New pages: two continuously updated paper lists

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We have set up two new pages that track the research literature our group follows closely. Both lists are harvested from arXiv and refreshed automatically every week.

Adversarial Example Papers (Carlini list continuation): a continuation of Nicholas Carlini’s well-known “(A Complete) List of All Adversarial Example Papers”, which stopped updating in September 2025. Our replication covers arXiv submissions since then, selected by a classifier trained on the original 13,697-entry list.

Cumulative number of adversarial example papers on arXiv

Physical AI Security Papers: a curated list of arXiv papers on the security (attacks and defenses) of AI-driven cyber-physical systems: autonomous driving, drones/UAV, robotics, and embodied AI. The list goes back to 2013 and is categorized by target platform.

Physical AI security papers per year by target category

Both pages are linked from the Projects menu. Comments and pointers to papers we may have missed are welcome.

A paper got accepted!

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We are thrilled to announce that our conference paper entitled “Artistic Adversarial Examples: Graffiti-Based Adversarial Attacks on Traffic Sign Recognition” has been accepted for publication in The 28th International Conference on Information and Communications Security (ICICS 2026). This work is a fruit of our international collaboration with Politecnico di Milano. Congratulations to Zhuge-kun and kudos to the entire team!

In this work, we propose artistic adversarial examples — adversarial patches crafted to look like ordinary street graffiti so that they blend naturally into urban scenes. Combining a graffiti-trained generative model with a two-stage optimization that couples white-box latent optimization and black-box placement refinement, our attack degrades traffic sign detection while keeping the perturbations visually plausible. Extensive experiments across digital, simulated, and physical settings show that the generated graffiti substantially disrupts traffic sign recognition, achieving up to a 72.8% attack success rate with an average confidence drop of 65.8%.

Zhenghao Michele Zhuge, Go Tsuruoka, Zhihe Zhang, Stefano Longari, Lachlan Moore, Stefano Zanero, and Tatsuya Mori, "Artistic Adversarial Examples: Graffiti-Based Adversarial Attacks on Traffic Sign Recognition." In Proc. of The 28th International Conference on Information and Communications Security (ICICS 2026), Fukui, Japan, Oct 2026.

Guest talk by Prof. Zhiqiang Lin (Ohio State University)

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On June 24, 2026, we were honored to host Prof. Zhiqiang Lin (Distinguished Professor of Engineering, and Director of the Institute for Cybersecurity and Digital Trust, The Ohio State University) at our laboratory on the Nishi-Waseda campus. Prof. Lin gave a research talk followed by an open discussion with our students and invited researchers.

Title: Validated Exploits, Not LLM Claims: Runtime-Grounded Security Agents for AI-Generated Code

Abstract: AI coding agents are rapidly changing how software is produced, but they also create a new assurance problem: generated code can be deployed faster than humans can review it. This talk examines how large language models can be used not merely to flag suspicious code, but to construct evidence-backed security findings for full-stack web applications. Prof. Lin presented BuckAI, an LLM-agent pipeline for web vulnerability discovery that combines code reasoning, static analysis, automated deployment, exploit generation, and runtime validation. The central design principle is that LLMs should propose hypotheses, while validators and runtime oracles determine whether those hypotheses correspond to real, exploitable behavior. The talk covered the challenges of building such a system — the lack of runnable benchmarks, the difficulty of deploying diverse web applications, and the unreliability of LLMs as final security judges — and concluded with lessons for building trustworthy security agents and open research directions in agentic AI for software security.

We thank Prof. Lin for the stimulating talk and the lively discussions with our group, and we look forward to future collaborations.