arXiv subjects

Mingshi Wu

Publications and source records attributed to Mingshi Wu.

3 recordsLinked to original sources

arXiv2026

Understanding the "Airport" Censorship Circumvention Ecosystem in China

In China, a burgeoning underground market sells citizens subscription-based censorship circumvention proxies known as ''airports''. We present the first systematic study of this ecosystem, combining user surveys, social media analysis, and active network measurements. We find that airports are by far the most popular off-the-shelf censorship circumvention tool in China, used by over half of our 1,667~survey respondents, who cite their ease of use, performance, and access to geo-restricted services like ChatGPT and Netflix. By scanning the Internet and scraping Telegram announcement channels, we identify 3,431 active airports built on a handful of open-source toolkits. We subscribe to 35 airports and characterize their performance, which often surpasses direct connections through the Great Firewall due to a distinctive multi-hop architecture. However, airports also pose new challenges and security risks: they accept payment through commercial services like Alipay, suffer frequent government takedowns, and are difficult for clients to configure optimally. Many airports also deploy their own distinct censorship policies. Airports are far more widely used than other circumvention tools from the academic literature, but introduce new forms of fragility and control, offering both lessons and opportunities for future circumvention research.

cs.CR↗

arXiv2026

CensorLess: Cost-Efficient Censorship Circumvention Through Serverless Cloud Functions

With the increase in Internet censorship globally, various circumvention tools have been designed and developed. However, the monetary cost of these tools deeply impacts both user choice and the sustainability of provider operations. Recent developments in censorship circumvention research attempted to achieve cost efficiency by utilizing Infrastructure-as-a-Service (IaaS) spot instances as bridges, but still incurred substantial expenses related to network connectivity and instance maintenance. In this work, we present CensorLess, a circumvention proxy built leveraging the unique benefits of a serverless platform. CensorLess comprises three components: a local proxy that handles client-side communication and ensures compliance with serverless functions' security restrictions, a function refresher that periodically regenerates bridges, and a live migration mechanism that maintains continuous connectivity. CensorLess inherits the serverless platform's cost efficiency, ephemerality, scalability, concurrency, and performance. Compared to existing low-cost, state-of-the-art circumvention techniques, CensorLess reduces costs by 97%, while simultaneously enabling robust censorship resistance by employing bridge rotation.

cs.CR↗

arXiv2025

R1dacted: Investigating Local Censorship in DeepSeek's R1 Language Model

DeepSeek recently released R1, a high-performing large language model (LLM) optimized for reasoning tasks. Despite its efficient training pipeline, R1 achieves competitive performance, even surpassing leading reasoning models like OpenAI's o1 on several benchmarks. However, emerging reports suggest that R1 refuses to answer certain prompts related to politically sensitive topics in China. While existing LLMs often implement safeguards to avoid generating harmful or offensive outputs, R1 represents a notable shift - exhibiting censorship-like behavior on politically charged queries. In this paper, we investigate this phenomenon by first introducing a large-scale set of heavily curated prompts that get censored by R1, covering a range of politically sensitive topics, but are not censored by other models. We then conduct a comprehensive analysis of R1's censorship patterns, examining their consistency, triggers, and variations across topics, prompt phrasing, and context. Beyond English-language queries, we explore censorship behavior in other languages. We also investigate the transferability of censorship to models distilled from the R1 language model. Finally, we propose techniques for bypassing or removing this censorship. Our findings reveal possible additional censorship integration likely shaped by design choices during training or alignment, raising concerns about transparency, bias, and governance in language model deployment.

cs.CL↗