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

331 papers, page 2 of 7

51. ICLR 2026Noise Tolerance of Distributionally Robust Learning
52. ICLR 2026Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization
53. ICLR 2026Obfuscated Activations Bypass LLM Latent-Space Defenses
54. ICLR 2026On Discovering Algorithms for Adversarial Imitation Learning
55. ICLR 2026On the Bayes Inconsistency of Disagreement Discrepancy Surrogates
56. ICLR 2026On the Impact of the Utility in Semivalue-based Data Valuation
57. ICLR 2026On the Interaction of Compressibility and Adversarial Robustness
58. ICLR 2026ON THE ROLE OF IMPLICIT REGULARIZATION OF STOCHASTIC GRADIENT DESCENT IN GROUP ROBUSTNESS
59. ICLR 2026On the Tension Between Optimality and Adversarial Robustness in Policy Optimization
60. ICLR 2026Online Conformal Prediction with Adversarial Feedback via Regret Minimization
61. ICLR 2026Pay Less Attention to Function Words for Free Robustness of Vision-Language Models
62. ICLR 2026QUEST: A robust attention formulation using query-modulated spherical attention
63. ICLR 2026RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments
64. ICLR 2026Reducing information dependency does not cause training data privacy. Adversarially non-robust features do.
65. ICLR 2026Robust Adversarial Attacks Against Unknown Disturbance via Inverse Gradient Sample
66. ICLR 2026Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning
67. ICLR 2026Robust Deep Reinforcement Learning against Adversarial Behavior Manipulation
68. ICLR 2026Robust Federated Inference
69. ICLR 2026Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling
70. ICLR 2026Robust LLM Unlearning via Post Judgment and Multi-round Thinking
71. ICLR 2026Robust Spiking Neural Networks Against Adversarial Attacks
72. ICLR 2026Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses
73. ICLR 2026Robustness of Probabilistic Models to Low-Quality Data: A Multi-Perspective Analysis
74. ICLR 2026Sampling-aware Adversarial Attacks Against Large Language Models
75. ICLR 2026Set Representation Auxiliary Learning with Adversarial Encoding Perturbation and Optimization
76. ICLR 2026Stretching Beyond the Obvious: A Gradient-Free Framework to Unveil the Hidden Landscape of Visual Invariance
77. ICLR 2026Testing Most Influential Sets
78. ICLR 2026The Price of Robustness: Stable Classifiers Need Overparameterization
79. ICLR 2026The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology
80. ICLR 2026Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networks
81. ICLR 2026Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks
82. ICLR 2026Training Dynamics Impact Post-Training Quantization Robustness
83. ICLR 2026Transferable and Stealthy Adversarial Attacks on Large Vision-Language Models
84. ICLR 2026TriQDef: Disrupting Semantic and Gradient Alignment to Prevent Adversarial Patch Transferability in Quantized Neural Networks
85. ICLR 2026Tug-of-War No More: Harmonizing Accuracy and Robustness in Vision-Language Models via Stability-Aware Task Vector Merging
86. ICLR 2026Understanding and Improving Continuous LLM Adversarial Training via In-context Learning Theory
87. ICLR 2026Understanding Sensitivity of Differential Attention through the Lens of Adversarial Robustness
88. ICLR 2026Untraceable DeepFakes via Traceable Fingerprint Elimination
89. ICLR 2026VEAttack: Downstream-agnostic Vision Encoder Attack against Large Vision Language Models
90. ICLR 2026Watch your steps: Dormant Adversarial Behaviors that Activate upon LLM Finetuning
91. ICLR 2026When Flatness Does (Not) Guarantee Adversarial Robustness
92. ICLR 2026Why Adversarially Train Diffusion Models?
93. ICLR 2026Why Do Unlearnable Examples Work: A Novel Perspective of Mutual Information
94. ICLR 2026Zero-Sacrifice Persistent-Robustness Adversarial Defense for Pre-Trained Encoders
95. ICLR 2025$\sigma$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples
96. ICLR 2025ADBM: Adversarial Diffusion Bridge Model for Reliable Adversarial Purification
97. ICLR 2025Adversarial Attacks on Data Attribution
98. ICLR 2025Adversarial Latent Feature Augmentation for Fairness
99. ICLR 2025Adversarial Machine Unlearning
100. ICLR 2025Adversarial Mixup Unlearning

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.