Large Audio-language Models (LAMs) have recently enabled powerful speech-based interactions by coupling audio encoders with Large Language Models (LLMs). However, the security of LAMs under adversarial attacks remains underexplored, especially through audio jailbreaks that craft malicious audio prompts to bypass alignment. Existing efforts primarily rely on converting text-based attacks into speech or applying shallow signal-level perturbations, overlooking the impact of human speech’s expressive variations on LAM alignment robustness. To address this gap, we propose StyleBreak, a novel style-aware audio jailbreak framework that systematically investigates how diverse human speech attributes affect LAM alignment robustness. Specifically, StyleBreak employs a two-stage style-aware transformation pipeline that perturbs both textual content and audio to control linguistic, paralinguistic, and extralinguistic attributes. Furthermore, we develop a query-adaptive policy network that automatically searches for adversarial styles to enhance the efficiency of LAM jailbreak exploration. Extensive evaluations demonstrate that LAMs exhibit critical vulnerabilities when exposed to diverse human speech attributes. Moreover, StyleBreak achieves substantial improvements in attack effectiveness and efficiency across multiple attack paradigms, highlighting the urgent need for more robust alignment in LAMs.

Graph Neural Networks (GNNs) have achieved significant success in anomaly detection across various domains. However, existing GNN approaches face notable challenges when applied to directed multigraphs where edge attributes serve as the sole source of information. This limitation complicates the learning of meaningful node representations and the accurate detection of anomalies, particularly in transaction networks. Moreover, existing methods typically suffer from dimensionality explosion and suboptimal use of structural and semantic information, leading to reduced detection performance. To address these challenges, we propose Feature Reconstruction for Anomaly Detection on Directed Multigraphs (FRAD-DM), a novel preprocessing framework tailored to enhance graph representations in node feature-absent scenarios. FRAD-DM employs advanced edge feature derivation techniques, temporal and structural subgraph analysis for node representation generation, and a reinforcement learning-based adaptive feature selection mechanism. This comprehensive framework optimizes feature spaces by balancing informativeness, task relevance, and computational efficiency, ensuring robust and scalable anomaly detection. Extensive experiments on real-world datasets under barely supervised settings demonstrate the effectiveness of FRAD-DM. By integrating it into various GNN architectures for node-level and edge-level anomaly detection tasks, negative-class F1-scores improve by 5.44% to 20.98%. These results highlight its capability to address the challenges of directed multigraphs in practical applications.