8 accepted papers
Random subspace method has wide security applications such as providing certified defenses against adversarial and backdoor attacks, and building robustly aligned LLM against jailbreaking attacks. However, the explanation of random subspace methods lacks sufficient exploration. Existing state-of-the…
By providing a standardized interface for LLM agents to interact with external tools, the Model Context Protocol (MCP) is quickly becoming a cornerstone of the modern autonomous agent ecosystem. However, it creates novel attack surfaces due to untrusted external tools. While prior work has focused o
We introduce **Native Parallel Reasoner (NPR)**, a teacher-free framework that enables Large Language Models (LLMs) to self-evolve genuine parallel reasoning capabilities. NPR transforms the model from sequential emulation to native parallel cognition through three key innovations: 1) a **self-disti…
Convolutional Neural Networks (CNNs) are extensively utilized for image classification. However, their high complexity hinders deployment on resource-constrained edge devices. Although existing pruning schemes could compress CNNs, they often rely on complete datasets and fail to account for the fact…
An image encoder pre-trained by self-supervised learning can be used as a general-purpose feature extractor to build downstream classifiers for various downstream tasks. However, many studies showed that an attacker can embed a trojan into an encoder such that multiple downstream classifiers built b…
Document-level relation extraction (DocRE) aims to identify relationships between entities within a document. Due to the vast number of entity pairs, fully annotating all fact triplets is challenging, resulting in datasets with numerous false negative samples. Recently, self-training-based methods h…
Different from a unimodal model whose input is from a single modality the input (called multi-modal input) of a multi-modal model is from multiple modalities such as image 3D points audio text etc. Similar to unimodal models many existing studies show that a multi-modal model is also vulnerable to a…
Convolutional neural networks (CNNs) have been extensively used in image classification. However, it is still challenging to directly deploy them on edge devices because of high computation requirements. To solve this problem, many pruning schemes for neural networks have been proposed. However, the…