arXiv subjects

Sulyab Thottungal Valapu

Publications and source records attributed to Sulyab Thottungal Valapu.

6 recordsLinked to original sources

arXiv2026

Zombies in Alternate Realities: The Afterlife of Domain Names in DNS Integrations

DNS integrations leverage the discovery, trust, and uniqueness of the global Domain Name System with a linkage to another naming ecosystem, so the DNS name can help identify resources such as a cryptocurrency wallet or software component. While DNS ownership is verified at linkage creation, many ecosystems do not track subsequent DNS changes. The result is zombie linkages, where the DNS ownership has expired or changed, but the mapping to the linked resource persists. We define a threat model for DNS integrations, identifying five classes of attacks that leverage or exploit zombie linkages. We measure zombie occurrence across three DNS integrations -- Web PKI; ENS, a blockchain naming system; and Maven Central, a Java software repository. We show that zombies exist in every ecosystem, but at very different fractions -- zombies make up roughly 3% of TLS certificates for new domains, 24% of ENS on-chain imports, and 15% of Maven Central namespaces. We evaluate how integration design choices affect outcomes, with validate-once integrations (ENS on-chain, Maven Central) accumulating long-lasting zombies, linkages with expiration (Web PKI) limiting damage, while integrations that validate on every use (ENS gasless) are zombie-free by design. We look for specific attacks, finding attacks actively available for exploitation in both Web PKI and Maven Central. Finally, we recommend steps to reduce zombie occurrence.

cs.CR↗

arXiv2025

Towards a Non-Binary View of IPv6 Adoption

Twelve years have passed since World IPv6 Launch Day, but what is the current state of IPv6 deployment? Prior work has examined IPv6 status as a binary: can a user do any IPv6? As deployment increases, we must consider a more nuanced, non-binary perspective on IPv6: how much and often can a user or a service use IPv6? We consider this question as a client, server, and cloud provider. Considering the client's perspective, we observe user traffic. We see that the fraction of IPv6 traffic a user sends varies greatly, both across users and day-by-day, with a standard deviation of over 15%. We show this variation occurs for two main reasons. First, IPv6 traffic is primarily human-generated, thus showing diurnal patterns. Second, some services lead with full IPv6 adoption, while others lag with partial or no support, so as users do different things their fraction of IPv6 varies. We look at server-side IPv6 adoption in two ways. First, we expand analysis of web services to examine how many are only partially IPv6 enabled due to their reliance on IPv4-only resources. Our findings reveal that only 12.6% of top 100k websites qualify as fully IPv6-ready. Finally, we examine cloud support for IPv6. Although all clouds and CDNs support IPv6, we find that tenant deployment rates vary significantly across providers. We find that ease of enabling IPv6 in the cloud is correlated with tenant IPv6 adoption rates, and recommend best practices for cloud providers to improve IPv6 adoption. Our results suggest IPv6 deployment is growing, but many services lag, presenting a potential for improvement.

cs.NI↗

arXiv2025

Crowd-SFT: Crowdsourcing for LLM Alignment

Large Language Models (LLMs) increasingly rely on Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) to align model responses with human preferences. While RLHF employs a reinforcement learning approach with a separate reward model, SFT uses human-curated datasets for supervised learning. Both approaches traditionally depend on small, vetted groups of annotators, making them costly, prone to bias, and limited in scalability. We propose an open, crowd-sourced fine-tuning framework that addresses these limitations by enabling broader feedback collection for SFT without extensive annotator training. Our framework promotes incentive fairness via a point-based reward system correlated with Shapley values and guides model convergence through iterative model updates. Our multi-model selection framework demonstrates up to a 55% reduction in target distance over single-model selection, enabling subsequent experiments that validate our point-based reward mechanism's close alignment with Shapley values (a well-established method for attributing individual contributions) thereby supporting fair and scalable participation.

cs.HC↗

arXiv2025

Geofeed Adoption and Authentication

IP Geofeed is a recently proposed informational standard that allows network operators to publish the geographical location of deployed IPv4 and IPv6 prefixes. In this work we study the adoption of IP geofeed, assess deployment of geofeed at Regional Internet Registry and Autonomous System levels, and analyze adherence to RFC 8805 and RFC 9092 in deployed geofeeds. We evaluate the authentication mechanism proposed in RFC 9092 and find that it lacks key features from a security perspective. We propose a novel approach to simplify the authentication of geofeeds and assess its efficiency using different benchmarks. Our findings highlight the challenges in current geofeed adoption and the potential for improving both security and scalability in geofeed validation processes.

cs.NI↗

arXiv2024

Reward-based Blockchain Infrastructure for 3D IC Supply Chain Provenance

In response to the growing demand for enhanced performance and power efficiency, the semiconductor industry has witnessed a paradigm shift toward heterogeneous integration, giving rise to 2.5D/3D chips. These chips incorporate diverse chiplets, manufactured globally and integrated into a single chip. Securing these complex 2.5D/3D integrated circuits (ICs) presents a formidable challenge due to inherent trust issues within the semiconductor supply chain. Chiplets produced in untrusted locations may be susceptible to tampering, introducing malicious circuits that could compromise sensitive information. This paper introduces an innovative approach that leverages blockchain technology to establish traceability for ICs and chiplets throughout the supply chain. Given that chiplet manufacturers are dispersed globally and may operate within different blockchain consortiums, ensuring the integrity of data within each blockchain ledger becomes imperative. To address this, we propose a novel dual-layer approach for establishing distributed trust across diverse blockchain ledgers. The lower layer comprises of a blockchain-based framework for IC supply chain provenance that enables transactions between blockchain instances run by different consortiums, making it possible to trace the complete provenance DAG of each IC. The upper layer implements a multi-chain reputation scheme that assigns reputation scores to entities while specifically accounting for high-risk transactions that cross blockchain trust zones. This approach enhances the credibility of the blockchain data, mitigating potential risks associated with the use of multiple consortiums and ensuring a robust foundation for securing 2.5D/3D ICs in the evolving landscape of heterogeneous integration.

cs.CR↗

arXiv2023

DARSAN: A Decentralized Review System Suitable for NFT Marketplaces

We introduce DARSAN, a decentralized review system designed for Non-Fungible Token (NFT) marketplaces, to address the challenge of verifying the quality of highly resalable products with few verified buyers by incentivizing unbiased reviews. DARSAN works by iteratively selecting a group of reviewers (called ``experts'') who are likely to both accurately predict the objective popularity and assess some subjective quality of the assets uniquely associated with NFTs. The system consists of a two-phased review process: a ``pre-listing'' phase where only experts can review the product, and a ``pre-sale'' phase where any reviewer on the system can review the product. Upon completion of the sale, DARSAN distributes incentives to the participants and selects the next generation of experts based on the performance of both experts and non-expert reviewers. We evaluate DARSAN through simulation and show that, once bootstrapped with an initial set of appropriately chosen experts, DARSAN favors honest reviewers and improves the quality of the expert pool over time without any external intervention even in the presence of potentially malicious participants.

cs.CY↗