Device driver isolation aims to protect kernels from faulty/malicious drivers, yet its security guarantees are not fully understood. Compartment Interface Vulnerabilities (CIVs), known in userspace applications, also impact driver isolation, but this area is underexplored. This paper surveys existing driver isolation frameworks, systematizes CIV classifications, and evaluates them in the driver isolation context. Our analysis reveals CIV prevalence under a baseline threat model, with large drivers exhibiting over 100 CIV instances and an average of 33 across the studied drivers. Enforcing additional security properties like CFI reduces average CIVs to approximately 28. This work offers insights into driver isolation security, CIV prevalence, and guidance for future systems.

Cybersickness remains a major challenge in virtual and mixed reality (VR/MR), yet existing methods primarily focus on predicting its onset without offering formal guarantees regarding its occurrence or effective mitigation. As VR/MR applications expand into safety-critical domains like healthcare, defense, verifiable safety assurances become essential to protect users from adverse physiological and psychological effects. This paper introduces a probabilistic verification framework leveraging Bayesian Networks (BN) to explicitly model the interactions among system parameters, human physiological responses, and cybersickness severity. Unlike deep learning approaches that lack interpretability and formal verification capabilities, the proposed BN model explicitly captures how environmental and system-level factors (e.g., luminance, spectral entropy, and image gradient complexity via HoG features) influence physiological responses (e.g., heart rate, reaction time, eye tracking), ultimately affecting cybersickness severity. By learning the joint probability distribution of these factors, our approach provides rigorous formal guarantees on cybersickness risk under specified operational conditions. If these guarantees are not met, automated adaptive adjustments are recommended to restore safe conditions. Experimental validation involving physiological and systemlevel data demonstrates that Bayesian Networks provide an interpretable and efficient framework, uniquely enabling formal probabilistic verification of cybersickness risks. This capability makes the proposed approach particularly suitable for designing and deploying VR/MR systems with explicitly verified safety constraints.

Ensuring the safety of neural network (NN) models in mixed reality (MR) systems is challenging due to adversarial manipulation of system parameters. We present PolySafe, which extends DeepPoly and Prover to validate safety of LSTM-based MR models. PolySafe unrolls temporal dependencies, introduces multi-plane abstractions for tighter bounds, and establishes probabilistic safety guarantees. It further includes an adaptive search that identifies minimal sets of critical parameters required to be constrained for defense. Evaluation on an MR engagement prediction model shows that PolySafe provides rigorous and actionable safety assurances for deployment.

Personal characteristics fundamentally shape virtual reality (VR) experiences, yet their integration into predictive models remains underexplored. This paper studies how to incorporate personal attributes (age, gender, prior VR experience) into Bayesian networks for cybersickness prediction via: (i) direct inclusion as root nodes, (ii) a two-stage model that learns a susceptibility score from personal attributes, and (iii) a stratified model. Using 26,040 samples from VR maze-navigation experiments, direct inclusion attains 82.53% accuracy (+14.02 percentage points over a 68.51% no-personal baseline). The two-stage approach reaches 77.32% while supporting cold-start prediction for unseen users, and stratified models achieve 73.62%. Using participant-level cross-validation to avoid subject leakage, we find that personalization consistently improves cybersickness prediction. These results argue that personal attributes should be treated as first-class signals in cybersickness models, with clear design trade-offs between maximal accuracy and deployability for unseen users, informing personalized VR systems and adaptive content delivery.

The extended Berkeley Packet Filter (eBPF) subsystem in Linux enables the extension of kernel functionality without modifying kernel code. In addition to its use in networking, eBPF provides the flexibility to perform tracing, add security checks, etc. To ensure that eBPF does not enable attackers to compromise the kernel, eBPF includes a verifier to validate every eBPF program before its execution, which includes checks that aim to prevent eBPF programs from modifying kernel memory due to memory errors. However, numerous vulnerabilities have been identified in the eBPF subsystem, including the verifier itself, which greatly violate expectations, leading to concerns about the threats of memory safety brought by eBPF. This paper presents the first systematic analysis of the memory safety risks inherent in the eBPF ecosystem, focusing on the challenges faced by the limitations of the eBPF verifier and current kernel defenses. We then evaluate proposed research mitigation strategies that apply isolation techniques, runtime checks, and static validation, highlighting their contributions and gaps. Our study finds that only 1.62-3.74% (37–85) of the memory operations in public eBPF programs cannot be proven memory safe comprehensively, motivating actionable insights towards enforcing comprehensive memory safety while accounting for performance and compatibility.

Heap memory errors remain a major source of software vulnerabilities. Existing memory safety defenses aim at protecting all objects, resulting in high performance cost and incomplete protection. Instead, we propose an approach that accurately identifies objects that are inexpensive to protect, and design a method to protect such objects comprehensively from all classes of memory errors. Towards this goal, we introduce the Uriah system that (1) statically identifies the heap objects whose accesses satisfy spatial and type safety, and (2) dynamically allocates such "safe" heap objects on an isolated safe heap to enforce a form of temporal safety while preserving spatial and type safety, called temporal allocated-type safety. Uriah finds 72.0% of heap allocation sites produce objects whose accesses always satisfy spatial and type safety in the SPEC CPU2006/2017 benchmarks, 5 server programs, and Firefox, which are then isolated on a safe heap using Uriah allocator to enforce temporal allocated-type safety. Uriah incurs only 2.9% and 2.6% runtime overhead, along with 9.3% and 5.4% memory overhead, on the SPEC CPU 2006 and 2017 benchmarks, while preventing exploits on all the heap memory errors in DARPA CGC binaries and 28 recent CVEs. Additionally, using existing defenses to enforce their memory safety guarantees on the unsafe heap objects significantly reduces overhead, enabling the protection of heap objects from all classes of memory errors at more practical costs.

Our work explores the challenge of developing secure kernel-driver interfaces designed to protect the kernel from isolated kernel extensions. We first analyze a range of possible attack vectors that exist in current isolation frameworks. Then, we suggest a new approach to building secure isolation boundaries centered around ideas that originate in safe operating systems: isolation of heaps and single ownership.

In this paper, we introduce VCSL (Video Copy Segment Localization), a new comprehensive segment-level annotated video copy dataset. Compared with existing copy detection datasets restricted by either video-level annotation or small-scale, VCSL not only has two orders of magnitude more segment-level labelled data, with 160k realistic video copy pairs containing more than 280k localized copied segment pairs, but also covers a variety of video categories and a wide range of video duration. All the copied segments inside each collected video pair are manually extracted and accompanied by precisely annotated starting and ending timestamps. Alongside the dataset, we also propose a novel evaluation protocol that better measures the prediction accuracy of copy overlapping segments between a video pair and shows improved adaptability in different scenarios. By benchmarking several baseline and state-of-the-art segment-level video copy detection methods with the proposed dataset and evaluation metric, we provide a comprehensive analysis that uncovers the strengths and weaknesses of current approaches, hoping to open up promising directions for future works. The VCSL dataset, metric and benchmark codes are all publicly available at https://github.com/alipay/vCSL.

With the explosive growth of web videos in recent years, large-scale Content-Based Video Retrieval (CBVR) becomes increasingly essential in video filtering, recommendation, and copyright protection. Segment-level CBVR (S-CBVR) locates the start and end time of similar segments in finer granularity, which is beneficial for user browsing efficiency and infringement detection especially in long video scenarios. The challenge of S-CBVR task is how to achieve high temporal alignment accuracy with efficient computation and low storage consumption. In this paper, we propose a Segment Similarity and Alignment Network (SSAN) in dealing with the challenge which is firstly trained end-to-end in S-CBVR. SSAN is based on two newly proposed modules in video retrieval: (1) An efficient Self-supervised Keyframe Extraction (SKE) module to reduce redundant frame features, (2) A robust Similarity Pattern Detection (SPD) module for temporal alignment. In comparison with uniform frame extraction, SKE not only saves feature storage and search time, but also introduces comparable accuracy and limited extra computation time. In terms of temporal alignment, SPD localizes similar segments with higher accuracy and efficiency than existing deep learning methods. Furthermore, we jointly train SSAN with SKE and SPD and achieve an end-to-end improvement. Meanwhile, the two key modules SKE and SPD can also be effectively inserted into other video retrieval pipelines and gain considerable performance improvements. Experimental results on public datasets show that SSAN can obtain higher alignment accuracy while saving storage and online query computational cost compared to existing methods.

Intrusion detection systems are a commonly deployed defense that examines network traffic, host operations, or both to detect attacks. However, more attacks bypass IDS defenses each year, and with the sophistication of attacks increasing as well, we must examine new perspectives for intrusion detection. Current intrusion detection systems focus on known attacks and/or vulnerabilities, limiting their ability to identify new attacks, and lack the visibility into all system components necessary to confirm attacks accurately, particularly programs. To change the landscape of intrusion detection, we propose that future IDSs track how attacks evolve across system layers by adapting the concept of attack graphs. Attack graphs were proposed to study how multi-stage attacks could be launched by exploiting known vulnerabilities. Instead of constructing attacks reactively, we propose to apply attack graphs proactively to detect sequences of events that fulfill the requirements for vulnerability exploitation. Using this insight, we examine how to generate modular attack graphs automatically that relate adversary accessibility for each component, called its attack surface, to flaws that provide adversaries with permissions that create threats, called attack states, and exploit operations from those threats, called attack actions. We evaluate the proposed approach by applying it to two case studies: (1) attacks on file retrieval, such as TOCTTOU attacks, and (2) attacks propagated among processes, such as attacks on Shell-shock vulnerabilities. In these case studies, we demonstrate how to leverage existing tools to compute attack graphs automatically and assess the effectiveness of these tools for building complete attack graphs. While we identify some research areas, we also find several reasons why attack graphs can provide a valuable foundation for improving future intrusion detection systems.