Adversarial Robustness papers, 2023–2026

Key findings

• Adversarial Robustness went from 313 papers in 2023 to 317 in 2026, roughly 1.0x in three years.
• NeurIPS publishes the most Adversarial Robustness work: 422 papers from 2023 to 2026, 33% of the four-venue total.
• The fastest-growing overlap is with Alignment and Safety: papers tagged with both rose from 13 in 2023 to 56 in 2026.
• Among the 5 topics under Security and Trust, it ranks #1 by 2026 paper count; the fastest grower in the group, Watermarking, is up 9.3x versus 1.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Little Robustness Is All You Need: Leveraging Predictions for Contextual Optimization
• ICML 2026Bias in Zeroth-Order Normal Estimation for Decision-Based Attacks
• ICLR 2026CERTIFIED VS. EMPIRICAL ADVERSARIAL ROBUSTNESS VIA HYBRID CONVOLUTIONS WITH ATTENTION STOCHASTICITY
• CVPR 2026A Combination of Noise and Bilateral Filters Achieve Supralinear and
• NeurIPS 2025A Unified Approach to Submodular Maximization Under Noise
• ICML 2025Adversarial Perturbations Are Formed by Iteratively Learning Linear Combinations of the Right Singular Vectors of the Adversarial Jacobian
• ICLR 2025$\sigma$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples
• CVPR 2025A Unified, Resilient, and Explainable Adversarial Patch Detector
• NeurIPS 2024A StrongREJECT for Empty Jailbreaks
• ICML 2024Adversarial Attacks on Combinatorial Multi-Armed Bandits
• ICLR 2024An LLM can Fool Itself: A Prompt-Based Adversarial Attack
• CVPR 2024Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization
• NeurIPS 2023A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)
• ICML 2023A Law of Robustness beyond Isoperimetry
• ICLR 2023A law of adversarial risk, interpolation, and label noise
• CVPR 2023Adversarial Normalization: I Can Visualize Everything (ICE)
• NeurIPS 2026A Regularization-Based Approach to Public Belief State Search for Adversarial Games
• ICML 2026CaliDist: Calibrating Large Language Models via Behavioral Robustness to Distraction
• ICLR 2026Conformal Robustness Control: A New Strategy for Robust Decision
• CVPR 2026A Provable Energy-Guided Test-Time Defense Boosting Adversarial Robustness of Large

All Adversarial Robustness papers

422 papers, page 8 of 9

351. NeurIPS 2023Designing Robust Transformers using Robust Kernel Density Estimation
352. NeurIPS 2023DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
353. NeurIPS 2023Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
354. NeurIPS 2023Distribution Learnability and Robustness
355. NeurIPS 2023Dynamic Regret of Adversarial Linear Mixture MDPs
356. NeurIPS 2023Dynamically Masked Discriminator for GANs
357. NeurIPS 2023Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
358. NeurIPS 2023Efficient Testable Learning of Halfspaces with Adversarial Label Noise
359. NeurIPS 2023Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization
360. NeurIPS 2023Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples
361. NeurIPS 2023Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
362. NeurIPS 2023Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
363. NeurIPS 2023Enhancing Adversarial Robustness via Score-Based Optimization
364. NeurIPS 2023Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts
365. NeurIPS 2023Fantastic Robustness Measures: The Secrets of Robust Generalization
366. NeurIPS 2023Federated Linear Bandits with Finite Adversarial Actions
367. NeurIPS 2023Hierarchical Randomized Smoothing
368. NeurIPS 2023HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
369. NeurIPS 2023Improving Adversarial Robustness via Information Bottleneck Distillation
370. NeurIPS 2023Improving Adversarial Transferability via Intermediate-level Perturbation Decay
371. NeurIPS 2023Improving Robustness with Adaptive Weight Decay
372. NeurIPS 2023LEACE: Perfect linear concept erasure in closed form
373. NeurIPS 2023Learning Provably Robust Estimators for Inverse Problems via Jittering
374. NeurIPS 2023Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis
375. NeurIPS 2023MADG: Margin-based Adversarial Learning for Domain Generalization
376. NeurIPS 2023Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness
377. NeurIPS 2023Meta-Learning Adversarial Bandit Algorithms
378. NeurIPS 2023Multi-scale Diffusion Denoised Smoothing
379. NeurIPS 2023Non-adversarial training of Neural SDEs with signature kernel scores
380. NeurIPS 2023On Evaluating Adversarial Robustness of Large Vision-Language Models
381. NeurIPS 2023On Private and Robust Bandits
382. NeurIPS 2023On Proper Learnability between Average- and Worst-case Robustness
383. NeurIPS 2023On the Adversarial Robustness of Out-of-distribution Generalization Models
384. NeurIPS 2023On the Robustness of Mechanism Design under Total Variation Distance
385. NeurIPS 2023On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks
386. NeurIPS 2023One Less Reason for Filter Pruning: Gaining Free Adversarial Robustness with Structured Grouped Kernel Pruning
387. NeurIPS 2023Optimal Transport Model Distributional Robustness
388. NeurIPS 2023PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization
389. NeurIPS 2023Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability
390. NeurIPS 2023Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More
391. NeurIPS 2023QuadAttac$K$: A Quadratic Programming Approach to Learning Ordered Top-$K$ Adversarial Attacks
392. NeurIPS 2023Rethinking the Backward Propagation for Adversarial Transferability
393. NeurIPS 2023Revisiting Adversarial Robustness Distillation from the Perspective of Robust Fairness
394. NeurIPS 2023Revisiting Visual Model Robustness: A Frequency Long-Tailed Distribution View
395. NeurIPS 2023Robust Data Valuation with Weighted Banzhaf Values
396. NeurIPS 2023Robust Lipschitz Bandits to Adversarial Corruptions
397. NeurIPS 2023Robust low-rank training via approximate orthonormal constraints
398. NeurIPS 2023Robust Matrix Sensing in the Semi-Random Model
399. NeurIPS 2023Robustness Guarantees for Adversarially Trained Neural Networks
400. NeurIPS 2023RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion

FAQ

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

1,291 in total: 313 in 2023, 334 in 2024, 327 in 2025, 317 in 2026.

How many Adversarial Robustness papers are at NeurIPS 2026?

NeurIPS 2026 has 81 Adversarial Robustness papers, versus 110 the year before.

How many Adversarial Robustness papers are at ICLR 2026?

ICLR 2026 has 94 Adversarial Robustness papers, versus 88 the year before.

How many Adversarial Robustness papers are at ICML 2026?

ICML 2026 has 93 Adversarial Robustness papers, versus 85 the year before.

How many Adversarial Robustness papers are at CVPR 2026?

CVPR 2026 has 49 Adversarial Robustness papers, versus 44 the year before.

Which conference publishes the most Adversarial Robustness papers?

NeurIPS, with 422 papers from 2023 to 2026.