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

262 papers, page 5 of 6

201. ICLR 2024Enhancing Transferable Adversarial Attacks on Vision Transformers through Gradient Normalization Scaling and High-Frequency Adaptation
202. ICLR 2024FedInverse: Evaluating Privacy Leakage in Federated Learning
203. ICLR 2024FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
204. ICLR 2024Fixed-Budget Differentially Private Best Arm Identification
205. ICLR 2024GNNCert: Deterministic Certification of Graph Neural Networks against Adversarial Perturbations
206. ICLR 2024Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
207. ICLR 2024Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
208. ICLR 2024Improving LoRA in Privacy-preserving Federated Learning
209. ICLR 2024IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks
210. ICLR 2024Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models
211. ICLR 2024LabelDP-Pro: Learning with Label Differential Privacy via Projections
212. ICLR 2024Language Model Detectors Are Easily Optimized Against
213. ICLR 2024Language Model Inversion
214. ICLR 2024Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
215. ICLR 2024Leave-one-out Distinguishability in Machine Learning
216. ICLR 2024Leveraging Optimization for Adaptive Attacks on Image Watermarks
217. ICLR 2024LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks
218. ICLR 2024On the Vulnerability of Adversarially Trained Models Against Two-faced Attacks
219. ICLR 2024One-shot Empirical Privacy Estimation for Federated Learning
220. ICLR 2024Optimal transport based adversarial patch to leverage large scale attack transferability
221. ICLR 2024Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection
222. ICLR 2024Privacy-Preserving In-Context Learning for Large Language Models
223. ICLR 2024PubDef: Defending Against Transfer Attacks From Public Models
224. ICLR 2024RAIN: Your Language Models Can Align Themselves without Finetuning
225. ICLR 2024Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
226. ICLR 2024Rethinking Label Poisoning for GNNs: Pitfalls and Attacks
227. ICLR 2024Rethinking Model Ensemble in Transfer-based Adversarial Attacks
228. ICLR 2024Sharpness-Aware Data Poisoning Attack
229. ICLR 2024Tangent Transformers for Composition,Privacy and Removal
230. ICLR 2024Teach LLMs to Phish: Stealing Private Information from Language Models
231. ICLR 2024Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game
232. ICLR 2024The False Promise of Imitating Proprietary Language Models
233. ICLR 2024The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
234. ICLR 2024Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks
235. ICLR 2024Towards Poisoning Fair Representations
236. ICLR 2024Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks
237. ICLR 2024Universal Backdoor Attacks
238. ICLR 2024You Only Query Once: An Efficient Label-Only Membership Inference Attack
239. ICLR 2023A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy
240. ICLR 2023Adversarial Attacks on Adversarial Bandits
241. ICLR 2023Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries
242. ICLR 2023CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning
243. ICLR 2023Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
244. ICLR 2023Differentially Private $L_2$-Heavy Hitters in the Sliding Window Model
245. ICLR 2023Disparate Impact in Differential Privacy from Gradient Misalignment
246. ICLR 2023Distributed Differential Privacy in Multi-Armed Bandits
247. ICLR 2023Easy Differentially Private Linear Regression
248. ICLR 2023Effective passive membership inference attacks in federated learning against overparameterized models
249. ICLR 2023Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
250. ICLR 2023Extracting Robust Models with Uncertain Examples

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.