ConneX: Automatically Resolving Transaction Opacity of Cross-Chain Bridges for Security Analysis

• URL: http://arxiv.org/abs/2511.01393v1
• Date: Mon, 03 Nov 2025 09:44:02 GMT
• Title: ConneX: Automatically Resolving Transaction Opacity of Cross-Chain Bridges for Security Analysis
• Authors: Hanzhong Liang, Yue Duan, Xing Su, Xiao Li, Yating Liu, Yulong Tian, Fengyuan Xu, Sheng Zhong,
• Abstract summary: ConneX is an automated system designed to accurately identify corresponding transaction pairs across both ends of cross-chain bridges.<n>Its successful application in tracing illicit funds underscores its practical utility for enhancing cross-chain security and transparency.
• Score: 24.725668502966585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the Web3 ecosystem evolves toward a multi-chain architecture, cross-chain bridges have become critical infrastructure for enabling interoperability between diverse blockchain networks. However, while connecting isolated blockchains, the lack of cross-chain transaction pairing records introduces significant challenges for security analysis like cross-chain fund tracing, advanced vulnerability detection, and transaction graph-based analysis. To address this gap, we introduce ConneX, an automated and general-purpose system designed to accurately identify corresponding transaction pairs across both ends of cross-chain bridges. Our system leverages Large Language Models (LLMs) to efficiently prune the semantic search space by identifying semantically plausible key information candidates within complex transaction records. Further, it deploys a novel examiner module that refines these candidates by validating them against transaction values, effectively addressing semantic ambiguities and identifying the correct semantics. Extensive evaluations on a dataset of about 500,000 transactions from five major bridge platforms demonstrate that ConneX achieves an average F1 score of 0.9746, surpassing baselines by at least 20.05\%, with good efficiency that reduces the semantic search space by several orders of magnitude (1e10 to less than 100). Moreover, its successful application in tracing illicit funds (including a cross-chain transfer worth $1 million) in real-world hacking incidents underscores its practical utility for enhancing cross-chain security and transparency.

Related papers

• RiskTagger: An LLM-based Agent for Automatic Annotation of Web3 Crypto Money Laundering Behaviors [65.80108147440863]
  RiskTagger is a large-language-model-based agent for the automatic annotation of crypto laundering behaviors in Web3.<n>RiskTagger is designed to replace or complement human annotators by addressing three key challenges: extracting clues from complex unstructured reports, reasoning over multichain transaction paths, and producing auditor-friendly explanations.
  arXiv  Detail & Related papers  (2025-10-12T08:54:28Z)
• BridgeShield: Enhancing Security for Cross-chain Bridge Applications via Heterogeneous Graph Mining [41.291205709614026]
  Cross-chain bridges play a vital role in enabling blockchain interoperability.<n>Due to the inherent design flaws and the enormous value they hold, they have become prime targets for hacker attacks.<n>Existing detection methods mainly address single-chain behaviors and fail to capture cross-chain semantics.<n>We propose BridgeShield, a detection framework that jointly models the source chain, off-chain coordination, and destination chain within a unified graph representation.
  arXiv  Detail & Related papers  (2025-08-28T07:59:20Z)
• Decompiling Smart Contracts with a Large Language Model [51.49197239479266]
  Despite Etherscan's 78,047,845 smart contracts deployed on (as of May 26, 2025), a mere 767,520 ( 1%) are open source.<n>This opacity necessitates the automated semantic analysis of on-chain smart contract bytecode.<n>We introduce a pioneering decompilation pipeline that transforms bytecode into human-readable and semantically faithful Solidity code.
  arXiv  Detail & Related papers  (2025-06-24T13:42:59Z)
• Transaction Proximity: A Graph-Based Approach to Blockchain Fraud Prevention [0.0]
  This paper introduces a fraud-deterrent access validation system for public blockchains.<n>"Transaction Proximity" measures the distance between wallets in the transaction graph.<n>"Easily Attainable Identities" identify wallets with direct transaction connections to centralized exchanges.
  arXiv  Detail & Related papers  (2025-05-30T07:00:07Z)
• Track and Trace: Automatically Uncovering Cross-chain Transactions in the Multi-blockchain Ecosystems [21.470299762211546]
  Cross-chain technology enables seamless asset transfer and message-passing within decentralized finance (DeFi) ecosystems.<n>This paper proposes ABCTRACER, an automated, bi-directional cross-chain transaction tracing tool, specifically designed for DeFi ecosystems.
  arXiv  Detail & Related papers  (2025-04-02T15:28:25Z)
• MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic Classification [59.96233305733875]
  Classifying traffic is essential for detecting security threats and optimizing network management.<n>We propose a Multi-Instance Encrypted Traffic Transformer (MIETT) to capture both token-level and packet-level relationships.<n>MIETT achieves results across five datasets, demonstrating its effectiveness in classifying encrypted traffic and understanding complex network behaviors.
  arXiv  Detail & Related papers  (2024-12-19T12:52:53Z)
• MAP the Blockchain World: A Trustless and Scalable Blockchain Interoperability Protocol for Cross-chain Applications [18.33587076597966]
  interoperability protocols enable cross-chain asset transfers or data retrievals between isolated chains. Existing protocols either face severe scalability issues due to high on-chain and off-chain costs, or suffer from trust concerns because of centralized designs. We propose textttMAP, a trustless blockchain interoperability protocol that relays cross-chain transactions across heterogeneous chains with high scalability.
  arXiv  Detail & Related papers  (2024-11-01T07:43:11Z)
• CONNECTOR: Enhancing the Traceability of Decentralized Bridge Applications via Automatic Cross-chain Transaction Association [23.89344998976908]
  Cross-chain transaction association identifies and matches unique transactions executed by bridge DApps.<n>We present CONNECTOR, an automated cross-chain transaction association analysis method based on bridge smart contracts.<n>We conduct real-world experiments on different types of bridges to demonstrate the effectiveness of CONNECTOR.
  arXiv  Detail & Related papers  (2024-09-08T00:52:06Z)
• Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0 [59.94605620983965]
  We design a Graph Attention Network (GAT)-based reliable block propagation optimization framework for blockchain-enabled Web 3.0. To achieve the reliability of block propagation, we introduce a reputation mechanism based on the subjective logic model. Considering that the GAT possesses the excellent ability to process graph-structured data, we utilize the GAT with reinforcement learning to obtain the optimal block propagation trajectory.
  arXiv  Detail & Related papers  (2024-03-20T01:58:38Z)
• Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study [73.87110604150315]
  Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
  arXiv  Detail & Related papers  (2024-01-28T10:46:17Z)
• Blockchain Large Language Models [65.7726590159576]
  This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions. The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System.
  arXiv  Detail & Related papers  (2023-04-25T11:56:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.