created":"2025-06-13","cited_by_count":8},{"id":"2407.01919","title":"A Method to Facilitate Membership Inference Attacks in Deep Learning Models","created":"2024-07-02","cited_by_count":7},{"id":"2209.06529","title":"Data Privacy and Trustworthy Machine Learning","created":"2022-09-14","cited_by_count":6},{"id":"2302.01740","title":"SoK: A Systematic Evaluation of Backdoor Trigger Characteristics in Image Classification","created":"2023-02-03","cited_by_count":6},{"id":"2310.17534","title":"SoK: Pitfalls in Evaluating Black-Box Attacks","created":"2023-10-26","cited_by_count":6},{"id":"2312.04542","title":"SoK: Unintended Interactions among Machine Learning Defenses and Risks","created":"2023-12-07","cited_by_count":6},{"id":"2402.18607","title":"Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective","created":"2024-02-28","cited_by_count":6},{"id":"2003.00865","title":"Towards Backdoor Attacks and Defense in Robust Machine Learning Models","created":"2020-02-25","cited_by_count":5},{"id":"2308.01469","title":"VertexSerum: Poisoning Graph Neural Networks for Link Inference","created":"2023-08-02","cited_by_count":5},{"id":"2503.06461","title":"Long-tailed Adversarial Training with Self-Distillation","created":"2025-03-09","cited_by_count":5},{"id":"2506.07888","title":"SoK: Data Reconstruction Attacks Against Machine Learning Models: Definition, Metrics, and Benchmark","created":"2025-06-09","cited_by_count":5},{"id":"1902.03020","title":"On the security relevance of weights in deep learning","created":"2019-02-08","cited_by_count":4},{"id":"2105.09453","title":"DeepStrike: Remotely-Guided Fault Injection Attacks on DNN Accelerator in Cloud-FPGA","created":"2021-05-20","cited_by_count":4},{"id":"2312.10578","title":"SAME: Sample Reconstruction against Model Extraction Attacks","created":"2023-12-17","cited_by_count":4},{"id":"2501.14414","title":"SoK: What Makes Private Learning Unfair?","created":"2025-01-24","cited_by_count":3},{"id":"2503.16266","title":"From Head to Tail: Efficient Black-box Model Inversion Attack via Long-tailed Learning","created":"2025-03-20","cited_by_count":3},{"id":"2505.18323","title":"Architectural Backdoors for Within-Batch Data Stealing and Model Inference Manipulation","created":"2025-05-23","cited_by_count":3},{"id":"2411.09776","title":"Combining Machine Learning Defenses without Conflicts","created":"2024-11-14","cited_by_count":2},{"id":"2502.18518","title":"Swallowing the Poison Pills: Insights from Vulnerability Disparity Among LLMs","created":"2025-02-23","cited_by_count":2},{"id":"2503.01686","title":"\\textsc{Perseus}: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes","created":"2025-03-03","cited_by_count":2},{"id":"2402.14937","title":"SoK: Analyzing Adversarial Examples: A Framework to Study Adversary Knowledge","created":"2024-02-22","cited_by_count":1},{"id":"2506.18870","title":"Amplifying Machine Learning Attacks Through Strategic Compositions","created":"2025-06-23","cited_by_count":1},{"id":"2510.07968","title":"One Risk Down, Another Up: Cross-Risk Interactions Induced by LLM Defenses","created":"2025-10-09","cited_by_count":1}],"next_cursor":null}