New Tool Aims to Enhance Security for Cross-Chain Bridges
/ 4 min read
Quick take - The article discusses the importance of cross-chain bridges in the blockchain ecosystem for asset transfer and interoperability, highlights their vulnerabilities leading to significant financial losses, and introduces a new tool called Cross-Chain Watcher designed to monitor and detect attacks in real time, while emphasizing the need for improved security measures and user awareness in cross-chain protocols.
Fast Facts
- Cross-chain bridges facilitate asset and information transfer between different blockchains but are vulnerable, leading to losses of approximately $3.2 billion since May 2021.
- A new tool, Cross-Chain Watcher (\toolName), has been proposed to monitor these bridges and detect attacks in real time, utilizing a cross-chain model powered by a Datalog engine.
- \toolName has successfully identified both successful attacks and unintended risky behaviors, including detecting 37 problematic transactions and over $7.8 million locked incorrectly.
- An open-source dataset of 81,000 cross-chain transactions has been created, capturing over $4.2 billion in token transfers, highlighting the growth of the cross-chain ecosystem.
- The study emphasizes the need for improved user awareness and security practices, as user errors contributed to over $4.8 million in unwithdrawn funds, while future work aims to enhance the cross-chain model’s capabilities.
Cross-Chain Bridges: A Critical Component of Blockchain Ecosystem
Cross-chain bridges are crucial components in the blockchain ecosystem, enabling interoperability between different blockchains. They allow for the seamless transfer of assets and information across various platforms. However, these bridges have been susceptible to vulnerabilities, leading to significant financial losses. Since May 2021, these vulnerabilities have resulted in losses amounting to approximately $3.2 billion.
Addressing Vulnerabilities with Cross-Chain Watcher
Despite previous studies highlighting these vulnerabilities, there is still a notable gap in quantitative research. Effective protective mechanisms are also lacking. To address these challenges, a new tool named Cross-Chain Watcher (\toolName) has been proposed. This tool is designed to monitor cross-chain bridges and detect attacks in real time.
\toolName utilizes a cross-chain model powered by a Datalog engine. This model can be integrated into any cross-chain bridge. The efficacy of \toolName has been demonstrated through analyses of high-profile attacks on the Ronin and Nomad bridges, which resulted in losses of $611 million and $190 million, respectively.
Capabilities and Findings of Cross-Chain Watcher
\toolName is capable of identifying not only successful attacks but also unintended behaviors that could pose risks. For instance, it has detected 37 cross-chain transactions that should not have been accepted. It also identified over $7.8 million locked on one chain but not released on Ethereum. Additionally, $200,000 was lost due to improper interactions with bridges.
An open-source dataset has been created, encompassing 81,000 cross-chain transactions, capturing over $4.2 billion in token transfers. The cross-chain ecosystem has experienced remarkable growth, with over $500 million raised in investments in 2023 alone. As of November 2024, the total value locked (TVL) in non-native bridges is approximately $11 billion, while native bridges stand at around $39 billion. Despite extensive audits, many bridges remain vulnerable and have been exploited multiple times.
Enhancing Security and User Awareness
Effective incident response frameworks are crucial for minimizing losses and improving the identification of attackers. The cross-chain model developed by \toolName captures essential security properties of bridges, including integrity, accountability, and availability. Cross-chain transactions typically involve a commitment process that necessitates verification on the destination blockchain.
The architecture of \toolName operates in two phases. The first phase involves data extraction from transaction receipts, while the second phase entails evaluation through the cross-chain model. This includes the Bridge Facts Extractor, which collects static facts about the bridge, and the CCTX Facts Extractor, which gathers implementation-specific data.
The study analyzed 18 different bridge hacks that resulted in over $3 billion in stolen funds. It defined rules within the Datalog framework to detect both known attacks and unexpected behaviors, categorized as either isolated or dependent based on their operational context.
The implementation of \toolName was rigorously tested on the Nomad and Ronin bridges, both of which had previously suffered attacks. Data collection for the analysis involved retrieving blockchain data from various sources and analyzing transaction events. This analysis uncovered discrepancies between events emitted by token contracts and bridge contracts, signaling potential anomalies.
Additionally, the study highlighted user vulnerabilities, including phishing attempts and other anomalies in transaction data. It was found that user errors and inadequate user interface/user experience (UI/UX) contributed to unwithdrawn funds, amounting to over $4.8 million.
The findings underscore the necessity for enhanced user awareness and improved security practices within cross-chain protocols. Future work aims to extend the cross-chain model and support additional bridges, strengthening security measures further and ensuring a more resilient cross-chain ecosystem.
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