arXiv subjects

Ram Sundara Raman

Publications and source records attributed to Ram Sundara Raman.

4 recordsLinked to original sources

arXiv2026

Evaluating Practical Enumeration and Blocking Attacks on the Snowflake Circumvention System

Proxy-based Internet censorship circumvention tools like Snowflake rely on large, dynamic pools of third-party proxies to resist IP-based blocking. We focus on two assumptions underpinning the security of Snowflake: that adversaries cannot easily enumerate proxy IPs, and that blocking those proxies would incur unacceptable collateral damage. In this paper, we test these assumptions by studying practical enumeration and blocking attacks against Snowflake conducted by malicious clients. We combine bounded, ethical real-world measurements with large-scale simulation to evaluate both present-day enumeration and blocking risk and broader attacker capabilities. Over 48 days of real-world measurements from May--June 2025, our attack enumerated over 21,000 unique proxy IP addresses belonging to almost 1,000 autonomous systems. Despite this high number, we find that proxy churn limits the overall effectiveness of enumeration over time, and reduces the impact on clients of individual proxy addresses being blocked. However, at the network level, blocking the top 1% of observed autonomous systems blocks more than 30% of observed Snowflakes while affecting 0% of Tranco Top 100 domains and ~2.5% of Top 1M domains. We discover that the broker's load-aware matching reveals stable, high-capacity proxies to attackers early, especially during periods of elevated demand such as the censorship even in Iran of June 2025, subsequently exposing the networks that contribute disproportionately to system connectivity. In simulation, increasing attacker scale sharply improves both enumeration and blocking success, while higher proxy churn significantly reduces blocking effectiveness. We conclude by discussing and evaluating practical mitigations, some of which have been integrated into Snowflake.

cs.CR↗

arXiv2026

PrivAudit: A Dual-Lens Auditing Framework for Website Privacy Practices under the CCPA

Five years after the enforcement of the California Consumer Privacy Act (CCPA), understanding how website privacy practices evolve at scale in response to regulation remains a key challenge for both researchers and regulators. Prior work and regulatory efforts have focused on manual and case-specific enforcement, but there remain no scalable approaches to systematically audit two key user-facing facets of websites that are crucial signals for the CCPA: privacy disclosures and front-end user tracking behavior. In this paper, we present PrivAudit, an automated auditing framework that adopts a dual-lens approach to capture: (1) privacy disclosures through large language model-based analysis of privacy policies grounded in CCPA provisions, and (2) user-observable data collection behavior through automated browser measurements of cookie writes under diverse privacy configurations. We apply PrivAudit to 998 websites and report two broad findings. The law is associated with stronger privacy disclosures: CCPA-subject policies are more likely to disclose opt-out mechanisms, data-sharing practices, and user rights. On the other hand, cookie-based tracking remains pervasive, with both CCPA-subject and not-subject websites setting a total of 6,392 targeting cookies, 49% of which are third-party writes. Moreover, cookies show limited-to-moderate responsiveness to privacy signals and consent choices, even when websites claim to honor them in their disclosures. Our results highlight the need for multi-layered and scalable auditing approaches that combine policy analysis with behavioral evidence. PrivAudit can support these auditing workflows at scale by generating actionable signals and patterns for further manual review. We open-source PrivAudit and are engaging with regulators to support auditing in practice.

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↗

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↗