arXiv subjects

Xianghang Mi

Publications and source records attributed to Xianghang Mi.

18 recordsLinked to original sources

arXiv2026

Pepper: High-bandwidth and Scalable Anonymous Broadcast with Cryptographic Privacy

We present Pepper, a high-bandwidth anonymous broadcast protocol that provides cryptographic sender anonymity against global adversaries. Pepper builds on a two-server DC-net architecture but introduces three key innovations: a self-contained anonymous registration subprotocol using verifiable distributed point functions, support for batch messaging via distributed multi-point functions, and a lightweight access control mechanism based on secret-shared proofs. Unlike prior systems, Pepper eliminates the need for external dialing services and allows each broadcaster to send multiple messages per epoch with a single audit, significantly improving throughput for large data transfers. Our implementation demonstrates that Pepper achieves millisecond-level registration audits, scales efficiently to thousands of channels, and delivers 1.2--20$\times$ higher effective messaging rates than state-of-the-art alternatives. Furthermore, Pepper is designed for practical deployment, with natural compatibility for co-deployment alongside Tor and federated social networks.

cs.CR↗

arXiv2026

When Youth Enter the Algorithmic Wild: Discovering and Understanding Potentially Harmful Teen Videos on Douyin and Kwai

Short-video platforms like Douyin and Kwai have become central to adolescent digital life, but they also risk exposing teens to algorithmically amplified harmful content. Despite its societal importance, the scale, mechanisms, and real-world impact of this exposure remain poorly understood. Measuring it is challenging: recommendation feeds are personalized black boxes, harmful content employs sophisticated evasion tactics, and naive crawlers fail to replicate authentic teen behavior. To bridge this gap, we propose PHTV-Scout, the first large-scale, behaviorally grounded measurement framework for Potentially Harmful Teen Videos (PHTVs). We integrate an offline survey of 683 adolescents with a tri-module online pipeline: (1) PHTV Hunter simulates teen accounts to collect recommendation feeds; (2) PHTV Arbiter, a LoRA-finetuned multimodal classifier, detects PHTVs with 94.29% accuracy and 96.41% precision; and (3) PHTV Analyzer performs fine-grained categorization and impact assessment. Over six months, we analyzed 186,727 videos and 51,287 comments, uncovering a troubling 6.11% PHTV prevalence--dominated by Child Sexual Exploitation Imagery (53.2%)--and revealing that harmful content thrives through covert interactions (e.g., grooming comments, self-disclosure) and active evasion (semantic camouflage, noise injection). Crucially, while Youth Mode blocks 100% of PHTVs, its low adoption (30-41%) leaves most teens unprotected. We further show that exposure is driven not by user identity but by regulation, platform algorithms, and even passive browsing, exposing the fragility of adolescent information environments. Our findings call for a paradigm shift from reactive takedowns to proactive, human-centered safeguards.

cs.CR↗

arXiv2026

Seeing the Unseen: Rethinking Illicit Promotion Detection with In-Context Learning

Illicit online promotion is a persistent threat that evolves to evade detection. Existing moderation systems remain tethered to platform-specific supervision and static taxonomies, a reactive paradigm that struggles to generalize across domains or uncover novel threats. This paper presents a systematic study of In-Context Learning (ICL) as a unified framework for illicit promotion detection. Through rigorous analysis, we show that properly configured ICL achieves performance comparable to fine-tuned models using 22x fewer labeled examples. We demonstrate three key capabilities: (1) Generalization to unseen threats: ICL generalizes to new illicit categories without category-specific demonstrations, with a performance drop of less than 6% for most evaluated categories. (2) Autonomous discovery: A novel two-stage pipeline distills 2,900 free-form labels into coherent taxonomies, surfacing eight previously undocumented illicit categories such as usury and illegal immigration. (3) Cross-platform generalization: Deployed on 200,000 real-world samples from search engines and Twitter without adaptation, ICL achieves 92.6% accuracy. Furthermore, 61.8% of its uniquely flagged samples correspond to borderline or obfuscated content missed by existing detectors. Our findings position ICL as a new paradigm for content moderation, combining the precision of specialized classifiers with cross-platform generalization and autonomous threat discovery. By shifting to inference-time reasoning, ICL offers a path toward proactively adaptive moderation systems.

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arXiv2026

Okara: Detection and Attribution of TLS Man-in-the-Middle Vulnerabilities in Android Apps with Foundation Models

Transport Layer Security (TLS) is fundamental to secure online communication, yet vulnerabilities in certificate validation that enable Man-in-the-Middle (MitM) attacks remain a pervasive threat in Android apps. Existing detection tools are hampered by low-coverage UI interaction, costly instrumentation, and a lack of scalable root-cause analysis. We present Okara, a framework that leverages foundation models to automate the detection and deep attribution of TLS MitM Vulnerabilities (TMVs). Okara's detection component, TMV-Hunter, employs foundation model-driven GUI agents to achieve high-coverage app interaction, enabling efficient vulnerability discovery at scale. Deploying TMV-Hunter on 37,349 apps from Google Play and a third-party store revealed 8,374 (22.42%) vulnerable apps. Our measurement shows these vulnerabilities are widespread across all popularity levels, affect critical functionalities like authentication and code delivery, and are highly persistent with a median vulnerable lifespan of over 1,300 days. Okara's attribution component, TMV-ORCA, combines dynamic instrumentation with a novel LLM-based classifier to locate and categorize vulnerable code according to a comprehensive new taxonomy. This analysis attributes 41% of vulnerabilities to third-party libraries and identifies recurring insecure patterns, such as empty trust managers and flawed hostname verification. We have initiated a large-scale responsible disclosure effort and will release our tools and datasets to support further research and mitigation.

cs.CR↗

arXiv2026

ICL-EVADER: Zero-Query Black-Box Evasion Attacks on In-Context Learning and Their Defenses

In-context learning (ICL) has become a powerful, data-efficient paradigm for text classification using large language models. However, its robustness against realistic adversarial threats remains largely unexplored. We introduce ICL-Evader, a novel black-box evasion attack framework that operates under a highly practical zero-query threat model, requiring no access to model parameters, gradients, or query-based feedback during attack generation. We design three novel attacks, Fake Claim, Template, and Needle-in-a-Haystack, that exploit inherent limitations of LLMs in processing in-context prompts. Evaluated across sentiment analysis, toxicity, and illicit promotion tasks, our attacks significantly degrade classifier performance (e.g., achieving up to 95.3% attack success rate), drastically outperforming traditional NLP attacks which prove ineffective under the same constraints. To counter these vulnerabilities, we systematically investigate defense strategies and identify a joint defense recipe that effectively mitigates all attacks with minimal utility loss (<5% accuracy degradation). Finally, we translate our defensive insights into an automated tool that proactively fortifies standard ICL prompts against adversarial evasion. This work provides a comprehensive security assessment of ICL, revealing critical vulnerabilities and offering practical solutions for building more robust systems. Our source code and evaluation datasets are publicly available at: https://github.com/ChaseSecurity/ICL-Evader .

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arXiv2025

Beyond One-Size-Fits-All: Personalized Harmful Content Detection with In-Context Learning

The proliferation of harmful online content--e.g., toxicity, spam, and negative sentiment--demands robust and adaptable moderation systems. However, prevailing moderation systems are centralized and task-specific, offering limited transparency and neglecting diverse user preferences--an approach ill-suited for privacy-sensitive or decentralized environments. We propose a novel framework that leverages in-context learning (ICL) with foundation models to unify the detection of toxicity, spam, and negative sentiment across binary, multi-class, and multi-label settings. Crucially, our approach enables lightweight personalization, allowing users to easily block new categories, unblock existing ones, or extend detection to semantic variations through simple prompt-based interventions--all without model retraining. Extensive experiments on public benchmarks (TextDetox, UCI SMS, SST2) and a new, annotated Mastodon dataset reveal that: (i) foundation models achieve strong cross-task generalization, often matching or surpassing task-specific fine-tuned models; (ii) effective personalization is achievable with as few as one user-provided example or definition; and (iii) augmenting prompts with label definitions or rationales significantly enhances robustness to noisy, real-world data. Our work demonstrates a definitive shift beyond one-size-fits-all moderation, establishing ICL as a practical, privacy-preserving, and highly adaptable pathway for the next generation of user-centric content safety systems. To foster reproducibility and facilitate future research, we publicly release our code on GitHub and the annotated Mastodon dataset on Hugging Face.

cs.CL↗

arXiv2025

Detecting and Understanding the Promotion of Illicit Goods and Services on Twitter

In this study, we reveal, for the first time, popular online social networks (especially Twitter) are being extensively abused by miscreants to promote illicit goods and services of diverse categories. This study is made possible by multiple machine learning tools that are designed to detect and analyze Posts of Illicit Promotion (PIPs) as well as revealing their underlying promotion campaigns. Particularly, we observe that PIPs are prevalent on Twitter, along with extensive visibility on other three popular OSNs including YouTube, Facebook, and TikTok. For instance, applying our PIP hunter to the Twitter platform for 6 months has led to the discovery of 12 million distinct PIPs which are widely distributed in 5 major natural languages and 10 illicit categories, e.g., drugs, data leakage, gambling, and weapon sales. Along the discovery of PIPs are 580K Twitter accounts publishing PIPs as well as 37K distinct instant messaging accounts that are embedded in PIPs and serve as next hops of communication with prospective customers. Also, an arms race between Twitter and illicit promotion operators is also observed. Especially, 90% PIPs can survice the first two months since getting published on Twitter, which is likely due to the diverse evasion tactics adopted by miscreants to masquerade PIPs.

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arXiv2024

Dissecting Open Edge Computing Platforms: Ecosystem, Usage, and Security Risks

Emerging in recent years, open edge computing platforms (OECPs) claim large-scale edge nodes, the extensive usage and adoption, as well as the openness to any third parties to join as edge nodes. For instance, OneThingCloud, a major OECP operated in China, advertises 5 million edge nodes, 70TB bandwidth, and 1,500PB storage. However, little information is publicly available for such OECPs with regards to their technical mechanisms and involvement in edge computing activities. Furthermore, different from known edge computing paradigms, OECPs feature an open ecosystem wherein any third party can participate as edge nodes and earn revenue for the contribution of computing and bandwidth resources, which, however, can introduce byzantine or even malicious edge nodes and thus break the traditional threat model for edge computing. In this study, we conduct the first empirical study on two representative OECPs, which is made possible through the deployment of edge nodes across locations, the efficient and semi-automatic analysis of edge traffic as well as the carefully designed security experiments. As the results, a set of novel findings and insights have been distilled with regards to their technical mechanisms, the landscape of edge nodes, the usage and adoption, and the practical security/privacy risks. Particularly, millions of daily active edge nodes have been observed, which feature a wide distribution in the network space and the extensive adoption in content delivery towards end users of 16 popular Internet services. Also, multiple practical and concerning security risks have been identified along with acknowledgements received from relevant parties, e.g., the exposure of long-term and cross-edge-node credentials, the co-location with malicious activities of diverse categories, the failures of TLS certificate verification, the extensive information leakage against end users, etc.

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arXiv2024

Shining Light into the Tunnel: Understanding and Classifying Network Traffic of Residential Proxies

Emerging in recent years, residential proxies (RESIPs) feature multiple unique characteristics when compared with traditional network proxies (e.g., commercial VPNs), particularly, the deployment in residential networks rather than data center networks, the worldwide distribution in tens of thousands of cities and ISPs, and the large scale of millions of exit nodes. All these factors allow RESIP users to effectively masquerade their traffic flows as ones from authentic residential users, which leads to the increasing adoption of RESIP services, especially in malicious online activities. However, regarding the (malicious) usage of RESIPs (i.e., what traffic is relayed by RESIPs), current understanding turns out to be insufficient. Particularly, previous works on RESIP traffic studied only the maliciousness of web traffic destinations and the suspicious patterns of visiting popular websites. Also, a general methodology is missing regarding capturing large-scale RESIP traffic and analyzing RESIP traffic for security risks. Furthermore, considering many RESIP nodes are found to be located in corporate networks and are deployed without proper authorization from device owners or network administrators, it is becoming increasingly necessary to detect and block RESIP traffic flows, which unfortunately is impeded by the scarcity of realistic RESIP traffic datasets and effective detection methodologies. To fill in these gaps, multiple novel tools have been designed and implemented in this study, which include a general framework to deploy RESIP nodes and collect RESIP traffic in a distributed manner, a RESIP traffic analyzer to efficiently process RESIP traffic logs and surface out suspicious traffic flows, and multiple machine learning based RESIP traffic classifiers to timely and accurately detect whether a given traffic flow is RESIP traffic or not.

cs.CR↗

arXiv2024

SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection

In this study, we introduce SpamDam, a SMS spam detection framework designed to overcome key challenges in detecting and understanding SMS spam, such as the lack of public SMS spam datasets, increasing privacy concerns of collecting SMS data, and the need for adversary-resistant detection models. SpamDam comprises four innovative modules: an SMS spam radar that identifies spam messages from online social networks(OSNs); an SMS spam inspector for statistical analysis; SMS spam detectors(SSDs) that enable both central training and federated learning; and an SSD analyzer that evaluates model resistance against adversaries in realistic scenarios. Leveraging SpamDam, we have compiled over 76K SMS spam messages from Twitter and Weibo between 2018 and 2023, forming the largest dataset of its kind. This dataset has enabled new insights into recent spam campaigns and the training of high-performing binary and multi-label classifiers for spam detection. Furthermore, effectiveness of federated learning has been well demonstrated to enable privacy-preserving SMS spam detection. Additionally, we have rigorously tested the adversarial robustness of SMS spam detection models, introducing the novel reverse backdoor attack, which has shown effectiveness and stealthiness in practical tests.

cs.CR↗

arXiv2024

Reflected Search Poisoning for Illicit Promotion

As an emerging black hat search engine optimization (SEO) technique, reflected search poisoning (RSP) allows a miscreant to free-ride the reputation of high-ranking websites, poisoning search engines with illicit promotion texts (IPTs) in an efficient and stealthy manner, while avoiding the burden of continuous website compromise as required by traditional promotion infections. However, little is known about the security implications of RSP, e.g., what illicit promotion campaigns are being distributed by RSP, and to what extent regular search users can be exposed to illicit promotion texts distributed by RSP. In this study, we conduct the first security study on RSP-based illicit promotion, which is made possible through an end-to-end methodology for capturing, analyzing, and infiltrating IPTs. As a result, IPTs distributed via RSP are found to be large-scale, continuously growing, and diverse in both illicit categories and natural languages. Particularly, we have identified over 11 million distinct IPTs belonging to 14 different illicit categories, with typical examples including drug trading, data theft, counterfeit goods, and hacking services. Also, the underlying RSP cases have abused tens of thousands of high-ranking websites, as well as extensively poisoning all four popular search engines we studied, especially Google Search and Bing. Furthermore, it is observed that benign search users are being exposed to IPTs at a concerning extent. To facilitate interaction with potential customers (victim search users), miscreants tend to embed various types of contacts in IPTs, especially instant messaging accounts. Further infiltration of these IPT contacts reveals that the underlying illicit campaigns are operated on a large scale. All these findings highlight the negative security implications of IPTs and RSPs, and thus call for more efforts to mitigate RSP-driven illicit promotion.

cs.CR↗

arXiv2024

Port Forwarding Services Are Forwarding Security Risks

We conduct the first comprehensive security study on representative port forwarding services (PFS), which emerge in recent years and make the web services deployed in internal networks available on the Internet along with better usability but less complexity compared to traditional techniques (e.g., NAT traversal techniques). Our study is made possible through a set of novel methodologies, which are designed to uncover the technical mechanisms of PFS, experiment attack scenarios for PFS protocols, automatically discover and snapshot port-forwarded websites (PFWs) at scale, and classify PFWs into well-observed categories. Leveraging these methodologies, we have observed the widespread adoption of PFS with millions of PFWs distributed across tens of thousands of ISPs worldwide. Furthermore, 32.31% PFWs have been classified into website categories that serve access to critical data or infrastructure, such as, web consoles for industrial control systems, IoT controllers, code repositories, and office automation systems. And 18.57% PFWs didn't enforce any access control for external visitors. Also identified are two types of attacks inherent in the protocols of Oray (one well-adopted PFS provider), and the notable abuse of PFSes by malicious actors in activities such as malware distribution, botnet operation and phishing.

cs.CR↗

arXiv2024

Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning

Despite achieving good performance and wide adoption, machine learning based security detection models (e.g., malware classifiers) are subject to concept drift and evasive evolution of attackers, which renders up-to-date threat data as a necessity. However, due to enforcement of various privacy protection regulations (e.g., GDPR), it is becoming increasingly challenging or even prohibitive for security vendors to collect individual-relevant and privacy-sensitive threat datasets, e.g., SMS spam/non-spam messages from mobile devices. To address such obstacles, this study systematically profiles the (in)feasibility of federated learning for privacy-preserving cyber threat detection in terms of effectiveness, byzantine resilience, and efficiency. This is made possible by the build-up of multiple threat datasets and threat detection models, and more importantly, the design of realistic and security-specific experiments. We evaluate FL on two representative threat detection tasks, namely SMS spam detection and Android malware detection. It shows that FL-trained detection models can achieve a performance that is comparable to centrally trained counterparts. Also, most non-IID data distributions have either minor or negligible impact on the model performance, while a label-based non-IID distribution of a high extent can incur non-negligible fluctuation and delay in FL training. Then, under a realistic threat model, FL turns out to be adversary-resistant to attacks of both data poisoning and model poisoning. Particularly, the attacking impact of a practical data poisoning attack is no more than 0.14\% loss in model accuracy. Regarding FL efficiency, a bootstrapping strategy turns out to be effective to mitigate the training delay as observed in label-based non-IID scenarios.

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arXiv2022

Stealthy Peers: Understanding Security Risks of WebRTC-Based Peer-Assisted Video Streaming

As an emerging service for in-browser content delivery, peer-assisted delivery network (PDN) is reported to offload up to 95\% of bandwidth consumption for video streaming, significantly reducing the cost incurred by traditional CDN services. With such benefits, PDN services significantly impact today's video streaming and content delivery model. However, their security implications have never been investigated. In this paper, we report the first effort to address this issue, which is made possible by a suite of methodologies, e.g., an automatic pipeline to discover PDN services and their customers, and a PDN analysis framework to test the potential security and privacy risks of these services. Our study has led to the discovery of 3 representative PDN providers, along with 134 websites and 38 mobile apps as their customers. Most of these PDN customers are prominent video streaming services with millions of monthly visits or app downloads (from Google Play). Also found in our study are another 9 top video/live streaming websites with each equipped with a proprietary PDN solution. Most importantly, our analysis on these PDN services has brought to light a series of security risks, which have never been reported before, including free riding of the public PDN services, video segment pollution, exposure of video viewers' IPs to other peers, and resource squatting. All such risks have been studied through controlled experiments and measurements, under the guidance of our institution's IRB. We have responsibly disclosed these security risks to relevant PDN providers, who have acknowledged our findings, and also discussed the avenues to mitigate these risks.

cs.CR↗

arXiv2022

An Extensive Study of Residential Proxies in China

We carry out the first in-depth characterization of residential proxies (RESIPs) in China, for which little is studied in previous works. Our study is made possible through a semantic-based classifier to automatically capture RESIP services. In addition to the classifier, new techniques have also been identified to capture RESIPs without interacting with and relaying traffic through RESIP services, which can significantly lower the cost and thus allow a continuous monitoring of RESIPs. Our RESIP service classifier has achieved a good performance with a recall of 99.7% and a precision of 97.6% in 10-fold cross validation. Applying the classifier has identified 399 RESIP services, a much larger set compared to 38 RESIP services collected in all previous works. Our effort of RESIP capturing lead to a collection of 9,077,278 RESIP IPs (51.36% are located in China), 96.70% of which are not covered in publicly available RESIP datasets. An extensive measurement on RESIPs and their services has uncovered a set of interesting findings as well as several security implications. Especially, 80.05% RESIP IPs located in China have sourced at least one malicious traffic flows during 2021, resulting in 52-million malicious traffic flows in total. And RESIPs have also been observed in corporation networks of 559 sensitive organizations including government agencies, education institutions and enterprises. Also, 3,232,698 China RESIP IPs have opened at least one TCP/UDP ports for accepting relaying requests, which incurs non-negligible security risks to the local network of RESIPs. Besides, 91% China RESIP IPs are of a lifetime less than 10 days while most China RESIP services show up a crest-trough pattern in terms of the daily active RESIPs across time.

cs.CR↗

arXiv2022

Clues in Tweets: Twitter-Guided Discovery and Analysis of SMS Spam

With its critical role in business and service delivery through mobile devices, SMS (Short Message Service) has long been abused for spamming, which is still on the rise today possibly due to the emergence of A2P bulk messaging. The effort to control SMS spam has been hampered by the lack of up-to-date information about illicit activities. In our research, we proposed a novel solution to collect recent SMS spam data, at a large scale, from Twitter, where users voluntarily report the spam messages they receive. For this purpose, we designed and implemented SpamHunter, an automated pipeline to discover SMS spam reporting tweets and extract message content from the attached screenshots. Leveraging SpamHunter, we collected from Twitter a dataset of 21,918 SMS spam messages in 75 languages, spanning over four years. To our best knowledge, this is the largest SMS spam dataset ever made public. More importantly, SpamHunter enables us to continuously monitor emerging SMS spam messages, which facilitates the ongoing effort to mitigate SMS spamming. We also performed an in-depth measurement study that sheds light on the new trends in the spammer's strategies, infrastructure and spam campaigns. We also utilized our spam SMS data to evaluate the robustness of the spam countermeasures put in place by the SMS ecosystem, including anti-spam services, bulk SMS services, and text messaging apps. Our evaluation shows that such protection cannot effectively handle those spam samples: either introducing significant false positives or missing a large number of newly reported spam messages.

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arXiv2018

Understanding and Mitigating the Security Risks of Voice-Controlled Third-Party Skills on Amazon Alexa and Google Home

Virtual personal assistants (VPA) (e.g., Amazon Alexa and Google Assistant) today mostly rely on the voice channel to communicate with their users, which however is known to be vulnerable, lacking proper authentication. The rapid growth of VPA skill markets opens a new attack avenue, potentially allowing a remote adversary to publish attack skills to attack a large number of VPA users through popular IoT devices such as Amazon Echo and Google Home. In this paper, we report a study that concludes such remote, large-scale attacks are indeed realistic. More specifically, we implemented two new attacks: voice squatting in which the adversary exploits the way a skill is invoked (e.g., "open capital one"), using a malicious skill with similarly pronounced name (e.g., "capital won") or paraphrased name (e.g., "capital one please") to hijack the voice command meant for a different skill, and voice masquerading in which a malicious skill impersonates the VPA service or a legitimate skill to steal the user's data or eavesdrop on her conversations. These attacks aim at the way VPAs work or the user's mis-conceptions about their functionalities, and are found to pose a realistic threat by our experiments (including user studies and real-world deployments) on Amazon Echo and Google Home. The significance of our findings have already been acknowledged by Amazon and Google, and further evidenced by the risky skills discovered on Alexa and Google markets by the new detection systems we built. We further developed techniques for automatic detection of these attacks, which already capture real-world skills likely to pose such threats.

cs.CR↗

arXiv2017

Understanding IoT Security Through the Data Crystal Ball: Where We Are Now and Where We Are Going to Be

Inspired by the boom of the consumer IoT market, many device manufacturers, start-up companies and technology giants have jumped into the space. Unfortunately, the exciting utility and rapid marketization of IoT, come at the expense of privacy and security. Industry reports and academic work have revealed many attacks on IoT systems, resulting in privacy leakage, property loss and large-scale availability problems. To mitigate such threats, a few solutions have been proposed. However, it is still less clear what are the impacts they can have on the IoT ecosystem. In this work, we aim to perform a comprehensive study on reported attacks and defenses in the realm of IoT aiming to find out what we know, where the current studies fall short and how to move forward. To this end, we first build a toolkit that searches through massive amount of online data using semantic analysis to identify over 3000 IoT-related articles. Further, by clustering such collected data using machine learning technologies, we are able to compare academic views with the findings from industry and other sources, in an attempt to understand the gaps between them, the trend of the IoT security risks and new problems that need further attention. We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas. We use this taxonomy as a beacon to assess each IoT work across a number of properties we define. Our assessment reveals that relevant security and privacy problems are far from solved. We discuss how each proposed solution can be applied to a problem area and highlight their strengths, assumptions and constraints. We stress the need for a security framework for IoT vendors and discuss the trend of shifting security liability to external or centralized entities. We also identify open research problems and provide suggestions towards a secure IoT ecosystem.

cs.CR↗