Zhao Liu

Shenao Wang, Yanjie Zhao, Zhao Liu et al.

LLMs are rapidly transitioning from research prototypes to core components in production systems across industries such as finance and healthcare. These deployments rely on a growing ecosystem of open-source frameworks and components, collectively forming the LLM supply chain. However, the increasing complexity of this stack introduces critical security risks that remain underexplored. In this work, we present the first systematic and large-scale empirical study of vulnerabilities in the LLM supply chain, analyzing 529 real-world vulnerabilities spanning 77 widely adopted repositories across 12 lifecycle stages. Our findings reveal that the disclosed vulnerabilities are heavily concentrated in the application layer and model integration layer. Among these, 18.5% of the vulnerabilities are LLM-specific, arising from unique architectural and workflow characteristics, such as improper handling of critical resources like model files, prompt templates, and datasets, as well as generative output validation errors. To understand the real-world impact, we examine 63,243 publicly exposed LLM services and find that 45.6% are affected by at least one remotely exploitable vulnerability, over 70% of which are critical or high severity. By correlating these vulnerabilities with their potential exploit scenarios in the wild, we observed that these issues can lead to serious security consequences, including model tampering, sensitive dataset exposure, and unauthorized GPU resource abuse. Based on our findings, we distill 5 actionable insights that can guide engineering teams in auditing and securing LLM services. Our work offers a data-driven foundation for securing the LLM supply chain and highlights urgent directions for both industry and future research.

Deheng Ye, Guibin Chen, Wen Zhang et al.

MOBA games, e.g., Honor of Kings, League of Legends, and Dota 2, pose grand challenges to AI systems such as multi-agent, enormous state-action space, complex action control, etc. Developing AI for playing MOBA games has raised much attention accordingly. However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i.e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes. As a result, full MOBA games without restrictions are far from being mastered by any existing AI system. In this paper, we propose a MOBA AI learning paradigm that methodologically enables playing full MOBA games with deep reinforcement learning. Specifically, we develop a combination of novel and existing learning techniques, including curriculum self-play learning, policy distillation, off-policy adaption, multi-head value estimation, and Monte-Carlo tree-search, in training and playing a large pool of heroes, meanwhile addressing the scalability issue skillfully. Tested on Honor of Kings, a popular MOBA game, we show how to build superhuman AI agents that can defeat top esports players. The superiority of our AI is demonstrated by the first large-scale performance test of MOBA AI agent in the literature.

Liwei Wang, Yu-Chin Chan, Faez Ahmed et al.

Metamaterials are emerging as a new paradigmatic material system to render unprecedented and tailorable properties for a wide variety of engineering applications. However, the inverse design of metamaterial and its multiscale system is challenging due to high-dimensional topological design space, multiple local optima, and high computational cost. To address these hurdles, we propose a novel data-driven metamaterial design framework based on deep generative modeling. A variational autoencoder (VAE) and a regressor for property prediction are simultaneously trained on a large metamaterial database to map complex microstructures into a low-dimensional, continuous, and organized latent space. We show in this study that the latent space of VAE provides a distance metric to measure shape similarity, enable interpolation between microstructures and encode meaningful patterns of variation in geometries and properties. Based on these insights, systematic data-driven methods are proposed for the design of microstructure, graded family, and multiscale system. For microstructure design, the tuning of mechanical properties and complex manipulations of microstructures are easily achieved by simple vector operations in the latent space. The vector operation is further extended to generate metamaterial families with a controlled gradation of mechanical properties by searching on a constructed graph model. For multiscale metamaterial systems design, a diverse set of microstructures can be rapidly generated using VAE for target properties at different locations and then assembled by an efficient graph-based optimization method to ensure compatibility between adjacent microstructures. We demonstrate our framework by designing both functionally graded and heterogeneous metamaterial systems that achieve desired distortion behaviors.