BridgeShield: Enhancing Security for Cross-chain Bridge Applications via Heterogeneous Graph Mining

• URL: http://arxiv.org/abs/2508.20517v1
• Date: Thu, 28 Aug 2025 07:59:20 GMT
• Title: BridgeShield: Enhancing Security for Cross-chain Bridge Applications via Heterogeneous Graph Mining
• Authors: Dan Lin, Shunfeng Lu, Ziyan Liu, Jiajing Wu, Junyuan Fang, Kaixin Lin, Bowen Song, Zibin Zheng,
• Abstract summary: 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.
• Score: 41.291205709614026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-chain bridges play a vital role in enabling blockchain interoperability. However, due to the inherent design flaws and the enormous value they hold, they have become prime targets for hacker attacks. Existing detection methods show progress yet remain limited, as they mainly address single-chain behaviors and fail to capture cross-chain semantics. To address this gap, we leverage heterogeneous graph attention networks, which are well-suited for modeling multi-typed entities and relations, to capture the complex execution semantics of cross-chain behaviors. We propose BridgeShield, a detection framework that jointly models the source chain, off-chain coordination, and destination chain within a unified heterogeneous graph representation. BridgeShield incorporates intra-meta-path attention to learn fine-grained dependencies within cross-chain paths and inter-meta-path attention to highlight discriminative cross-chain patterns, thereby enabling precise identification of attack behaviors. Extensive experiments on 51 real-world cross-chain attack events demonstrate that BridgeShield achieves an average F1-score of 92.58%, representing a 24.39% improvement over state-of-the-art baselines. These results validate the effectiveness of BridgeShield as a practical solution for securing cross-chain bridges and enhancing the resilience of multi-chain ecosystems.

Related papers

• On the Adversarial Robustness of Discrete Image Tokenizers [56.377796750281796]
  We first formulate attacks that aim to perturb the features extracted by discrete tokenizers, and thus change the extracted tokens.<n>We fine-tune popular tokenizers with unsupervised adversarial training, keeping all other components frozen.<n>Our approach significantly improves robustness to both unsupervised and end-to-end supervised attacks and generalizes well to unseen tasks and data.
  arXiv  Detail & Related papers  (2026-02-20T14:39:17Z)
• ConneX: Automatically Resolving Transaction Opacity of Cross-Chain Bridges for Security Analysis [24.725668502966585]
  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.
  arXiv  Detail & Related papers  (2025-11-03T09:44:02Z)
• Hedge Funds on a Swamp: Analyzing Patterns, Vulnerabilities, and Defense Measures in Blockchain Bridges [5.662299435213421]
  bridges have become essential infrastructure for enabling interoperability across different blockchain networks.<n>They are the single largest source of financial loss in Web3.<n>We present a comprehensive systematization of blockchain bridge design and security.
  arXiv  Detail & Related papers  (2025-07-08T16:39:23Z)
• SoK: A Review of Cross-Chain Bridge Hacks in 2023 [36.136619420474766]
  Cross-chain bridges remain vulnerable to various attacks despite sophisticated designs and security measures.<n>This paper analyzes recent cross-chain bridge hacks in 2022 and 2023 and examines the exploited vulnerabilities.<n>The findings contribute to developing industry-wide standards for bridge security and operational resilience.
  arXiv  Detail & Related papers  (2025-01-06T23:05:14Z)
• Edge-Only Universal Adversarial Attacks in Distributed Learning [49.546479320670464]
  In this work, we explore the feasibility of generating universal adversarial attacks when an attacker has access to the edge part of the model only. Our approach shows that adversaries can induce effective mispredictions in the unknown cloud part by leveraging key features on the edge side. Our results on ImageNet demonstrate strong attack transferability to the unknown cloud part.
  arXiv  Detail & Related papers  (2024-11-15T11:06:24Z)
• Safeguarding Blockchain Ecosystem: Understanding and Detecting Attack Transactions on Cross-chain Bridges [3.07869141026886]
  Attacks on cross-chain bridges have resulted in losses of nearly 4.3 billion dollars since 2021.<n>This paper collects the largest number of cross-chain bridge attack incidents to date, including 49 attacks that occurred between June 2021 and September 2024.<n>We propose the BridgeGuard tool to detect attacks against cross-chain business logic.
  arXiv  Detail & Related papers  (2024-10-18T14:25:05Z)
• 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)
• SoK: Security of Cross-chain Bridges: Attack Surfaces, Defenses, and Open Problems [43.80265187232706]
  Cross-chain bridges are used to facilitate token and data exchanges across blockchains. Although bridges are becoming increasingly popular, they are still in their infancy and have been attacked multiple times recently. This paper analyzes the security landscape of cross-chain bridges in a holistic manner.
  arXiv  Detail & Related papers  (2023-12-19T20:13:21Z)
• Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers [58.802352477207094]
  We explore the great potential of a pre-trained vision Transformer (ViT) to bridge the vast distribution gap between two modalities. We propose a mask modeling strategy that randomly masks a specific modality of some tokens to enforce the interaction between tokens from different modalities interacting proactively. Experiments demonstrate that our plug-and-play training augmentation techniques can significantly boost state-of-the-art one-stream and two trackersstream to a large extent in terms of both tracking precision and success rate.
  arXiv  Detail & Related papers  (2023-07-09T08:58:47Z)

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.