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

53 papers, page 1 of 2

1. ICLR 2024Adversarial Attacks on Fairness of Graph Neural Networks
2. ICLR 2024Be Careful What You Smooth For: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks
3. ICLR 2024Beyond Memorization: Violating Privacy via Inference with Large Language Models
4. ICLR 2024Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
5. ICLR 2024BRUSLEATTACK: A QUERY-EFFICIENT SCORE- BASED BLACK-BOX SPARSE ADVERSARIAL ATTACK
6. ICLR 2024Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
7. ICLR 2024Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks
8. ICLR 2024Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning
9. ICLR 2024Confidential-DPproof: Confidential Proof of Differentially Private Training
10. ICLR 2024Deceptive Fairness Attacks on Graphs via Meta Learning
11. ICLR 2024Demystifying Poisoning Backdoor Attacks from a Statistical Perspective
12. ICLR 2024Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
13. ICLR 2024DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer
14. ICLR 2024DP-SGD Without Clipping: The Lipschitz Neural Network Way
15. ICLR 2024Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios
16. ICLR 2024Enhancing Transferable Adversarial Attacks on Vision Transformers through Gradient Normalization Scaling and High-Frequency Adaptation
17. ICLR 2024FedInverse: Evaluating Privacy Leakage in Federated Learning
18. ICLR 2024FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
19. ICLR 2024Fixed-Budget Differentially Private Best Arm Identification
20. ICLR 2024GNNCert: Deterministic Certification of Graph Neural Networks against Adversarial Perturbations
21. ICLR 2024Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
22. ICLR 2024Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
23. ICLR 2024Improving LoRA in Privacy-preserving Federated Learning
24. ICLR 2024IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks
25. ICLR 2024Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models
26. ICLR 2024LabelDP-Pro: Learning with Label Differential Privacy via Projections
27. ICLR 2024Language Model Detectors Are Easily Optimized Against
28. ICLR 2024Language Model Inversion
29. ICLR 2024Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
30. ICLR 2024Leave-one-out Distinguishability in Machine Learning
31. ICLR 2024Leveraging Optimization for Adaptive Attacks on Image Watermarks
32. ICLR 2024LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks
33. ICLR 2024On the Vulnerability of Adversarially Trained Models Against Two-faced Attacks
34. ICLR 2024One-shot Empirical Privacy Estimation for Federated Learning
35. ICLR 2024Optimal transport based adversarial patch to leverage large scale attack transferability
36. ICLR 2024Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection
37. ICLR 2024Privacy-Preserving In-Context Learning for Large Language Models
38. ICLR 2024PubDef: Defending Against Transfer Attacks From Public Models
39. ICLR 2024RAIN: Your Language Models Can Align Themselves without Finetuning
40. ICLR 2024Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
41. ICLR 2024Rethinking Label Poisoning for GNNs: Pitfalls and Attacks
42. ICLR 2024Rethinking Model Ensemble in Transfer-based Adversarial Attacks
43. ICLR 2024Sharpness-Aware Data Poisoning Attack
44. ICLR 2024Tangent Transformers for Composition,Privacy and Removal
45. ICLR 2024Teach LLMs to Phish: Stealing Private Information from Language Models
46. ICLR 2024Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game
47. ICLR 2024The False Promise of Imitating Proprietary Language Models
48. ICLR 2024The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
49. ICLR 2024Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks
50. ICLR 2024Towards Poisoning Fair 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.