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 1 of 4

1. CVPR 2024Attack To Defend: Exploiting Adversarial Attacks for Detecting Poisoned Models
2. CVPR 2024BrainWash: A Poisoning Attack to Forget in Continual Learning
3. CVPR 2024Data Poisoning based Backdoor Attacks to Contrastive Learning
4. CVPR 2024Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization
5. CVPR 2024Efficient Privacy-Preserving Visual Localization Using 3D Ray Clouds
6. CVPR 2024Improving Transferable Targeted Adversarial Attacks with Model Self-Enhancement
7. CVPR 2024LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning
8. CVPR 2024Nearest is Not Dearest: Towards Practical Defense against Quantization-conditioned Backdoor Attacks
9. CVPR 2024OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition
10. CVPR 2024Overload: Latency Attacks on Object Detection for Edge Devices
11. CVPR 2024PAD: Patch-Agnostic Defense against Adversarial Patch Attacks
12. CVPR 2024Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving
13. CVPR 2024Physical Backdoor: Towards Temperature-based Backdoor Attacks in the Physical World
14. CVPR 2024Privacy-Preserving Face Recognition Using Trainable Feature Subtraction
15. CVPR 2024Privacy-Preserving Optics for Enhancing Protection in Face De-Identification
16. CVPR 2024Re-thinking Data Availability Attacks Against Deep Neural Networks
17. CVPR 2024Selective Interpretable and Motion Consistent Privacy Attribute Obfuscation for Action Recognition
18. CVPR 2024Semantic-Aware Multi-Label Adversarial Attacks
19. CVPR 2024SimAC: A Simple Anti-Customization Method for Protecting Face Privacy against Text-to-Image Synthesis of Diffusion Models
20. CVPR 2024Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning
21. CVPR 2024Ungeneralizable Examples
22. CVPR 2024Validating Privacy-Preserving Face Recognition under a Minimum Assumption
23. ICLR 2024Adversarial Attacks on Fairness of Graph Neural Networks
24. ICLR 2024Be Careful What You Smooth For: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks
25. ICLR 2024Beyond Memorization: Violating Privacy via Inference with Large Language Models
26. ICLR 2024Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
27. ICLR 2024BRUSLEATTACK: A QUERY-EFFICIENT SCORE- BASED BLACK-BOX SPARSE ADVERSARIAL ATTACK
28. ICLR 2024Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
29. ICLR 2024Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks
30. ICLR 2024Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning
31. ICLR 2024Confidential-DPproof: Confidential Proof of Differentially Private Training
32. ICLR 2024Deceptive Fairness Attacks on Graphs via Meta Learning
33. ICLR 2024Demystifying Poisoning Backdoor Attacks from a Statistical Perspective
34. ICLR 2024Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
35. ICLR 2024DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer
36. ICLR 2024DP-SGD Without Clipping: The Lipschitz Neural Network Way
37. ICLR 2024Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios
38. ICLR 2024Enhancing Transferable Adversarial Attacks on Vision Transformers through Gradient Normalization Scaling and High-Frequency Adaptation
39. ICLR 2024FedInverse: Evaluating Privacy Leakage in Federated Learning
40. ICLR 2024FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
41. ICLR 2024Fixed-Budget Differentially Private Best Arm Identification
42. ICLR 2024GNNCert: Deterministic Certification of Graph Neural Networks against Adversarial Perturbations
43. ICLR 2024Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
44. ICLR 2024Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
45. ICLR 2024Improving LoRA in Privacy-preserving Federated Learning
46. ICLR 2024IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks
47. ICLR 2024Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models
48. ICLR 2024LabelDP-Pro: Learning with Label Differential Privacy via Projections
49. ICLR 2024Language Model Detectors Are Easily Optimized Against
50. ICLR 2024Language Model Inversion

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.