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

1,290 papers, page 5 of 26

201. ICML 2026Provably Efficient Policy-Reward Co-Pretraining for Adversarial Imitation Learning
202. ICML 2026PSMix: Robust Point Cloud Recognition through Spectral Domain Mixing
203. ICML 2026RADAR: Defending RAG Dynamically against Retrieval Corruption
204. ICML 2026Regret Minimization With a Crowd of Awakening Experts
205. ICML 2026Reward Under Attack: Analyzing the Robustness and Hackability of Process Reward Models
206. ICML 2026Ripple Perturbations Through Structure: Likelihood-Constrained Adversarial Attacks on Heterogeneous Tabular Data
207. ICML 2026Robust Bayesian Optimisation with Unbounded Corruptions
208. ICML 2026Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions
209. ICML 2026Robust Vision-Language Models via Manifold-Adversarial Adapters
210. ICML 2026Robustness of Mixtures of Experts to Feature Noise
211. ICML 2026Safety-Efficacy Trade Off: Robustness against Data-Poisoning
212. ICML 2026Scalable GANs with Transformers
213. ICML 2026Security--Fidelity Tradeoffs: The Hidden Cost of Prompt Injection Defense
214. ICML 2026Segment Anything with Robust Uncertainty-Accuracy Correlation
215. ICML 2026Self-Calibrated Consistency can Fight Back for Adversarial Robustness in Vision-Language Models
216. ICML 2026Semantic Router: On the Feasibility of Hijacking MLLMs via a Single Adversarial Perturbation
217. ICML 2026Ski Rental with Distributional Predictions of Unknown Quality
218. ICML 2026SORA: Free Second-Order Attacks in Fast Adversarial Training
219. ICML 2026SS-TPT: Stability and Suitability-Guided Test-Time Prompt Tuning for Adversarially Robust Vision-Language Models
220. ICML 2026Statistically Undetectable Backdoors in Deep Neural Networks
221. ICML 2026Steering Beyond the Support: Adversarial Training on Unsupervised Jailbroken Activation Simulation
222. ICML 2026Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data
223. ICML 2026Suppress and Diversify: Refining Robust Pathways for Corruption Robustness
224. ICML 2026SWE-ABS: Adversarial Benchmark Strengthening Exposes Inflated Success Rates on Test-based Benchmark
225. ICML 2026The Latent Guardian: Defending Collaborative Perception via Feature-Level Consistency Verification
226. ICML 2026Toward Understanding Adversarial Distillation: Why Robust Teachers Fail
227. ICML 2026Towards Fine-Grained Robustness: Attention-Guided Test-Time Prompt Tuning for Vision-Language Models
228. ICML 2026Towards Optimal Robustness in Learning-Augmented Paging
229. ICML 2026Towards Understanding Generalization of Federated Adversarial Learning: Perspective of Algorithmic Stability
230. ICML 2026Training with Honeypots: Reshaping How LLMs Fail Under Adversarial Attacks
231. ICML 2026Training-Free Adversarial Robustness in Computational MRI
232. ICML 2026Two Modalities Are Better Than One: Efficient Adversarial Purification via Multimodal Diffusion Models
233. ICML 2026Unifying Adversarial Robustness and Training Across Text Scoring Models
234. ICML 2026When Can You Poison Rewards? A Tight Characterization of Reward Poisoning in Linear MDPs
235. ICML 2026You Don't Protect if You Don't Expect: Breaking the Key Assumption behind CLIP's Test-Time Defenses
236. NeurIPS 2026A Little Robustness Is All You Need: Leveraging Predictions for Contextual Optimization
237. NeurIPS 2026A Margin Perspective on LoRA: Robustness to Catastrophic Forgetting and Adapter Merging (MaLoRA)
238. NeurIPS 2026A Regularization-Based Approach to Public Belief State Search for Adversarial Games
239. NeurIPS 2026A Retained-Signal Interface for LLM Watermark Robustness under Paraphrase
240. NeurIPS 2026A Simple Class-Agnostic Approach to Enhance Fair Adversarial Training
241. NeurIPS 2026A Solver-Efficient Neural Adversarial Attack on Subgraph Matching Models
242. NeurIPS 2026Adversarial Corpus Selection to Attack Subgraph Matching based Graph Retrieval
243. NeurIPS 2026Adversarial Group Fairness in Contextual Bandits: When Robust is Not Fair
244. NeurIPS 2026AI Models Can Provably Hide Arbitrary Capabilities
245. NeurIPS 2026AnchorRep: Defending LLMs Against Cross-Model Adversarial Transfer via Representation Repulsion
246. NeurIPS 2026Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
247. NeurIPS 2026BayesAT: Bayes-Guided Progressive Distillation for Semi-Supervised Adversarial Training
248. NeurIPS 2026Benchmarking Sensor-Fault Robustness in Forecasting
249. NeurIPS 2026Beyond Correctness: Robustness-Driven Evolutionary Self-Training for Large Language Models
250. NeurIPS 2026Beyond Ground Truth: Evaluating Non-Verifiable Reasoning in LLMs through Moral Robustness

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.