Social Virtual Reality (VR) platforms provide immersive social experiences but also expose users to serious risks of online harassment. Existing safety measures are largely reactive, while proactive solutions that detect harassment behavior during an incident often depend on sensitive biometric data, raising privacy concerns. In this paper, we present HarassGuard, a vision-language model (VLM) based system that detects physical harassment in social VR using only visual input. We construct an IRB-approved harassment vision dataset, apply prompt engineering, and fine-tune VLMs to detect harassment behavior by considering contextual information in social VR. Experimental results demonstrate that HarassGuard achieves competitive performance compared to state-of-the-art baselines (i.e., LSTM/CNN, Transformer), reaching an accuracy of up to 88.09% in binary classification and 68.85% in multi-class classification. Notably, HarassGuard matches these baselines while using significantly fewer fine-tuning samples (200 vs. 1,115), offering unique advantages in contextual reasoning and privacy-preserving detection.

Software-Defined Networking (SDN) has aggressively grown in data center networks, telecommunication providers, and enterprises by virtue of its programmable and extensible control plane. Also, there have been many kinds of research on the security of SDN components along with the growth of SDN. Some of them have inspected network policy inconsistency problems that can severely cause network reliability and security issues in SDN. However, they do not consider whether a single network policy itself is corrupted during processing inside and between SDN components. In this paper, we thus focus on the question of how to automatically identify cases in which the SDN stack fails to prevent policy inconsistencies from arising among those components. We then present AudiSDN, an automated fuzz-testing framework designed to formulate test cases in which policy inconsistencies can arise inOpenFlownetworks, the most prevalent SDN protocol. To prove its feasibility, we applied AudiSDN to two widely used SDN controllers, Floodlight and ONOS, and uncovered three separate CVEs (Common Vulnerabilities and Exposures) that cause the network policy inconsistencies among SDN components. Furthermore, we investigate the design flaws that cause the inconsistencies in modern SDN components, suggesting specific validations to address such a serious but understudied pragmatic concern.

At the foundation of every network security architecture lies the premise that formulated network flow policies are reliably deployed and enforced by the network infrastructure. However, software-defined networks (SDNs) add a particular challenge to satisfying this premise, as for SDNs the flow pol-icy implementation spans multiple applications and abstraction layers across the SDN stack. In this paper, we focus on the question of how to automatically identify cases in which the SDN stack fails to prevent policy inconsistencies from arising among these components. This question is rather essential, as when such inconsistencies arise the implications to the security and reliability of the network are devastating. We present AudiSDN, an automated fuzz-testing framework designed to formulate test cases in which policy inconsistencies can arise in OpenFlow networks, the most prevalent SDN protocol used today. We also present results from applying AudiSDN to two widely used SDN controllers, Floodlight and ONOS. In fact, our test results have led to the filing of 3 separate CVE reports. We believe that the approach presented in this paper is applicable to the breadth of OpenFlow platforms used today, and that its broader usage will help to address a serious but yet understudied pragmatic concern.