Data to 7 Oct 2026

Stealing Hyperparameters in Machine Learning

arXiv 1802.05351 ↗First posted 14 Feb 2018cs.CR cs.LG stat.MLcited by 115

Not on the map. Why?

Cited by 25 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidentiality of the proprietary algorithms that the learner uses to learn them. In this work, we propose attacks on stealing the hyperparameters that are learned by a learner. We call our attacks hyperparameter stealing attacks. Our attacks are applicable to a variety of popular machine learning algorithms such as ridge regression, logistic regression, support vector machine, and neural network. We evaluate the effectiveness of our attacks both theoretically and empirically. For instance, we evaluate our attacks on Amazon Machine Learning. Our results demonstrate that our attacks can accurately steal hyperparameters. We also study countermeasures. Our results highlight the need for new defenses against our hyperparameter stealing attacks for certain machine learning algorithms.

Key facts. Cited by 115 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 36; 2022: 21; 2023: 22; 2024: 16; 2025: 13; 2026: 7.

115 papers.

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2021Membership Inference Attacks From First Principlescited by 839
2022Reconstructing Training Data from Trained Neural Networkscited by 129
2024PLeak: Prompt Leaking Attacks against Large Language Model Applicationscited by 113
2021Membership Inference Attack on Graph Neural Networkscited by 81
2021Node-Level Membership Inference Attacks Against Graph Neural Networkscited by 80
2021ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Modelscited by 79
2022I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defencescited by 75
2021Practical Blind Membership Inference Attack via Differential Comparisonscited by 73
2021EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learningcited by 68
2021Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecturecited by 63
2021Property Inference From Poisoningcited by 56
2021Model Stealing Attacks Against Inductive Graph Neural Networkscited by 50

38 more

2022SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learningcited by 47
2021Survey: Leakage and Privacy at Inference Timecited by 44
2021Formalizing and Estimating Distribution Inference Riskscited by 42
2021Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Aheadcited by 40
2021The Threat of Offensive AI to Organizationscited by 37
2021Quantifying and Mitigating Privacy Risks of Contrastive Learningcited by 35
2023A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutionscited by 34
2021Property Inference Attacks Against GANscited by 32
2022Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoderscited by 30
2021Practical Defences Against Model Inversion Attacks for Split Neural Networkscited by 29
2024Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trendscited by 26
2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
2022StolenEncoder: Stealing Pre-trained Encoders in Self-supervised Learningcited by 21
2023Model Extraction Attacks Revisitedcited by 21
2023Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscationcited by 20
2021Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexitycited by 19
2021"Adversarial Examples" for Proof-of-Learningcited by 19
2023Stealing the Decoding Algorithms of Language Modelscited by 19
2024AI Risk Management Should Incorporate Both Safety and Securitycited by 17
2021Membership Inference Attacks are Easier on Difficult Problemscited by 16
2021Teacher Model Fingerprinting Attacks Against Transfer Learningcited by 15
2022Poisoning Attacks and Defenses on Artificial Intelligence: A Surveycited by 15
2023Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challengescited by 14
2021Get a Model! Model Hijacking Attack Against Machine Learning Modelscited by 13
2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
2024Trustworthy Distributed AI Systems: Robustness, Privacy, and Governancecited by 12
2021Membership Inference Attacks on Knowledge Graphscited by 11
2022Federated Learning Attacks and Defenses: A Surveycited by 11
2021Dompteur: Taming Audio Adversarial Examplescited by 10
2022Semi-Leak: Membership Inference Attacks Against Semi-supervised Learningcited by 10
2023Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Thingscited by 10
2023Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attackscited by 10
2021Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspectivecited by 9
2023AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Modelscited by 9
2024Model Stealing for Any Low-Rank Language Modelcited by 9
2021Can one hear the shape of a neural network?: Snooping the GPU via Magnetic Side Channelcited by 8
2025Fingerprinting LLMs via Prompt Injectioncited by 8
2021Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIscited by 7

50 most cited of 115 All 115 (JSON)