While autonomous driving systems and intelligent transportation infrastructures become increasingly software-defined and network-connected, ensuring their cybersecurity has become a critical component of traffic safety. Large language models (LLMs) have recently shown promise in automating aspects of penetration testing, yet most existing approaches remain limited to simple, single-step exploits. They struggle to handle complex, multi-stage vulnerabilities that demand precise coordination, contextual reasoning, and knowledge reuse. This is particularly problematic in safety-critical domains, such as autonomous vehicles, where subtle software flaws can cascade across interdependent subsystems. In this work, we present CurriculumPT, a novel LLM-based penetration testing framework specifically designed for the security of intelligent systems. CurriculumPT combines curriculum learning and a multi-agent system to enable LLM agents to progressively acquire and apply exploitation skills across common vulnerabilities and exposures-based tasks. Through a structured progression from simple to complex vulnerabilities, agents build and refine an experience knowledge base that supports generalization to new attack surfaces without requiring model fine-tuning. We evaluate CurriculumPT on 15 real-world vulnerabilities scenarios and demonstrate that it outperforms three state-of-the-art baselines by up to 18 percentage points in exploit success rate, while achieving superior efficiency in execution time and resource usage. Our results confirm that CurriculumPT is capable of autonomous, scalable penetration testing and knowledge transfer, laying the groundwork for intelligent security auditing of modern autonomous driving systems and other cyberphysical transportation platforms.

With the rapid development of wireless communication technology and intelligent mobile devices, unmanned aerial vehicle (UAV) cluster is becoming increasingly popular in both civilian and military applications. Recently, a swarm intelligence-based UAV cluster study, aiming to enable efficient and autonomous collaboration, has drawn lots of interest. However, new security problems may be introduced with such swarm intelligence. In this work, we perform the first detailed security analysis to a kind of flocking-based UAV cluster with 5 policies, an upgrade version of the well-known Boids model. Targeting a realistic threat in a source-to-destination flying task, we design a data spoofing strategy and further perform complete vulnerability analysis. We reveal that such design and implementation are highly vulnerable. After breaking through the authentication of ad hoc on-demand distance vector (AODV) routing protocol by rushing attack, an attacker can masquerade as the first-arrival UAV within a specific scope of destination and generate data spoofing of arrival status to the following UAVs, so as to interfere with their normal flying paths of destination arrival and cause unexpected arrival delays amid urgent tasks. Experiments with detailed analysis from the 5-UAV cluster to the 10-UAV cluster are conducted to show specific feature composition-based attack effect and corresponding average delay. We also discuss promising defense suggestions leveraging the insights from our analysis.

Recent advances in deep learning (DL) have brought tremendous gains in signal modulation classification. However, DL-based classifiers lack transparency and interpretability, which raises concern about model's reliability and hinders the wide deployment in real-word applications. While explainable methods have recently emerged, little has been done to explain the DL-based signal modulation classifiers. In this work, we propose a novel model-agnostic explainer, Model-Agnostic Signal modulation classification Explainer (MASE), which provides explanations for the predictions of black-box modulation classifiers. With the subsequence-based signal interpretable representation and in-distribution local signal sampling, MASE learns a local linear surrogate model to derive a class activation vector, which assigns importance values to the timesteps of signal instance. Besides, the constellation-based explanation visualization is adopted to spotlight the important signal features relevant to model prediction. We furthermore propose the first generic quantitative explanation evaluation framework for signal modulation classification to automatically measure the faithfulness, sensitivity, robustness, and efficiency of explanations. Extensive experiments are conducted on two real-world datasets with four black-box signal modulation classifiers. The quantitative results indicate MASE outperforms two state-of-the-art methods with 44.7% improvement in faithfulness, 30.6% improvement in robustness, and 44.1% decrease in sensitivity. Through qualitative visualizations, we further demonstrate the explanations of MASE are more human interpretable and provide better understanding into the reliability of black-box model decisions.

Pedestrian detection plays a crucial role in autonomous driving by identifying the position, size, orientation, and dynamic features of pedestrians in images or videos, assisting autonomous vehicles in making better decisions and controls. It’s worth noting that the performance of pedestrian detection models largely depends on the quality and diversity of available training data. Current datasets for autonomous driving have limitations in terms of diversity, scale, and quality. In recent years, numerous studies have proposed the use of data augmentation strategies to expand the coverage of datasets, aiming to maximize the utilization of existing training data. However, these data augmentation methods often overlook the diversity of data scenarios. To overcome this challenge, in this paper, we propose a more comprehensive method for data augmentation, based on image descriptions and diffusion models. This method aims to cover a wider range of scene variations, including different weather conditions and lighting situations. We have designed a classifier to select data samples for augmentation, followed by extracting visual features based on image captions and converting them into high-level semantic information as textual descriptions for the corresponding samples. Finally, we utilize diffusion models to generate new variants. Additionally, we have designed three modification patterns to increase diversity in aspects such as weather conditions, lighting, and pedestrian poses within the data. We conducted extensive experiments on the KITTI dataset and in real-world environments, demonstrating that our proposed method significantly enhances the performance of pedestrian detection models in complex scenarios. This meticulous consideration of data augmentation will notably enhance the applicability and robustness of pedestrian detection models in actual autonomous driving scenarios.

With the development of the Internet, user comments produced an unprecedented impact on information acquisition, goods purchase, and other aspects. For example, the user comments can quickly render a topic the focus of discussion in social networks. It can promote the sales of goods in e-commerce, and it influences the ratings of books, movies, or albums. Among these network applications and services, “astroturfing,” a kind of online suspicious behavior, can generate abnormal, damaging, and even illegal behaviors in cyberspace that mislead public perception and bring a bad effect on Internet users and society. Hence, the manner of detecting and combating astroturfing behavior has become highly urgent, attracting interest from researchers both from information technology and sociology. In the current paper, we restudy it mainly from the perspective of information technology, summarize the latest research findings of astroturfing detection, analyze the astroturfing feature, classify the machine learning-based detection methods and evaluation criteria, and introduce the main applications. Different from the previous surveys, we also discuss the new future directions of astroturfing detection, such as cross-domain astroturfing detection and user privacy protection.

Robust reinforcement learning (RL) has been a challenging problem due to the gap between simulation and the real world. Existing efforts typically address the robust RL problem by solving a max-min problem. The main idea is to maximize the cumulative reward under the worst-possible perturbations. However, the worst-case optimization either leads to overly conservative solutions or unstable training process, which further affects the policy robustness and generalization performance. In this paper, we tackle this problem from both formulation definition and algorithm design. First, we formulate the robust RL as a max-expectation optimization problem, where the goal is to find an optimal policy under both the worst cases and the non-worst cases. Then, we propose a novel framework DRRL to solve the max-expectation optimization. Given our definition of the feasible tasks, a task generation and sequencing mechanism is introduced to dynamically output tasks at appropriate difficulty level for the current policy. With these progressive tasks, DRRL realizes dynamic multi-task learning to improve the policy robustness and the training stability. Finally, extensive experiments demonstrate that the proposed method exhibits significant performance on the unmanned CarRacing game and multiple high-dimensional MuJoCo environments.