arXiv subjects

Roya Ensafi

Publications and source records attributed to Roya Ensafi.

13 recordsLinked to original sources

arXiv2026

Don't Trust the Super-App: A Case Study of Russia's Max

Super-apps, an emerging mobile architecture, host third-party mini-apps inside a single app, allowing users to access diverse services. A decade of security research on the super-app ecosystem has all assumed super-apps to be a trusted intermediary. We argue this implicit trust is difficult to justify: China's WeChat is already shown to passively track its user's activity across mini-apps at extraordinary scale; Russia's MAX's parent company is reported to be deeply entangled with the state prosecution of online speech; and Iran's Bale was reported to be functioning in the world's longest internet shutdown due to its state-backed support. In this paper, we show how malicious super-apps have undeniable capabilities to silently undermine the security and privacy of mini-apps and users without leaving any trace. Using MAX as an example, we show how it can capture mini-app UI, read and write mini-app local storage, inject arbitrary JavaScript into a mini-app's runtime, mediate mini-app network traffic, and control authentication context in ways that can enable silent user impersonation. Sadly, these capabilities manifest themselves in any super-app because of the architectural privileges granted to them by design. We argue that mobile OS and app store interventions are urgently needed to close this architectural blind spot before it is further exploited.

cs.CR↗

arXiv2025

Fingerprinting Deep Packet Inspection Devices by Their Ambiguities

Users around the world face escalating network interference such as censorship, throttling, and interception, largely driven by the commoditization and growing availability of Deep Packet Inspection (DPI) devices. Once reserved for a few well-resourced nation-state actors, the ability to interfere with traffic at scale is now within reach of nearly any network operator. Despite this proliferation, our understanding of DPIs and their deployments on the Internet remains limited -- being network intermediary leaves DPI unresponsive to conventional host-based scanning tools, and DPI vendors actively obscuring their products further complicates measurement efforts. In this work, we present a remote measurement framework, dMAP (DPI Mapper), that derives behavioral fingerprints for DPIs to differentiate and cluster these otherwise indistinguishable middleboxes at scale, as a first step toward active reconnaissance of DPIs on the Internet. Our key insight is that parsing and interpreting traffic as network intermediaries inherently involves ambiguities -- from under-specified protocol behaviors to differing RFC interpretations -- forcing DPI vendors into independent implementation choices that create measurable variance among DPIs. Based on differential fuzzing, dMAP systematically discovers, selects, and deploys specialized probes that translate DPI internal parsing behaviors into externally observable fingerprints. Applying dMAP to DPI deployments globally, we demonstrate its practical feasibility, showing that even a modest set of 20-40 discriminative probes reliably differentiates a wide range of DPI implementations, including major nation-state censorship infrastructures and commercial DPI products. We discuss how our fingerprinting methodology generalizes beyond censorship to other forms of targeted interference.

cs.NI↗

arXiv2025

Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processing

The ability of Natural Language Processing (NLP) methods to categorize text into multiple classes has motivated their use in online content moderation tasks, such as hate speech and fake news detection. However, there is limited understanding of how or why these methods make such decisions, or why certain content is moderated in the first place. To investigate the hidden mechanisms behind content moderation, we explore multiple directions: 1) training classifiers to reverse-engineer content moderation decisions across countries; 2) explaining content moderation decisions by analyzing Shapley values and LLM-guided explanations. Our primary focus is on content moderation decisions made across countries, using pre-existing corpora sampled from the Twitter Stream Grab. Our experiments reveal interesting patterns in censored posts, both across countries and over time. Through human evaluations of LLM-generated explanations across three LLMs, we assess the effectiveness of using LLMs in content moderation. Finally, we discuss potential future directions, as well as the limitations and ethical considerations of this work. Our code and data are available at https://github.com/causalNLP/censorship

cs.CL↗

arXiv2024

OpenVPN is Open to VPN Fingerprinting

VPN adoption has seen steady growth over the past decade due to increased public awareness of privacy and surveillance threats. In response, certain governments are attempting to restrict VPN access by identifying connections using "dual use" DPI technology. To investigate the potential for VPN blocking, we develop mechanisms for accurately fingerprinting connections using OpenVPN, the most popular protocol for commercial VPN services. We identify three fingerprints based on protocol features such as byte pattern, packet size, and server response. Playing the role of an attacker who controls the network, we design a two-phase framework that performs passive fingerprinting and active probing in sequence. We evaluate our framework in partnership with a million-user ISP and find that we identify over 85% of OpenVPN flows with only negligible false positives, suggesting that OpenVPN-based services can be effectively blocked with little collateral damage. Although some commercial VPNs implement countermeasures to avoid detection, our framework successfully identified connections to 34 out of 41 "obfuscated" VPN configurations. We discuss the implications of the VPN fingerprintability for different threat models and propose short-term defenses. In the longer term, we urge commercial VPN providers to be more transparent about their obfuscation approaches and to adopt more principled detection countermeasures, such as those developed in censorship circumvention research.

cs.CR↗

arXiv2023

CERTainty: Detecting DNS Manipulation at Scale using TLS Certificates

DNS manipulation is an increasingly common technique used by censors and other network adversaries to prevent users from accessing restricted Internet resources and hijack their connections. Prior work in detecting DNS manipulation relies largely on comparing DNS resolutions with trusted control results to identify inconsistencies. However, the emergence of CDNs and other cloud providers practicing content localization and load balancing leads to these heuristics being inaccurate, paving the need for more verifiable signals of DNS manipulation. In this paper, we develop a new technique, CERTainty, that utilizes the widely established TLS certificate ecosystem to accurately detect DNS manipulation, and obtain more information about the adversaries performing such manipulation. We find that untrusted certificates, mismatching hostnames, and blockpages are powerful proxies for detecting DNS manipulation. Our results show that previous work using consistency-based heuristics is inaccurate, allowing for 72.45% false positives in the cases detected as DNS manipulation. Further, we identify 17 commercial DNS filtering products in 52 countries, including products such as SafeDNS, SkyDNS, and Fortinet, and identify the presence of 55 ASes in 26 countries that perform ISP-level DNS manipulation. We also identify 226 new blockpage clusters that are not covered by previous research. We are integrating techniques used by CERTainty into active measurement platforms to continuously and accurately monitor DNS manipulation.

cs.CR↗

arXiv2022

"All of them claim to be the best": Multi-perspective study of VPN users and VPN providers

As more users adopt VPNs for a variety of reasons, it is important to develop empirical knowledge of their needs and mental models of what a VPN offers. Moreover, studying VPN users alone is not enough because, by using a VPN, a user essentially transfers trust, say from their network provider, onto the VPN provider. To that end, we are the first to study the VPN ecosystem from both the users' and the providers' perspectives. In this paper, we conduct a quantitative survey of 1,252 VPN users in the U.S. and qualitative interviews of nine providers to answer several research questions regarding the motivations, needs, threat model, and mental model of users, and the key challenges and insights from VPN providers. We create novel insights by augmenting our multi-perspective results, and highlight cases where the user and provider perspectives are misaligned. Alarmingly, we find that users rely on and trust VPN review sites, but VPN providers shed light on how these sites are mostly motivated by money. Worryingly, we find that users have flawed mental models about the protection VPNs provide, and about data collected by VPNs. We present actionable recommendations for technologists and security and privacy advocates by identifying potential areas on which to focus efforts and improve the VPN ecosystem.

cs.CR↗

arXiv2022

A Study of Third-party Resources Loading on Web

This paper performs a large-scale study of dependency chains in the web, to find that around 50% of first-party websites render content that they did not directly load. Although the majority (84.91%) of websites have short dependency chains (below 3 levels), we find websites with dependency chains exceeding 30. Using VirusTotal, we show that 1.2% of these third-parties are classified as suspicious -- although seemingly small, this limited set of suspicious third-parties have remarkable reach into the wider ecosystem. We find that 73% of websites under-study load resources from suspicious third-parties, and 24.8% of first-party webpages contain at least three third-parties classified as suspicious in their dependency chain. By running sandboxed experiments, we observe a range of activities with the majority of suspicious JavaScript codes downloading malware.

cs.CR↗

arXiv2021

Challenges in cybersecurity: Lessons from biological defense systems

We explore the commonalities between methods for assuring the security of computer systems (cybersecurity) and the mechanisms that have evolved through natural selection to protect vertebrates against pathogens, and how insights derived from studying the evolution of natural defenses can inform the design of more effective cybersecurity systems. More generally, security challenges are crucial for the maintenance of a wide range of complex adaptive systems, including financial systems, and again lessons learned from the study of the evolution of natural defenses can provide guidance for the protection of such systems.

cs.CY↗

arXiv2019

The Chain of Implicit Trust: An Analysis of the Web Third-party Resources Loading

The Web is a tangled mass of interconnected services, where websites import a range of external resources from various third-party domains. However, the latter can further load resources hosted on other domains. For each website, this creates a dependency chain underpinned by a form of implicit trust between the first-party and transitively connected third-parties. The chain can only be loosely controlled as first-party websites often have little, if any, visibility of where these resources are loaded from. This paper performs a large-scale study of dependency chains in the Web, to find that around 50% of first-party websites render content that they did not directly load. Although the majority (84.91%) of websites have short dependency chains (below 3 levels), we find websites with dependency chains exceeding 30. Using VirusTotal, we show that 1.2% of these third-parties are classified as suspicious --- although seemingly small, this limited set of suspicious third-parties have remarkable reach into the wider ecosystem. By running sandboxed experiments, we observe a range of activities with the majority of suspicious JavaScript downloading malware; worryingly, we find this propensity is greater among implicitly trusted JavaScripts.

cs.CR↗

arXiv2016

Characterizing and Avoiding Routing Detours Through Surveillance States

An increasing number of countries are passing laws that facilitate the mass surveillance of Internet traffic. In response, governments and citizens are increasingly paying attention to the countries that their Internet traffic traverses. In some cases, countries are taking extreme steps, such as building new Internet Exchange Points (IXPs), which allow networks to interconnect directly, and encouraging local interconnection to keep local traffic local. We find that although many of these efforts are extensive, they are often futile, due to the inherent lack of hosting and route diversity for many popular sites. By measuring the country-level paths to popular domains, we characterize transnational routing detours. We find that traffic is traversing known surveillance states, even when the traffic originates and ends in a country that does not conduct mass surveillance. Then, we investigate how clients can use overlay network relays and the open DNS resolver infrastructure to prevent their traffic from traversing certain jurisdictions. We find that 84\% of paths originating in Brazil traverse the United States, but when relays are used for country avoidance, only 37\% of Brazilian paths traverse the United States. Using the open DNS resolver infrastructure allows Kenyan clients to avoid the United States on 17\% more paths. Unfortunately, we find that some of the more prominent surveillance states (e.g., the U.S.) are also some of the least avoidable countries.

cs.NI↗

arXiv2016

Identifying and characterizing Sybils in the Tor network

Being a volunteer-run, distributed anonymity network, Tor is vulnerable to Sybil attacks. Little is known about real-world Sybils in the Tor network, and we lack practical tools and methods to expose Sybil attacks. In this work, we develop sybilhunter, the first system for detecting Sybil relays based on their appearance, such as configuration; and behavior, such as uptime sequences. We used sybilhunter's diverse analysis techniques to analyze nine years of archived Tor network data, providing us with new insights into the operation of real-world attackers. Our findings include diverse Sybils, ranging from botnets, to academic research, and relays that hijack Bitcoin transactions. Our work shows that existing Sybil defenses do not apply to Tor, it delivers insights into real-world attacks, and provides practical tools to uncover and characterize Sybils, making the network safer for its users.

cs.CR↗

arXiv2014

Large-scale Spatiotemporal Characterization of Inconsistencies in the World's Largest Firewall

A nation-scale firewall, colloquially referred to as the "Great Firewall of China," implements many different types of censorship and content filtering to control China's Internet traffic. Past work has shown that the firewall occasionally fails. In other words, sometimes clients in China are able to reach blacklisted servers outside of China. This phenomenon has not yet been characterized because it is infeasible to find a large and geographically diverse set of clients in China from which to test connectivity. In this paper, we overcome this challenge by using hybrid idle scan techniques that are able to measure connectivity between a remote client and an arbitrary server, neither of which are under the control of the researcher performing measurements. In addition to hybrid idle scans, we present and employ a novel side channel in the Linux kernel's SYN backlog. We demonstrate both techniques by measuring the reachability of the Tor network which is known to be blocked in China. Our measurements reveal that 1) failures in the firewall occur throughout the entire country without any conspicuous geographical patterns, 2) a network block in China appears to have unfiltered access to parts of the Tor network, and 3) the filtering seems to be mostly centralized at the level of Internet exchange points. Our work also answers many other open questions about the Great Firewall's architecture and implementation.

cs.NI↗

arXiv2013

Detecting Intentional Packet Drops on the Internet via TCP/IP Side Channels: Extended Version

We describe a method for remotely detecting intentional packet drops on the Internet via side channel inferences. That is, given two arbitrary IP addresses on the Internet that meet some simple requirements, our proposed technique can discover packet drops (e.g., due to censorship) between the two remote machines, as well as infer in which direction the packet drops are occurring. The only major requirements for our approach are a client with a global IP Identifier (IPID) and a target server with an open port. We require no special access to the client or server. Our method is robust to noise because we apply intervention analysis based on an autoregressive-moving-average (ARMA) model. In a measurement study using our method featuring clients from multiple continents, we observed that, of all measured client connections to Tor directory servers that were censored, 98% of those were from China, and only 0.63% of measured client connections from China to Tor directory servers were not censored. This is congruent with current understandings about global Internet censorship, leading us to conclude that our method is effective.

cs.NI↗