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Yangyan Li

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3 papers

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TMLR Journal 2026 Journal Article

Involuntary Jailbreak: On Self-Prompting Attacks

Yangyang Guo
Yangyan Li
Mohan Kankanhalli

In this study, we disclose a worrying new vulnerability in Large Language Models (LLMs), which we term involuntary jailbreak. Unlike existing jailbreak attacks, this weakness is distinct in that it does not involve a specific attack objective, such as generating instructions for building a bomb. Prior attack methods predominantly target localized components of the LLM guardrail. In contrast, involuntary jailbreaks may potentially compromise the global guardrail structure, which our method reveals to be surprisingly fragile. We merely employ a single universal prompt to achieve this goal. In particular, we instruct LLMs to generate several questions (self-prompting) that would typically be rejected, along with their corresponding in-depth responses (rather than a refusal). Remarkably, this simple prompt strategy consistently jailbreaks almost all leading LLMs tested, such as Claude Opus 4.1, Grok 4, Gemini 2.5 Pro, and GPT 4.1. With its wide targeting scope and near-universal effectiveness, this vulnerability makes existing jailbreak attacks seem less necessary until it is patched. More importantly, we hope this problem can motivate researchers and practitioners to rethink and re-evaluate the robustness of LLM guardrails and contribute to stronger safety alignment in the future.

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NeurIPS Conference 2018 Conference Paper

PointCNN: Convolution On X-Transformed Points

Yangyan Li
Rui Bu
Mingchao Sun
Wei Wu
Xinhan Di
Baoquan Chen

We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e. g. images). However, point cloud are irregular and unordered, thus a direct convolving of kernels against the features associated with the points will result in deserting the shape information while being variant to the orders. To address these problems, we propose to learn a X-transformation from the input points, which is used for simultaneously weighting the input features associated with the points and permuting them into latent potentially canonical order. Then element-wise product and sum operations of typical convolution operator are applied on the X-transformed features. The proposed method is a generalization of typical CNNs into learning features from point cloud, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.

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NeurIPS Conference 2016 Conference Paper

FPNN: Field Probing Neural Networks for 3D Data

Yangyan Li
Soeren Pirk
Hao Su
Charles Qi
Leonidas Guibas

Building discriminative representations for 3D data has been an important task in computer graphics and computer vision research. Convolutional Neural Networks (CNNs) have shown to operate on 2D images with great success for a variety of tasks. Lifting convolution operators to 3D (3DCNNs) seems like a plausible and promising next step. Unfortunately, the computational complexity of 3D CNNs grows cubically with respect to voxel resolution. Moreover, since most 3D geometry representations are boundary based, occupied regions do not increase proportionately with the size of the discretization, resulting in wasted computation. In this work, we represent 3D spaces as volumetric fields, and propose a novel design that employs field probing filters to efficiently extract features from them. Each field probing filter is a set of probing points -- sensors that perceive the space. Our learning algorithm optimizes not only the weights associated with the probing points, but also their locations, which deforms the shape of the probing filters and adaptively distributes them in 3D space. The optimized probing points sense the 3D space "intelligently", rather than operating blindly over the entire domain. We show that field probing is significantly more efficient than 3DCNNs, while providing state-of-the-art performance, on classification tasks for 3D object recognition benchmark datasets.

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