2026-06-09

FDM: A Framework for Decision-making to build ML-based Malware detection systems

第一作者: Tadiwa Vhito · 方向: AI 安全

06/05 12:21

恶意软件检测机器学习决策框架配置选择隐藏英文摘要

Selecting appropriate machine learning (ML) configurations for malware detection is a complex, multi-criteria problem. Model choice, feature engineering, and update mechanisms must jointly satisfy operational constraints that vary across deployment contexts. This paper proposes the Framework for Decision-making (FDM) to build ML-based malware detection systems. The FDM formalises this selection process using the Weighted Configuration Compatibility Score (WCCS), a multi-criteria scoring function mapping five operational parameters (platform constraint, resource budget, response latency, update frequency, and detection sensitivity) to ranked recommendations across nine configuration dimensions. To validate the framework, four experiments were conducted on three datasets (a private Windows API dataset, the public Malimg image benchmark, and an Android static API dataset). Key results...

论文介绍 针对基于机器学习的恶意软件检测系统配置复杂性问题，提出决策框架FDM。核心方法是通过加权配置兼容性分数（WCCS），将五个操作参数映射到九个配置维度的排名推荐，以优化模型选择、特征工程和更新机制。意义在于为部署上下文提供多标准决策支持，提升ML恶意软件检测系统的适用性和效能。

On the Incentive Compatibility of Block Propagation in Bitcoin

第一作者: Fumichika Maeda · 方向: 系统安全

比特币区块传播激励兼容性挖矿奖励隐藏英文摘要

Abstract:Bitcoin is permissionless and does not rely on any central administrator, which gives it strong censorship resistance. At the same time, it is important to incentivize miners to behave in ways that align with the interests of the system as a whole. This paper asks whether miners are individually incentivized to propagate blocks, one of the most fundamental processes in Bitcoin. Miners collectively maintain the blockchain by generating blocks and disseminating them across the network. If miners have an incentive not to propagate some blocks, this would indicate a fundamental flaw in Bitcoin's incentive design. Although prior work has studied how propagation delays affect forks and mining rewards, it has not fully characterized miners' incentives to improve block propagation under different tie-breaking rules. To address this gap, we derive analytical reward expressions for each...

论文介绍 该研究探讨比特币矿工是否有动力去传播区块这一核心过程。在比特币这种无许可系统中，激励矿工行为符合整体利益至关重要。论文分析了在不同平局打破规则下，矿工改进区块传播的激励情况，并推导了相关的分析性奖励表达式，旨在识别潜在的激励设计缺陷。

AMD-FCG: An Enhanced Function Call Graph Dataset with Integrated Topological Features for Malware Detection and Classification

第一作者: Parthajit Borah · 方向: 系统安全

恶意软件检测函数调用图数据集拓扑特征隐藏英文摘要

Abstract:As malware illustrates a complex structure and behavior, detection of these has been a significant challenge in the domain of cybersecurity along with related services in daily life. So, it becomes crucial to have a reliable and adaptive solution to address the issue. Among the several detection methods developed over the years, one of the most reliable ones is studying and analyzing the structural and behavioral patterns of malware. These patterns of sophisticated malware can be obtained with the help of Function Call Graphs (FCGs). However, to effectively cover numerous groups of families of malware, it is required to have a sufficiently large dataset for the system to operate on. In order to ensure accuracy and robustness of the system, the dataset should comprise samples of different malwares and a benign application for secure execution of the detection process. This...

论文介绍 该论文提出AMD-FCG，一个用于恶意软件检测与分类的增强型函数调用图数据集。研究指出，有效的恶意软件检测需要分析其结构行为模式，而现有函数调用图数据集在规模和特征丰富性上存在不足。新数据集整合了拓扑特征，旨在提升检测系统的准确性和鲁棒性。

What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media

第一作者: Zifan Peng · 方向: 软件安全

社交媒体隐私泄露多模态基准隐私暴露评分隐藏英文摘要

Abstract:Public social media posts can reveal private information through weak cues scattered across text, images, or metadata. Such leakage is often cumulative and cross-post: cues that appear harmless in isolation may jointly expose a user's home, workplace, or routine. However, current research lacks a unified benchmark for user-level multimodal privacy leakage and an evaluation metric that captures exposure severity beyond binary accuracy. To address these gaps, we propose SopriBench, a synthetic benchmark guided by leakage patterns abstracted from a private reference corpus of Rednote and Instagram accounts, covering 50 user profiles and 1,569 images with attributes, contextual sensitivity, granularity, leakage type, inference difficulty, and supporting evidence. We further introduce the Privacy Exposure Score (PES), which weights value granularity by contextual sensitivity...

论文介绍 社交媒体公开帖子可能通过分散的微弱线索累积泄露用户隐私。本文针对用户级多模态隐私泄露问题，提出了基准SopriBench和隐私暴露评分PES。SopriBench是一个合成基准，模拟了泄露模式，并引入PES来量化不同敏感情境下的暴露严重性，旨在为评估和防御此类泄露提供统一框架。

The Custody Envelope Threshold: Authority-Scaled Admission of External Artifacts in Institutional Infrastructure

第一作者: Amadeus Brandes · 方向: 系统安全

基础设施安全外部构件接纳模型授权尺度隐藏英文摘要

Abstract:Modern infrastructure depends on externally maintained artifacts such as package-registry dependencies, CI/CD actions, container images, Terraform providers and modules, developer extensions, model artifacts, and AI tool servers. These artifacts are easy to fetch but difficult for institutions to admit, govern, and revoke. This paper proposes the Custody Envelope Threshold, an authority-scaled model of artifact admission. It argues that direct institutional admission is defensible only when object identity, ingress path, and revocation capacity are sufficiently closed relative to the execution authority delegated to the artifact. When this threshold is not met, institutions tend to proxy, policy-mediate, vendor-mediate, internalize, quarantine, or reject the artifact. The framework is operationalized as a four-condition ordinal instrument and connected to reference-monitor...

论文介绍 现代基础设施依赖大量外部维护的构件（如包依赖、容器镜像）。本文提出「监管信封阈值」模型，以应对机构接纳、治理和撤销这些构件的挑战。该模型是一个基于授权尺度的构件接纳框架，认为直接接纳仅在构件的标识、入口路径和撤销能力与所授权限足够封闭时才合理，否则机构应采取代理、策略调解等替代方案。

AgileOS: A GPU Operating System Layer for Protected CUDA Services

第一作者: Zhuoping Yang · 方向: 系统安全

GPU操作系统CUDA服务保护虚拟化隐藏英文摘要

Abstract:Modern GPU applications increasingly interact with storage systems, network devices, vendor libraries, and GPU-resident services rather than executing only isolated compute kernels. This shift creates a need for operating-system-like protection around GPU services, where service metadata, device queues, memory-mapped I/O regions, and library-internal state should not be directly exposed to untrusted application kernels. However, today's CUDA programming model, by default, still gives each application direct ownership of its CUDA context, device pointers, runtime handles, module loading path, and kernel launches, leaving protected GPU services to build their own ad hoc interfaces and isolation mechanisms. This paper presents the initial design and prototype scope of AgileOS, a GPU operating-system layer for protected CUDA services. AgileOS virtualizes CUDA at the library...

论文介绍 现代GPU应用频繁与外部系统交互，催生了对GPU服务进行操作系统级保护的需求。然而，当前CUDA编程模型默认暴露了过多的底层控制权。本文提出AgileOS的初步设计，这是一个用于受保护CUDA服务的GPU操作系统层。它通过在库层面虚拟化CUDA，旨在隔离服务元数据、设备队列等，防止不受信应用内核的直接访问。

MalTree: Tracing Malware Evolution from Embeddings at Scale

第一作者: Akash Amalan · 方向: AI 安全

06/05 01:51

恶意软件进化系统发育分析时间验证演化树隐藏英文摘要

Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve. Understanding evolutionary relationships among malware families can inform proactive defense, but traditional reverse engineering can take months to years to uncover such lineage relationships. We propose MalTree, a framework that applies bioinformatics inspired phylogenetic techniques (UPGMA and Neighbor-Joining) at scale to model malware evolution automatically using structural, behavioral, and image-based features. We introduce temporal validation using VirusTotal timestamps to assess whether inferred trees reflect actual evolutionary order. MalTree achieves 87% temporal consistency, indicating that inferred evolutionary relationships closely align with real-world emergence timelines. Our analysis shows that some families mutate over 10 times faster than others...

论文介绍 理解恶意软件家族间的进化关系有助于主动防御。本文提出MalTree框架，它借鉴生物信息学的系统发育技术（如UPGMA），利用结构、行为和图像特征大规模自动建模恶意软件的演化关系。该框架引入基于VirusTotal时间戳的时间验证方法，实验表明推断的演化树与真实出现时间线高度一致，达到了87%的时间一致性。

Subtle Injection for Ground-truth Inference of LLM Training Data

第一作者: Abraham Itzhak Weinberg · 方向: 软件安全

大语言模型训练数据成员推断金丝雀序列隐藏英文摘要

Abstract:As large language models (LLMs) are increasingly trained on scraped web corpora without authorisation, content owners require forensic methods to prove that their documents were included in a model's training set. We propose \textbf{SIGIL} (\textbf{S}ubtle \textbf{I}njection for \textbf{G}round-truth \textbf{I}nference of \textbf{L}LM training data), a framework that embeds imperceptible \emph{canary sequences} into protected text and code such that any LLM trained on those documents exhibits statistically detectable behavioural signatures when probed with targeted queries. SIGIL defines five canary strategies -- lexical-rare, lexical-phrase, syntactic, semantic, and code-pattern -- and a \emph{Membership Inference Score} (MIS) grounded in the Neyman-Pearson hypothesis testing framework with formal false-positive rate (FPR) control. Simulator parameters are calibrated against...

论文介绍 针对大语言模型可能未经许可使用网络文本进行训练的问题，本文提出SIGIL框架，帮助内容所有者取证证明其文档被纳入训练集。该方法在受保护文本和代码中嵌入难以察觉的「金丝雀序列」，当LLM使用这些数据训练后，通过特定查询可探测到统计上显著的行为特征。框架定义了五种金丝雀策略和基于假设检验的成员推断评分。

Enhancing Malware Detection with Generative AI: Using Variational Autoencoders to Boost Machine Learning Classifiers' Performance

第一作者: Mohammad Alharbi · 方向: AI 安全

05/17 13:14

恶意软件检测变分自编码器数据增强机器学习隐藏英文摘要

The advancement of malware poses obstacles for cybersecurity, necessitating the development of advanced detection techniques. This paper proposes an approach to enhance malware detection through the use of a generative artificial intelligence model. Specifically, variational autoencoders (VAEs) are used with the random forest, XGBoost and sequential model machine learning classifiers. Generated synthetic malware samples are used to address the critical issue of data scarcity for new or less common malware types. This approach can be used to augment datasets to improve classifier robustness. The proposed methodology uses VAEs to create high-quality diverse synthetic datasets that closely mimic real-world malware data. The effectiveness of these augmented datasets is evaluated by comparing the performance of the machine learning classifiers when they are trained with the original data...

论文介绍 为应对高级恶意软件的检测挑战，本文提出利用生成式人工智能增强检测能力。具体而言，采用变分自编码器生成合成的恶意软件样本，以解决新型或罕见恶意软件的数据稀缺问题。这些合成数据被用于增强数据集，旨在提升随机森林、XGBoost等机器学习分类器的鲁棒性和性能。

Sort, Partition, Randomize: Optimal Binary Hypothesis Testing under Local Differential Privacy

第一作者: Elena Ghazi · 方向: 软件安全

局部差分隐私二元假设测试f-散度最优机制排序分区随机化隐藏英文摘要

Abstract:We study optimal design of $\varepsilon$-locally differentially private mechanisms for binary hypothesis testing. Each observation is drawn from one of two known distributions $P_0,P_1$ on a finite alphabet of size $k$, privatized by a mechanism $Q$, and then used to infer which distribution generated the data. We measure testing utility using an $f$-divergence, including total variation, KL, and hockey-stick divergences, between the two induced output distributions. Previous work established structural properties of optimal mechanisms, but only yielded exponential-time algorithms. We prove a sharp structure: for every $\varepsilon$ and every $f$-divergence objective, after sorting the alphabet by likelihood ratio, there exists an optimal mechanism that partitions the sorted alphabet into contiguous blocks and applies randomized response to the block label. We call this class...

论文介绍 本文研究在ε-局部差分隐私约束下，二元假设测试的最优机制设计。每个观测来自两个已知分布之一，通过隐私机制处理后用于推断。作者证明，对字母表按似然比排序后，存在最优机制将排序字母表划分为连续块，并对块标签应用随机响应，适用于总变差、KL等多种f-散度目标，提高了计算效率。

An End-to-End Encrypted Control Pipeline for Multi-Agent Coordination via CKKS Homomorphic Encryption

第一作者: Sai Sandeep Damera · 方向: 密码学协议

同态加密多智能体协调CKKS端到端加密控制管道隐藏英文摘要

Abstract:Cloud-based coordination of multi-agent systems requires sharing state with a central server, creating a conflict between coordination and privacy. Fully homomorphic encryption (FHE) resolves this in principle, but its severe arithmetic constraints demand that every stage of the control loop be redesigned from first principles. We present an end-to-end encrypted control pipeline in which sensing, state estimation, state propagation, and consensus control all operate on CKKS-encrypted data using only addition, multiplication, and cyclic rotation. In order to overcome the computational challenges of FHE, we employ steady-state Kalman gains instead of solving for the matrices online and graph Laplacians are applied via the diagonal method at a cost proportional to the number of nonzero cyclic diagonals, accommodating ring, torus, and complete-graph topologies within a unified...

论文介绍 针对多智能体协调中隐私与协调的冲突，本文提出基于CKKS同态加密的端到端加密控制管道。所有阶段（感知、状态估计、传播和共识控制）均在加密数据上运行，仅使用加法、乘法和循环旋转操作。采用稳态卡尔曼增益和图拉普拉斯对角方法，支持环状、环面和完全图拓扑，解决了云计算中的隐私保护问题。

The Capacity of Information-Theoretic Secure Aggregation in Federated Learning

第一作者: Lanxin Yi · 方向: 隐私保护

联邦学习安全聚合信息论密钥分发通信成本隐藏英文摘要

Abstract:Secure aggregation allows a server to aggregate users' local updates while preserving update privacy. Existing information-theoretic problems typically assume that correlated random keys are provided by a trusted third party (TTP) or generated via prescribed groupwise structures, while the communication cost for establishing such correlated keys is often ignored. Consequently, the fundamental limits under general key-distribution mechanisms remain unknown. In this paper, we study the $T$-colluding information-theoretic secure aggregation problem with $N$ users under a general two-phase framework consisting of a key distribution phase and an update aggregation phase. Unlike prior work, we model key distribution through user-to-user communication and allow arbitrary user-generated key-distribution mechanisms, eliminating TTP or prescribed structures. This enables a joint...

论文介绍 本文研究联邦学习中信息论安全聚合的容量问题。考虑N个用户和T-共谋模型，提出通用两阶段框架：密钥分发阶段和更新聚合阶段。通过用户间通信分发密钥，消除对可信第三方的依赖，分析任意用户生成密钥机制下的最优性，为隐私保护聚合提供理论基础。

A Large-Scale Per-Speaker Analysis of Re-identification Risk in Speech Anonymization

第一作者: Orane Dufour · 方向: 隐私保护

06/05 20:21

语音匿名化重识别风险逐说话者分析隐私评估声纹识别隐藏英文摘要

Speech anonymization is commonly evaluated using averagecase metrics such as the equal error rate, which can hide large disparities in re-identification risks across individuals. In this paper, we conduct a large-scale per-speaker privacy analysis using a linkability-based metric under a worst-case scenario. Nearly 5,000 speakers are evaluated across multiple anonymization systems, attacker architectures, and conversation lengths. While linkability scores are highly polarized at the speaker level, the sets of easy to re-identify and hard to re-identify speakers vary substantially across configurations. We show that no single factor explains speaker vulnerability. Instead, the re-identification risk emerges from the interaction between the attacker, the anonymizer, and the amount of available speech. These results challenge the notion of intrinsic speaker-level privacy risks and...

论文介绍 对语音匿名化系统进行大规模逐说话者隐私风险分析。采用基于可链接性的指标，在最坏情况下评估近5000名说话者。发现重识别风险在说话者层面高度分化，受攻击者、匿名器和语音量交互影响，无单一因素可解释脆弱性，挑战了内在说话者隐私风险的概念。

TRACE: Trajectory Reasoning through Adaptive Cross-Step Evidence Aggregation for LLM Agents

第一作者: Vijitha Mittapalli · 方向: AI 安全

LLM智能体轨迹监控证据聚合恶意行为检测TRACE框架隐藏英文摘要

Abstract:Autonomous LLM agents can pursue hidden malicious objectives through sequences of individually benign actions, making sabotage difficult to detect using standard trajectory-level monitoring. Existing approaches either evaluate complete trajectories in a single pass or partition them into independently scored windows, limiting their ability to connect evidence across temporally distant actions. We propose TRACE, a monitoring framework for long-horizon LLM agent trajectories. TRACE operates through a TIJ (Triage-Inspect-Judge) loop that identifies high-signal regions, performs targeted inspection while maintaining accumulated evidence across reasoning steps, and synthesizes a trajectory-level verdict. We evaluate TRACE on ten task domains from SHADE-Arena against state-of-the-art baselines. TRACE achieves an aggregate F1 of 0.713 and recall of 0.844, with the largest gains on...

论文介绍 针对LLM智能体通过隐蔽行动进行恶意行为检测困难的问题，提出TRACE监控框架。通过Triage-Inspect-Judge循环，识别高信号区域，跨推理步骤聚合证据，综合轨迹级判决。在多种任务领域中评估，实现高F1和召回率，增强了长 horizon 轨迹监控的有效性。

An Expanded Synthetic Conversation Dataset for Multi-Turn Smishing Detection

第一作者: Carl Lochstampfor · 方向: 安全研究

06/05 11:46

短信钓鱼检测多轮对话合成数据集COVA-X长文档模型隐藏英文摘要

Our prior work introduced COVA, a synthetically generated multi-turn conversational smishing dataset of 3,201 labeled conversations, establishing baseline detection benchmarks across eight models. While XGBoost with TF-IDF features achieved the best performance, with 72.5\% accuracy and 0.691 macro F1, transformer models underperformed, which was attributed to input truncation and insufficient training data. We present COVA-X, an expanded dataset of 10,985 conversations spanning eight elder-targeted scam categories, produced by an improved generation pipeline addressing contamination, label mismatch, stage-direction bleed, and prompt-design failures from the first iteration. Retraining all classifiers on the expanded dataset yields the central finding of this work: Longformer now surpasses XGBoost on all evaluation metrics, achieving 79.71\% accuracy and 0.7786 macro F1 compared with...

论文介绍 扩展多轮短信钓鱼检测的合成对话数据集COVA-X，包含10,985个对话，覆盖八类老年诈骗。改进生成管道，解决污染、标签不匹配等问题。在扩展数据集上重新训练分类器，发现Longformer模型在所有评估指标上超越XGBoost，将准确率提高到79.71%，提升了检测性能。

Unified Safe In-context Image Generation in Multimodal Diffusion Transformers via Restricting Unsafe Information Flows

第一作者: Xiang Yang · 方向: 安全研究

扩散变换器图像生成安全不安全信息流UVR框架多模态注意力隐藏英文摘要

Abstract:Diffusion transformers (DiTs) equipped with multimodal attention (MM-Attn) have become a dominant paradigm for image generation. However, preventing the generation of harmful content remains a critical challenge, particularly in image-to-image (I2I) editing tasks. Existing safety mechanisms are primarily designed for text-to-image (T2I) synthesis or U-Net-based architectures, which limits their effectiveness for unified safety mitigation in DiT-based frameworks. To bridge this gap, we propose Unified Visual Safety Regulator (UVR), a training-free safe generation framework that regulates unsafe semantics in generated images. UVR is grounded in an analysis of attention dynamics from the perspective of information flow in MM-Attn. We identify a task-independent start-up stage, during which unsafe semantics in output patches rapidly emerge and can be accurately localized, followed...

论文介绍 针对多模态扩散变换器（DiT）中图像生成的安全挑战，提出Unified Visual Safety Regulator（UVR）框架。通过分析注意力动态，识别任务无关的启动阶段，在其中定位不安全语义，并限制信息流。无需训练即可调控生成图像中的有害内容，适用于文本到图像和图像到图像编辑。

Hearing the Unspoken: Language Model Priors for Acoustic Adversarial Attacks

第一作者: Jiani Xie · 方向: AI 安全

06/05 10:18

声学对抗攻击自动语音识别语言模型先验实时处理对抗性扰动隐藏英文摘要

Automatic Speech Recognition (ASR) systems operating in real-time settings must process acoustic input under strict temporal constraints, where transcription decisions are inherently made on incomplete information. This causal constraint serves as an information bottleneck on attackers, significantly limiting attack performance. Our new Semantic Gambit attack breaks this causal limitation by augmenting the adversary with predictive context derived from a Large Language Model in real-time. Our experiments show that this form of augmentation can elevate the corpus-level Word Error Rate to 35.6% -- a three-fold increase over the current state-of-the-art. Ultimately, this work reveals how common, low-latency LLM tooling can be exploited to systematically subvert real-time ASR pipelines.

论文介绍 提出Semantic Gambit攻击，利用语言模型先验增强声学对抗攻击，以打破实时自动语音识别（ASR）系统的因果限制。通过预测上下文提供额外信息，显著提升攻击效果，在语料级将词错误率提高到35.6%，揭示低延迟LLM工具可被利用来系统性地颠覆实时ASR管道。

The Economics of Proof-of-Useful-Work

第一作者: Rafael Pass · 方向: 区块链安全

06/05 04:39

区块链工作量证明有用工作证明经济学模型安全性隐藏英文摘要

Proof-of-work (PoW) blockchains rely on computational expenditure to secure a ledger supporting a native cryptocurrency. In existing systems such as Bitcoin, this expenditure is intentionally useless: the computation secures consensus but produces no external economic output. An emerging alternative -- proof of useful work (PoUW) -- enables the same computation to simultaneously secure the blockchain and generate economically valuable output. However, PoUW is often criticized on economic grounds: if the work is useful, attackers might be "paid to attack," potentially weakening security. We develop a competitive-equilibrium model of a PoUW blockchain in which compute can be allocated across pure mining, pure useful work -- instantiated as machine-learning inference -- or "duplex" work that produces both with computational overheads. We provide a complete closed-form characterization of...

论文介绍 该研究探讨有用工作证明（PoUW）区块链的经济学问题。传统工作量证明（PoW）系统中计算支出无用，而PoUW旨在使计算同时确保区块链安全和产生经济价值，但可能被攻击者利用以削弱安全性。作者开发了一个竞争均衡模型，分析计算如何分配于纯挖矿、纯有用工作（如机器学习推理）或双工工作，并提供完整的解析表征，为PoUW系统的设计提供经济学见解，平衡安全与效率。

Beyond the Canonical Protocol: Quantum Encrypted Cloning from Secret-Sharing Access Structures

第一作者: Gabriele Gianini · 方向: 密码学协议

06/04 17:23

量子加密克隆量子秘密共享访问结构密码学量子计算隐藏英文摘要

Quantum encrypted cloning shows that an unknown quantum state can be distributed into multiple encrypted copies without contradicting the no-cloning theorem: each copy is unusable on its own, but can be redeemed together with a suitable quantum key. Recent work has related canonical encrypted-cloning protocols to particular forms of quantum secret sharing. Here we take the converse perspective: instead of mapping a given encrypted-cloning protocol into QSS, we use QSS access structures as a design library from which encrypted-cloning schemes can be extracted. The criterion is access-structural. A QSS scheme supports a quantum encrypted-cloning structure whenever it contains a family of qualified sets with a non-qualified common intersection. The common subsystem is interpreted as the key, while the non-common parts are interpreted as encrypted clones relative to that key. Thus quantum...

论文介绍 该研究解决如何在不违反量子无克隆定理的情况下，将未知量子态分发为多个加密副本的问题。核心方法是从量子秘密共享（QSS）的访问结构中提取加密克隆方案，利用合格集与非合格交集定义密钥和加密克隆。这种基于访问结构的设计为量子安全通信和量子信息处理提供了新的协议框架，扩展了加密克隆的应用潜力。

Online Safety Regulation Increases Privacy Risk: Evidence from the UK Online Safety Act

第一作者: Dhyey Mehta · 方向: 隐私保护

在线安全法规隐私风险英国在线安全法用户行为VPN隐藏英文摘要

Abstract:Governments worldwide are increasingly regulating digital platforms to reduce online harms, particularly those affecting children. However, access restrictions can alter user behaviour and introduce new privacy and security risks. The UK Online Safety Act (OSA), passed in October 2023, illustrates this trend: it extends age-assurance and safety requirements to social media, search, and pornography services, and rolled out in phases. Ofcom's illegal content enforcement duties came into force in March 2025, and mandatory age verification for adult content took effect in July 2025. This phased rollout enables real-time observation of behavioural responses to regulation. To address this, we analyse Reddit discourse across VPN and UK Politics communities and conduct a privacy-policy risk analysis of 69 unique VPN services. We find that each of these three milestones produced...

论文介绍 该研究分析英国在线安全法（OSA）对用户隐私和安全的影响。通过追踪法规实施的三个关键时间点，作者分析了Reddit上VPN和英国政治社区的讨论，并对69个VPN服务进行隐私政策风险评估。研究发现，法规可能导致用户行为改变，从而增加隐私风险，为政策制定者评估法规效果提供实证依据。