Ying He

Yulin Chen, Haoran Li, Yuan Sui et al.

Large language models (LLMs) have demonstrated impressive performance and have come to dominate the field of natural language processing (NLP) across various tasks. However, due to their strong instruction-following capabilities and inability to distinguish between instructions and data content, LLMs are vulnerable to prompt injection attacks. These attacks manipulate LLMs into deviating from the original input instructions and executing maliciously injected instructions within data content, such as web documents retrieved from search engines. Existing defense methods, including prompt-engineering and fine-tuning approaches, typically instruct models to follow the original input instructions while suppressing their tendencies to execute injected instructions. However, our experiments reveal that suppressing instruction-following tendencies is challenging. Through analyzing failure cases, we observe that although LLMs tend to respond to any recognized instructions, they are aware of which specific instructions they are executing and can correctly reference them within the original prompt. Motivated by these findings, we propose a novel defense method that leverages, rather than suppresses, the instruction-following abilities of LLMs. Our approach prompts LLMs to generate responses that include both answers and their corresponding instruction references. Based on these references, we filter out answers not associated with the original input instructions. Comprehensive experiments demonstrate that our method outperforms prompt-engineering baselines and achieves performance comparable to fine-tuning methods, reducing the attack success rate (ASR) to 0 percent in some scenarios. Moreover, our approach has minimal impact on overall utility.

Cheng Wang, Yue Liu, Baolong Bi et al.

Large Reasoning Models (LRMs) have exhibited extraordinary prowess in tasks like mathematics and coding, leveraging their advanced reasoning capabilities. Nevertheless, as these capabilities progress, significant concerns regarding their vulnerabilities and safety have arisen, which can pose challenges to their deployment and application in real-world settings. This paper presents a comprehensive survey of LRMs, meticulously exploring and summarizing the newly emerged safety risks, attacks, and defense strategies. By organizing these elements into a detailed taxonomy, this work aims to offer a clear and structured understanding of the current safety landscape of LRMs, facilitating future research and development to enhance the security and reliability of these powerful models.

Yuan Sui, Yufei He, Tri Cao et al.

Large Language Models (LLMs) struggle with high computational time and error propagation during inference time, especially for complex tasks like math, puzzles, or coding requiring multi-step thinking. While existing reasoning models with chain-of-thoughts (CoT) can enable LLMs to do step-wise analysis and reflection, they often face the issue of wasting computation on less productive solutions and fail to make progress during inference time. In this paper, we propose Meta-Reasoner, a new framework to enable LLMs ``Think about how to think'', i.e., optimize the inference compute by adjusting strategies on how to reason during inference time. Inspired by dual-process theory, our method decouples the high-level strategy generation (e.g., backtracking, switching approaches, or restarting) from stepwise CoT generation via a lightweight progress report. The strategy module only consider the summarized version from the previous CoTs to propose new strategies accordingly. We employ the contextual multi-armed bandits (CMABs) for this module to iteratively evaluate the previous reasoning states and dynamically adjust the strategy to avoid reasoning get stuck in less productive paths during inference. Evaluations on math problems (e.g., Game-of-24, TheoremQA) and scientific problems (e.g., SciBench) demonstrate that our method improves performance by 9-12\% over previous SOTA methods while reducing inference time by 28-35\%. This approach also generalizes to other domains like creative writing, demonstrating its versatility for diverse reasoning-intensive problems using LLMs.

Keyi Liu, Weidong Yang, Ben Fei et al.

Self-supervised learning (SSL) for point cloud pre-training has become a cornerstone for many 3D vision tasks, enabling effective learning from large-scale unannotated data. At the scene level, existing SSL methods often incorporate volume rendering into the pre-training framework, using RGB-D images as reconstruction signals to facilitate cross-modal learning. This strategy promotes alignment between 2D and 3D modalities and enables the model to benefit from rich visual cues in the RGB-D inputs. However, these approaches are limited by their reliance on implicit scene representations and high memory demands. Furthermore, since their reconstruction objectives are applied only in 2D space, they often fail to capture underlying 3D geometric structures. To address these challenges, we propose Gaussian2Scene, a novel scene-level SSL framework that leverages the efficiency and explicit nature of 3D Gaussian Splatting (3DGS) for pre-training. The use of 3DGS not only alleviates the computational burden associated with volume rendering but also supports direct 3D scene reconstruction, thereby enhancing the geometric understanding of the backbone network. Our approach follows a progressive two-stage training strategy. In the first stage, a dual-branch masked autoencoder learns both 2D and 3D scene representations. In the second stage, we initialize training with reconstructed point clouds and further supervise learning using the geometric locations of Gaussian primitives and rendered RGB images. This process reinforces both geometric and cross-modal learning. We demonstrate the effectiveness of Gaussian2Scene across several downstream 3D object detection tasks, showing consistent improvements over existing pre-training methods.

Allan Cook, Michael Robinson, Mohamed Amine Ferrag et al.

The synergy between the cloud and the IoT has emerged largely due to the cloud having attributes which directly benefit the IoT and enable its continued growth. IoT adopting Cloud services has brought new security challenges. In this book chapter, we pursue two main goals: 1) to analyse the different components of Cloud computing and the IoT and 2) to present security and privacy problems that these systems face. We thoroughly investigate current security and privacy preservation solutions that exist in this area, with an eye on the Industrial Internet of Things, discuss open issues and propose future directions

Mark Evans, Leandros A. Maglaras, Ying He et al.

There continue to be numerous breaches publicised pertaining to cyber security despite security practices being applied within industry for many years. This article is intended to be the first in a number of articles as research into cyber security assurance processes. This article is compiled based on current research related to cyber security assurance and the impact of the human element on it. The objective of this work is to identify elements of cyber security that would benefit from further research and development based on the literature review findings. The results outlined in this article present a need for the cyber security field to look in to established industry areas to benefit from effective practices such as human reliability assessment, along with improved methods of validation such as statistical quality control in order to obtain true assurance. The article proposes the development of a framework that will be based upon defined and repeatable quantification, specifically relating to the range of human aspect tasks that provide, or are intended not to negatively affect cyber security posture.