arXiv subjects

Jiangshan Yu

Publications and source records attributed to Jiangshan Yu.

At least 19 recordsLinked to original sources

arXiv2026

JUNO: Aggregated Vector Consensus for Optimal Asynchronous Common Subset

In this paper, we propose \textit{aggregated vector consensus}, a new vector consensus primitive designed for asynchronous networks. The primitive achieves agreement by outputting a vector of values aggregated from independent process inputs. We then introduce \textsc{Juno}, an asynchronous common subset (ACS) protocol that fully implements our aggregated vector consensus to attain optimal $\mathcal{O}(n^2)$ message complexity. We further implement and evaluate \textsc{Juno} in comparison with the legacy HoneyBadgerBFT and the state-of-the-art Dory. Experiment results demonstrate its efficacy and efficiency. Our protocol demonstrates an average throughput performance improvement of 93\% compared with HoneyBadgerBFT and a 47\% improvement compared with Dory. Notably, our study makes significant progress in addressing the gap in applying vector consensus protocol in fully asynchronous networks.

cs.CR↗

arXiv2026

SoK: Blockchain Agent-to-Agent Payments

Agentic AI rivals human capabilities across a wide range of domains. Looking ahead, it is foreseeable that AI agents will autonomously handle complex workflows and interactions. Early prototypes of this paradigm are emerging, e.g., OpenClaw and Moltbook, signaling a shift toward Agent-to-Agent (A2A) ecosystems. However, despite these promising blueprints, critical trust and security challenges remain, particularly in scenarios involving financial transactions. Ensuring secure and reliable payment mechanisms between unknown and untrusted agents is crucial to complete a fully functional and trustworthy A2A ecosystem. Although blockchain-based infrastructures provide a natural foundation for this setting, via programmable settlement, transparent accounting, and open interoperability, trust and security challenges have not yet been fully addressed. Hence, for the first time, we systematize blockchain-based A2A payments, e.g., X402, with a four-stage lifecycle: discovery, authorization, execution, and accounting. We categorize representative designs at each stage and identify key challenges, including weak intent binding, misuse under valid authorization, payment-service decoupling, and limited accountability. We highlight future directions for strengthening cross-stage consistency, enabling behavior-aware control, and supporting compositional payment workflows across agents and systems.

q-fin.GN↗

arXiv2026

TxRay: Agentic Postmortem of Live Blockchain Attacks

Decentralized Finance (DeFi) has turned blockchains into financial infrastructure, allowing anyone to trade, lend, and build protocols without intermediaries, but this openness exposes pools of value controlled by code. Within five years, the DeFi ecosystem has lost over 15.75B USD to reported exploits. Many exploits arise from permissionless opportunities that any participant can trigger using only public state and standard interfaces, which we call Anyone-Can-Take (ACT) opportunities. Despite on-chain transparency, postmortem analysis remains slow and manual: investigations start from limited evidence, sometimes only a single transaction hash, and must reconstruct the exploit lifecycle by recovering related transactions, contract code, and state dependencies. We present TxRay, a Large Language Model (LLM) agentic postmortem system that uses tool calls to reconstruct live ACT attacks from limited evidence. Starting from one or more seed transactions, TxRay recovers the exploit lifecycle, derives an evidence-backed root cause, and generates a runnable, self-contained Proof of Concept (PoC) that deterministically reproduces the incident. TxRay self-checks postmortems by encoding incident-specific semantic oracles as executable assertions. To evaluate PoC correctness and quality, we develop PoCEvaluator, an independent agentic execution-and-review evaluator. On 114 incidents from DeFiHackLabs, TxRay produces an expert-aligned root cause and an executable PoC for 105 incidents, achieving 92.11% end-to-end reproduction. Under PoCEvaluator, 98.1% of TxRay PoCs avoid hard-coding attacker addresses, a +22.9pp lift over DeFiHackLabs. In a live deployment, TxRay delivers validated root causes in 40 minutes and PoCs in 59 minutes at median latency. TxRay's oracle-validated PoCs enable attack imitation, improving coverage by 15.6% and 65.5% over STING and APE.

cs.CR↗

arXiv2026

SoK: Stablecoins in Retail Payments

Stablecoins have emerged as a rapidly growing digital payment instrument, raising the question of whether blockchain-based settlement can function as a substitute for incumbent card networks in retail payments. This Systematization of Knowledge (SoK) provides a systematic comparison between stablecoin payment arrangements and card networks by situating both within a unified analytical framework. We first map their respective payment infrastructures, participant roles, and transaction lifecycles, highlighting fundamental differences in how authorization, settlement, and recourse are organized. Building on this mapping, we introduce the CLEAR framework, which evaluates retail payment systems across five dimensions: cost, legality, experience, architecture, and reach. Our analysis shows that stablecoins deliver efficient, continuous, and programmable settlement, often compressing rail-level merchant fees and enabling 24/7 value transfer. However, these advantages are accompanied by an inversion of the traditional pricing and risk-allocation structure. Card networks internalize consumer-side frictions through subsidies, standardized liability rules, and post-transaction recourse, thereby supporting mass-market adoption. Stablecoin arrangements, by contrast, externalize transaction fees, error prevention, and dispute resolution to users, intermediaries, and courts, resulting in weaker consumer protection, higher cognitive burden at the point of interaction, and fragmented acceptance. Accordingly, stablecoins exhibit a conditional comparative advantage in closed-loop environments, cross-border corridors, and high-friction payment contexts, but remain structurally disadvantaged as open-loop retail payment instruments.

q-fin.GN↗

arXiv2025

Leveraging Large Language Models to Bridge Cross-Domain Transparency in Stablecoins

Stablecoins such as USDT and USDC aspire to peg stability by coupling issuance controls with reserve attestations. In practice, however, transparency remains fragmented across heterogeneous data sources, with key evidence about circulation, reserves, and disclosure dispersed across records that are difficult to connect and interpret jointly. We introduce a large language model (LLM)-based automated framework for bridging cross-domain transparency in stablecoins by aligning issuer disclosures with observable circulation evidence. First, we propose an integrative framework using LLMs to parse documents, extract salient financial indicators, and semantically align reported statements with corresponding market and issuance metrics. Second, we integrate multi-chain issuance records and disclosure documents within a model context protocol (MCP) framework that standardizes LLM access to both quantitative market data and qualitative disclosure narratives. This framework enables unified retrieval and contextual alignment across heterogeneous stablecoin information sources and facilitates consistent analysis. Third, we demonstrate the capability of LLMs to operate across heterogeneous data domains in blockchain analytics, quantifying discrepancies between reported and observed circulation and examining their implications for transparency and price dynamics. Our findings reveal systematic gaps between disclosed and verifiable data, showing that LLM-assisted analysis enhances cross-domain transparency and supports automated, data-driven auditing in decentralized finance (DeFi).

cs.CR↗

arXiv2025

Measuring Memecoin Fragility

Memecoins, emerging from internet culture and community-driven narratives, have rapidly evolved into a unique class of crypto assets. Unlike technology-driven cryptocurrencies, their market dynamics are primarily shaped by viral social media diffusion, celebrity influence, and speculative capital inflows. To capture the distinctive vulnerabilities of these ecosystems, we present the first Memecoin Ecosystem Fragility Framework (ME2F). ME2F formalizes memecoin risks in three dimensions: i) Volatility Dynamics Score capturing persistent and extreme price swings together with spillover from base chains; ii) Whale Dominance Score quantifying ownership concentration among top holders; and iii) Sentiment Amplification Score measuring the impact of attention-driven shocks on market stability. We apply ME2F to representative tokens (over 65% market share) and show that fragility is not evenly distributed across the ecosystem. Politically themed tokens such as TRUMP, MELANIA, and LIBRA concentrate the highest risks, combining volatility, ownership concentration, and sensitivity to sentiment shocks. Established memecoins such as DOGE, SHIB, and PEPE fall into an intermediate range. Benchmark tokens ETH and SOL remain consistently resilient due to deeper liquidity and institutional participation. Our findings provide the first ecosystem-level evidence of memecoin fragility and highlight governance implications for enhancing market resilience in the Web3 era.

cs.CR↗

arXiv2025

An Incentive-Compatible Reward Sharing Mechanism for Mitigating Mirroring Attacks in Decentralized Data-Feed Systems

Decentralized data-feed systems enable blockchain-based smart contracts to access off-chain information by aggregating values from multiple oracles. To improve accuracy, these systems typically use an aggregation function, such as majority voting, to consolidate the inputs they receive from oracles and make a decision. Depending on the final decision and the values reported by the oracles, the participating oracles are compensated through shared rewards. However, such incentive mechanisms are vulnerable to mirroring attacks, where a single user controls multiple oracles to bias the decision of the aggregation function and maximize rewards. This paper analyzes the impact of mirroring attacks on the reliability and dependability of majority voting-based data-feed systems. We demonstrate how existing incentive mechanisms can unintentionally encourage rational users to implement such attacks. To address this, we propose a new incentive mechanism that discourages Sybil behavior. We prove that the proposed mechanism leads to a Nash Equilibrium in which each user operates only one oracle. Finally, we discuss the practical implementation of the proposed incentive mechanism and provide numerical examples to demonstrate its effectiveness.

cs.GT↗

arXiv2025

Mosaic: Client-driven Account Allocation Framework in Sharded Blockchains

Recent account allocation studies in sharded blockchains are typically miner-driven, requiring miners to perform global optimizations for all accounts to enhance system-wide performance. This forces each miner to maintain a complete copy of the entire ledger, resulting in significant storage, communication, and computation overhead. In this work, we explore an alternative research direction by proposing Mosaic, the first client-driven framework for distributed, lightweight local optimization. Rather than relying on miners to allocate all accounts, Mosaic enables clients to independently execute a local algorithm to determine their residing shards. Clients can submit migration requests to a beacon chain when relocation is necessary. Mosaic naturally addresses key limitations of miner-driven approaches, including the lack of miner incentives and the significant overhead. While clients are flexible to adopt any algorithm for shard allocation, we design and implement a reference algorithm, Pilot, to guide them. Clients execute Pilot to maximize their own benefits, such as reduced transaction fees and confirmation latency. On a real-world Ethereum dataset, we implement and evaluate Pilot against state-of-the-art miner-driven global optimization solutions. The results demonstrate that Mosaic significantly enhances computational efficiency, achieving a four-order-of-magnitude reduction in computation time, with the reduced input data size from 1.44 GB to an average of 228.66 bytes per account. Despite these efficiency gains, Pilot introduces only about a 5% increase in the cross-shard ratio and maintains approximately 98% of the system throughput, demonstrating a minimal trade-off in overall effectiveness.

cs.DC↗

arXiv2025

Generative Large Language Model usage in Smart Contract Vulnerability Detection

Recent years have seen an explosion of activity in Generative AI, specifically Large Language Models (LLMs), revolutionising applications across various fields. Smart contract vulnerability detection is no exception; as smart contracts exist on public chains and can have billions of dollars transacted daily, continuous improvement in vulnerability detection is crucial. This has led to many researchers investigating the usage of generative large language models (LLMs) to aid in detecting vulnerabilities in smart contracts. This paper presents a systematic review of the current LLM-based smart contract vulnerability detection tools, comparing them against traditional static and dynamic analysis tools Slither and Mythril. Our analysis highlights key areas where each performs better and shows that while these tools show promise, the LLM-based tools available for testing are not ready to replace more traditional tools. We conclude with recommendations on how LLMs are best used in the vulnerability detection process and offer insights for improving on the state-of-the-art via hybrid approaches and targeted pre-training of much smaller models.

cs.CR↗

arXiv2025

Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Study

Cryptocurrencies are widely used, yet current methods for analyzing transactions often rely on opaque, black-box models. While these models may achieve high performance, their outputs are usually difficult to interpret and adapt, making it challenging to capture nuanced behavioral patterns. Large language models (LLMs) have the potential to address these gaps, but their capabilities in this area remain largely unexplored, particularly in cybercrime detection. In this paper, we test this hypothesis by applying LLMs to real-world cryptocurrency transaction graphs, with a focus on Bitcoin, one of the most studied and widely adopted blockchain networks. We introduce a three-tiered framework to assess LLM capabilities: foundational metrics, characteristic overview, and contextual interpretation. This includes a new, human-readable graph representation format, LLM4TG, and a connectivity-enhanced transaction graph sampling algorithm, CETraS. Together, they significantly reduce token requirements, transforming the analysis of multiple moderately large-scale transaction graphs with LLMs from nearly impossible to feasible under strict token limits. Experimental results demonstrate that LLMs have outstanding performance on foundational metrics and characteristic overview, where the accuracy of recognizing most basic information at the node level exceeds 98.50% and the proportion of obtaining meaningful characteristics reaches 95.00%. Regarding contextual interpretation, LLMs also demonstrate strong performance in classification tasks, even with very limited labeled data, where top-3 accuracy reaches 72.43% with explanations. While the explanations are not always fully accurate, they highlight the strong potential of LLMs in this domain. At the same time, several limitations persist, which we discuss along with directions for future research.

cs.CR↗

arXiv2024

Detect Llama -- Finding Vulnerabilities in Smart Contracts using Large Language Models

In this paper, we test the hypothesis that although OpenAI's GPT-4 performs well generally, we can fine-tune open-source models to outperform GPT-4 in smart contract vulnerability detection. We fine-tune two models from Meta's Code Llama and a dataset of 17k prompts, Detect Llama - Foundation and Detect Llama - Instruct, and we also fine-tune OpenAI's GPT-3.5 Turbo model (GPT-3.5FT). We then evaluate these models, plus a random baseline, on a testset we develop against GPT-4, and GPT-4 Turbo's, detection of eight vulnerabilities from the dataset and the two top identified vulnerabilities - and their weighted F1 scores. We find that for binary classification (i.e., is this smart contract vulnerable?), our two best-performing models, GPT-3.5FT and Detect Llama - Foundation, achieve F1 scores of $0.776$ and $0.68$, outperforming both GPT-4 and GPT-4 Turbo, $0.66$ and $0.675$. For the evaluation against individual vulnerability identification, our top two models, GPT-3.5FT and Detect Llama - Foundation, both significantly outperformed GPT-4 and GPT-4 Turbo in both weighted F1 for all vulnerabilities ($0.61$ and $0.56$ respectively against GPT-4's $0.218$ and GPT-4 Turbo's $0.243$) and weighted F1 for the top two identified vulnerabilities ($0.719$ for GPT-3.5FT, $0.674$ for Detect Llama - Foundation against GPT-4's $0.363$ and GPT-4 Turbo's $0.429$).

cs.CR↗

arXiv2023

The Dark Side of NFTs: A Large-Scale Empirical Study of Wash Trading

NFTs (Non-Fungible Tokens) have seen significant growth since they first captured public attention in 2021. However, the NFT market is plagued by fake transactions and economic bubbles, e.g., NFT wash trading. Wash trading typically refers to a transaction involving the same person or two colluding individuals, and has become a major threat to the NFT ecosystem. Previous studies only detect NFT wash trading from the financial aspect, while the real-world wash trading cases are much more complicated (e.g., not aiming at inflating the market value). There is still a lack of multi-dimension analysis to better understand NFT wash trading. Therefore, we present the most comprehensive study of NFT wash trading, analyzing 8,717,031 transfer events and 3,830,141 sale events from 2,701,883 NFTs. We first optimize the dataset collected via the OpenSea API. Next, we identify three types of NFT wash trading and propose identification algorithms. Our experimental results reveal 824 transfer events and 5,330 sale events (accounting for a total of \$8,857,070.41) and 370 address pairs related to NFT wash trading behaviors, causing a minimum loss of \$3,965,247.13. Furthermore, we provide insights from six aspects, i.e., marketplace design, profitability, NFT project design, payment token, user behavior, and NFT ecosystem.

cs.CR↗

arXiv2023

Vivisecting the Dissection: On the Role of Trusted Components in BFT Protocols

A recent paper by Gupta et al. (EuroSys'23) challenged the usefulness of trusted component (TC) based Byzantine fault-tolerant (BFT) protocols to lower the replica group size from $3f+1$ to $2f+1$, identifying three limitations of such protocols and proposing that TCs should be used instead to improve the performance of BFT protocols. Here, we point out flaws in both arguments and advocate that the most worthwhile use of TCs in BFT protocols is indeed to make them as resilient as crash fault-tolerant (CFT) protocols, which can tolerate up to $f$ faulty replicas using $2f+1$ replicas.

cs.DC↗

arXiv2023

Towards Scalable Cross-Chain Messaging

Blockchains were originally designed as closed execution environments and lack the ability to communicate directly with external systems. To overcome this limitation, many blockchains employ relayers, external applications capable of transporting data between different blockchains. Typically, the process of relaying data is permissionless and multiple independent relayers work concurrently to transport the same information between two blockchains. While this model increases the reliability of data delivery by providing redundancy, it also introduces challenges that have not been previously discussed. In this work, we bridge this gap by discussing the shortcomings of permissionless cross-chain relaying systems and identifying three issues that adversely impact their performance, scalability and security. We take the first step towards addressing issues that hinder performance and scalability by proposing a novel protocol to enable coordination among independent relayers. Additionally, we provide an in-depth discussion about the trade-offs associated with the design of relayer coordination protocols for permissionless settings. Through this work we provide a foundation for improving cross-chain relaying services.

cs.DC↗

arXiv2023

New Bounds on the Accuracy of Majority Voting for Multi-Class Classification

Majority voting is a simple mathematical function that returns the value that appears most often in a set. As a popular decision fusion technique, the majority voting function (MVF) finds applications in resolving conflicts, where a number of independent voters report their opinions on a classification problem. Despite its importance and its various applications in ensemble learning, data crowd-sourcing, remote sensing, and data oracles for blockchains, the accuracy of the MVF for the general multi-class classification problem has remained unknown. In this paper, we derive a new upper bound on the accuracy of the MVF for the multi-class classification problem. More specifically, we show that under certain conditions, the error rate of the MVF exponentially decays toward zero as the number of independent voters increases. Conversely, the error rate of the MVF exponentially grows with the number of independent voters if these conditions are not met. We first explore the problem for independent and identically distributed voters where we assume that every voter follows the same conditional probability distribution of voting for different classes, given the true classification of the data point. Next, we extend our results for the case where the voters are independent but non-identically distributed. Using the derived results, we then provide a discussion on the accuracy of the truth discovery algorithms. We show that in the best-case scenarios, truth discovery algorithms operate as an amplified MVF and thereby achieve a small error rate only when the MVF achieves a small error rate, and vice versa, achieve a large error rate when the MVF also achieves a large error rate. In the worst-case scenario, the truth discovery algorithms may achieve a higher error rate than the MVF. Finally, we confirm our theoretical results using numerical simulations.

stat.ML↗

arXiv2023

Fault Independence in Blockchain

Byzantine Fault-Tolerant (BFT) protocols have been proposed to tolerate malicious behaviors in state machine replications. With classic BFT protocols, the total number of replicas is known and fixed a priori. The resilience of BFT protocols, i.e., the number of tolerated Byzantine replicas (denoted f ), is derived from the total number of replicas according to the quorum theory. To guarantee that an attacker cannot control more than f replicas, so to guarantee safety, it is vital to ensure fault independence among all replicas. This in practice is achieved by enforcing diverse configurations of replicas, i.e., each replica has a unique configuration, avoiding f fault compromises more than f replicas. While managing replica diversity in BFT protocols has been studied in permissioned environments with a small number of replicas, no prior work has discussed the fault independence in a permissionless environment (such as public blockchains) where anyone can join and leave the system at any time. This is particularly challenging due to the following two facts. First, with permissionless environment, any one can join as a replica at any time and no global coordinator can be relied on to manage replica diversity. Second, while great progress has been made to scale consensus algorithms to thousands of replicas, the replica diversity cannot provide fault independence at this scale, limiting practical and meaningful resilience. This paper provides the first discussion on the impact of fault independence on permissionless blockchains, provides discussions on replica configuration diversity, quantifies replica diversity by using entropy, and defines optimal fault independence.

cs.DC↗

arXiv2023

Analyzing the Performance of the Inter-Blockchain Communication Protocol

With the increasing demand for communication between blockchains, improving the performance of cross-chain communication protocols becomes an emerging challenge. We take a first step towards analyzing the limitations of cross-chain communication protocols by comprehensively evaluating Cosmos Network's Inter-Blockchain Communication Protocol. To achieve our goal we introduce a novel framework to guide empirical evaluations of cross-chain communication protocols. We implement an instance of our framework as a tool to evaluate the IBC protocol. Our findings highlight several challenges, such as high transaction confirmation latency, bottlenecks in the blockchain's RPC implementation and concurrency issues that hinder the scalability of the cross-chain message relayer. We also demonstrate how to reduce the time required to complete cross-chain transfers by up to 70% when submitting large amounts of transfers. Finally, we discuss challenges faced during deployment with the objective of contributing to the development and advancement of cross-chain communication.

cs.PF↗

arXiv2022

TxAllo: Dynamic Transaction Allocation in Sharded Blockchain Systems

The scalability problem has been one of the most significant barriers limiting the adoption of blockchains. Blockchain sharding is a promising approach to this problem. However, the sharding mechanism introduces a significant number of cross-shard transactions, which are expensive to process. This paper focuses on the transaction allocation problem to reduce the number of cross-shard transactions for better scalability. In particular, we systematically formulate the transaction allocation problem and convert it to the community detection problem on a graph. A deterministic and fast allocation scheme TxAllo is proposed to dynamically infer the allocation of accounts and their associated transactions. It directly optimizes the system throughput, considering both the number of cross-shard transactions and the workload balance among shards. We evaluate the performance of TxAllo on an Ethereum dataset containing over 91 million transactions. Our evaluation results show that for a blockchain with 60 shards, TxAllo reduces the cross-shard transaction ratio from 98% (by using traditional hash-based allocation) to about 12%. In the meantime, the workload balance is well maintained. Compared with other methods, the execution time of TxAllo is almost negligible. For example, when updating the allocation every hour, the execution of TxAllo only takes 0.5 seconds on average, whereas other concurrent works, such as BrokerChain (INFOCOM'22) leveraging the classic METIS method, require 422 seconds.

cs.DB↗