Changting Lin

The rapid advancement of diffusion models has raised concerns about their misuse in generating deceptive visual content, motivating growing interest in AI-generated image detection. Many existing detection methods rely on image semantic features, but modern diffusion models are optimized to closely match the semantic structure of real images, reducing the effectiveness of semantic-based detection. An alternative line of work exploits differences revealed through diffusion reconstruction; however, most existing approaches treat reconstruction error as a static and passive metric, which can be sensitive to generators or post-processing, thereby limiting robustness. In this work, we propose Adversarial Diffusion Reconstruction Distance (ADRD), a detection framework that models diffusion reconstruction as a dynamic response process rather than a fixed descriptor. ADRD actively probes the reconstruction behavior by introducing perturbation in latent space and measuring how reconstruction deviations respond under identical perturbations. We empirically observe that real images typically exhibit larger and more variable reconstruction responses, while diffusion-generated images tend to show more stable reconstruction behavior, reflecting differences in their alignment with the diffusion model's implicit data manifold. By characterizing reconstruction sensitivity instead of absolute reconstruction error, ADRD provides a complementary perspective to existing reconstruction-based detectors. Experimental evaluations on multiple benchmarks suggest that reconstruction response under controlled perturbations constitutes a meaningful signal for diffusion-generated image detection. The code is available at https://github.com/ezell-chou/adrd.

Lightweight Large Language Models (LwLLMs) are reduced-parameter, optimized models designed to run efficiently on consumer-grade hardware, offering significant advantages in resource efficiency, cost-effectiveness, and data privacy. However, these models often struggle with limited inference and reasoning capabilities, which restrict their performance on complex tasks and limit their practical applicability. Moreover, existing prompt optimization methods typically rely on extensive manual effort or the meta-cognitive abilities of state-of-the-art LLMs, making them less effective for LwLLMs. To address these challenges, we introduce DeBoP, a new Direct Behavior Optimization Paradigm, original from the Chain-of-Thought (CoT) prompting technique. Unlike CoT Prompting, DeBoP is an automatic optimization method, which focuses on the optimization directly on the behavior of LwLLMs. In particular, DeBoP transforms the optimization of complex prompts into the optimization of discrete, quantifiable execution sequences using a gradient-free Monte Carlo Tree Search. We evaluate DeBoP on seven challenging tasks where state-of-the-art LLMs excel but LwLLMs generally underperform. Experimental results demonstrate that DeBoP significantly outperforms recent prompt optimization methods on most tasks. In particular, DeBoP-optimized LwLLMs surpass GPT-3.5 on most tasks while reducing computational time by approximately 60% compared to other automatic prompt optimization methods.

Safety alignment in large language models (LLMs) is achieved through fine-tuning mechanisms that regulate neuron activations to suppress harmful content. In this work, we propose a novel approach to induce disalignment by identifying and modifying the neurons responsible for safety constraints. Our method consists of three key steps: Neuron Activation Analysis, where we examine activation patterns in response to harmful and harmless prompts to detect neurons that are critical for distinguishing between harmful and harmless inputs; Similarity-Based Neuron Identification, which systematically locates the neurons responsible for safe alignment; and Neuron Relearning for Safety Removal, where we fine-tune these selected neurons to restore the model's ability to generate previously restricted responses. Experimental results demonstrate that our method effectively removes safety constraints with minimal fine-tuning, highlighting a critical vulnerability in current alignment techniques. Our findings underscore the need for robust defenses against adversarial fine-tuning attacks on LLMs.

Hongming Yang, Shi Lin, Jun Shao, Changting Lin, Donghai Zhu, Meng Han, Qinglei Kong. Findings of the Association for Computational Linguistics: ACL 2025. 2025.

A Cyber-Physical System (CPS) is a networked system with communication protocols and technologies that are susceptible to potential vulnerabilities and security threats. For example, an adversary may try to uncover the Source Location Privacy (SLP) of a target to perform an attack by analyzing network traffic in a random manner. However, this approach is usually inefficient. Considering the inefficiency of traditional random walk methods, we incorporate an active learning mechanism to equip with the backtracking framework to effectively distinguish between the fake and real network traffic with a limited number of learning samples. In this paper, we present an Active Learning-based Backtracking Attack (ALBA) which leverages muti-layer sampling technique to discover SLP. ALBA offers a more intelligent and faster approach in discovering the SLP. A new muti-layer sampling strategy is also proposed to enhance the performance of the backtracking attack. The effectiveness of our proposed ALBA is evaluated using the NSL-KDD dataset, which simulates the communication environment of CPS. The experimental results show that ALBA outperforms existing state-of-the-art schemes.

Critical functionality and huge influence of the hot trend/topic page (HTP) in microblogging sites have driven the creation of a new kind of underground se

Vertical Federated Learning (VFL) is a federated learning paradigm where multiple participants, who share the same set of samples but hold different features, jointly train machine learning models. Although VFL enables collaborative machine learning without sharing raw data, it is still susceptible to various privacy threats. In this paper, we conduct the first comprehensive survey of the state-of-the-art in privacy attacks and defenses in VFL. We provide taxonomies for both attacks and defenses, based on their characterizations, and discuss open challenges and future research directions. Specifically, our discussion is structured around the model's life cycle, by delving into the privacy threats encountered during different stages of machine learning and their corresponding countermeasures. This survey not only serves as a resource for the research community but also offers clear guidance and actionable insights for practitioners to safeguard data privacy throughout the model's life cycle.

Currently, the development of IoT firmware heavily depends on third-party components (TPCs) to improve development efficiency. Nevertheless, TPCs are not secure, and the vulnerabilities in TPCs will influence the security of IoT firmware. Existing works pay less attention to the vulnerabilities caused by TPCs, and we still lack a comprehensive understanding of the security impact of TPC vulnerability against firmware. To fill in the knowledge gap, we design and implement FirmSec , which leverages syntactical features and control-flow graph features to detect the TPCs in firmware, and then recognizes the corresponding vulnerabilities. Based on FirmSec , we present the first large-scale analysis of the security risks raised by TPCs on 34,136 firmware images. We successfully detect 584 TPCs and identify 128,757 vulnerabilities caused by 429 CVEs. Our in-depth analysis reveals the diversity of security risks in firmware and discovers some well-known vulnerabilities are still rooted in firmware. Besides, we explore the geographical distribution of vulnerable devices and confirm that the security situation of devices in different regions varies. Our analysis also indicates that vulnerabilities caused by TPCs in firmware keep growing with the boom of the IoT ecosystem. Further analysis shows 2,478 commercial firmware images have potentially violated GPL/AGPL licensing terms.