Privacy Attacks papers, 2023–2026

Key findings

• Privacy Attacks went from 124 papers in 2023 to 219 in 2026, roughly 1.8x in three years.
• ICLR publishes the most Privacy Attacks work: 262 papers from 2023 to 2026, 38% of the four-venue total.
• The fastest-growing overlap is with Alignment and Safety: papers tagged with both rose from 2 in 2023 to 80 in 2026.
• Among the 5 topics under Federated and Privacy, it ranks #1 by 2026 paper count; the fastest grower in the group, Secure Aggregation, is up 1.8x versus 1.8x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Private Empirical Defense Against Privacy Audits
• ICML 2026A Statistical Framework for Analyzing Specification Resistance to Learnware-Inversion Risks
• ICLR 2026(Token-Level) \textbf{InfoRMIA}: Stronger Membership Inference and Privacy Assessment for LLMs
• CVPR 2026Out of Sight Out of Track Adversarial Attacks on Propagation-based
• NeurIPS 2025A Reliable Cryptographic Framework for Empirical Machine Unlearning Evaluation
• ICML 2025A Certified Unlearning Approach without Access to Source Data
• ICLR 2025Adversarial Attacks on Data Attribution
• CVPR 2025Adv-CPG: A Customized Portrait Generation Framework with Facial Adversarial Attacks
• NeurIPS 2024Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers
• ICML 2024Collective Certified Robustness against Graph Injection Attacks
• ICLR 2024Be Careful What You Smooth For: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks
• CVPR 2024Efficient Privacy-Preserving Visual Localization Using 3D Ray Clouds
• NeurIPS 2023Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
• ICML 2023Data Poisoning Attacks Against Multimodal Encoders
• ICLR 2023Adversarial Attacks on Adversarial Bandits
• CVPR 2023Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders
• NeurIPS 2026Adaptive Target-Charging with Privacy Filters and Individual Accounting
• ICML 2026How does Bayesian Sampling help Membership Inference Attacks?
• ICLR 2026Beyond Membership: Limitations of Add/Remove Adjacency in Differential Privacy
• CVPR 2026Revisiting Geometric Obfuscation with Dual Convergent Lines for Privacy-Preserving Image

All Privacy Attacks papers

180 papers, page 3 of 4

101. ICML 2024Provable Privacy with Non-Private Pre-Processing
102. ICML 2024Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation
103. ICML 2024Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration via Shift Reduction Lemmas
104. ICML 2024Rethinking DP-SGD in Discrete Domain: Exploring Logistic Distribution in the Realm of signSGD
105. ICML 2024Revisiting Character-level Adversarial Attacks for Language Models
106. ICML 2024Shifted Interpolation for Differential Privacy
107. ICML 2024SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding
108. ICML 2024Split-and-Denoise: Protect large language model inference with local differential privacy
109. ICML 2024The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks
110. ICML 2024The Privacy Power of Correlated Noise in Decentralized Learning
111. ICML 2024Toward Availability Attacks in 3D Point Clouds
112. ICML 2024Trained Random Forests Completely Reveal your Dataset
113. ICML 2024Transferable Facial Privacy Protection against Blind Face Restoration via Domain-Consistent Adversarial Obfuscation
114. ICML 2024Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy
115. ICML 2024Unveiling Privacy, Memorization, and Input Curvature Links
116. ICML 2024Verifying message-passing neural networks via topology-based bounds tightening
117. ICML 2024Watermark Stealing in Large Language Models
118. NeurIPS 2024A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
119. NeurIPS 2024AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks
120. NeurIPS 2024AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
121. NeurIPS 2024Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections
122. NeurIPS 2024Attack-Aware Noise Calibration for Differential Privacy
123. NeurIPS 2024Auditing Privacy Mechanisms via Label Inference Attacks
124. NeurIPS 2024Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs
125. NeurIPS 2024Certified Machine Unlearning via Noisy Stochastic Gradient Descent
126. NeurIPS 2024Continual Counting with Gradual Privacy Expiration
127. NeurIPS 2024Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature
128. NeurIPS 2024DAGER: Exact Gradient Inversion for Large Language Models
129. NeurIPS 2024Data Free Backdoor Attacks
130. NeurIPS 2024Deep Support Vectors
131. NeurIPS 2024Differentially Private Set Representations
132. NeurIPS 2024Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
133. NeurIPS 2024Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
134. NeurIPS 2024Efficient Adversarial Training in LLMs with Continuous Attacks
135. NeurIPS 2024Efficient Availability Attacks against Supervised and Contrastive Learning Simultaneously
136. NeurIPS 2024Faster Repeated Evasion Attacks in Tree Ensembles
137. NeurIPS 2024HEPrune: Fast Private Training of Deep Neural Networks With Encrypted Data Pruning
138. NeurIPS 2024HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning
139. NeurIPS 2024Improving Alignment and Robustness with Circuit Breakers
140. NeurIPS 2024Instance-Optimal Private Density Estimation in the Wasserstein Distance
141. NeurIPS 2024Instance-Specific Asymmetric Sensitivity in Differential Privacy
142. NeurIPS 2024Large Language Model Unlearning
143. NeurIPS 2024MALT Powers Up Adversarial Attacks
144. NeurIPS 2024Measuring Dejavu Memorization Efficiently
145. NeurIPS 2024Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration
146. NeurIPS 2024Membership Inference Attacks against Large Vision-Language Models
147. NeurIPS 2024MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models
148. NeurIPS 2024Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
149. NeurIPS 2024On Differentially Private Subspace Estimation in a Distribution-Free Setting
150. NeurIPS 2024On provable privacy vulnerabilities of graph representations

FAQ

How many Privacy Attacks papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

698 in total: 124 in 2023, 180 in 2024, 175 in 2025, 219 in 2026.

How many Privacy Attacks papers are at NeurIPS 2026?

NeurIPS 2026 has 55 Privacy Attacks papers, versus 45 the year before.

How many Privacy Attacks papers are at ICLR 2026?

ICLR 2026 has 102 Privacy Attacks papers, versus 83 the year before.

How many Privacy Attacks papers are at ICML 2026?

ICML 2026 has 38 Privacy Attacks papers, versus 29 the year before.

How many Privacy Attacks papers are at CVPR 2026?

CVPR 2026 has 24 Privacy Attacks papers, versus 18 the year before.

Which conference publishes the most Privacy Attacks papers?

ICLR, with 262 papers from 2023 to 2026.