1 citations · 5 across the 13 of their papers we have counts for
13 papers
GoAT-X: A Graph of Auditing Thoughts for Securing Token Transactions in Cross-Chain Contracts
Zijun Feng, Yuming Feng, Yu Wang +4
Cross-chain bridges, the critical infrastructure of the multi-chain ecosystem, have become a primary target for attackers, resulting in over $2.8 billion in losses due to subtle im…
V2E: Validating Smart Contract Vulnerabilities through Profit-driven Exploit Generation and Execution
Jingwen Zhang, Yuhong Nan, Kaiwen Ning +6
Smart contracts are a critical component of blockchain systems. Due to the large amount of digital assets carried by smart contracts, their security is of critical importance. Alth…
Defusing the Trigger: Tail-Risk-Informed Attention Rebalancing for LLM Backdoor Mitigation
Kaisheng Fan, Yishu Gao, Xunzhu Tang +2
Backdoored large language models (LLMs) exhibit attacker-specified behavior at inference time while retaining normal performance on benign inputs. Existing mitigations often requir…
SSR: Safeguarding Staking Rewards by Defining and Detecting Logical Defects in DeFi Staking
Zewei Lin, Jiachi Chen, Jingwen Zhang +4
Decentralized Finance (DeFi) staking is one of the most prominent applications within the DeFi ecosystem, where DeFi projects enable users to stake tokens on the platform and rewar…
One Signature, Multiple Payments: Demystifying and Detecting Signature Replay Vulnerabilities in Smart Contracts
Zexu Wang, Jiachi Chen, Zewei Lin +7
Smart contracts have significantly advanced blockchain technology, and digital signatures are crucial for reliable verification of contract authority. Through signature verificatio…
Copy-and-Paste? Identifying EVM-Inequivalent Code Smells in Multi-chain Reuse Contracts
Zexu Wang, Jiachi Chen, Tao Zhang +4
As the development of Solidity contracts on Ethereum, more developers are reusing them on other compatible blockchains. However, developers may overlook the differences between the…
Defining and Detecting the Defects of the Large Language Model-based Autonomous Agents
Kaiwen Ning, Jiachi Chen, Jingwen Zhang +5
AI agents are systems capable of perceiving their environment, autonomously planning and executing tasks. Recent advancements in LLM have introduced a transformative paradigm for A…
Definition and Detection of Centralization Defects in Smart Contracts
Zewei Lin, Jiachi Chen, Jiajing Wu +2
In recent years, security incidents stemming from centralization defects in smart contracts have led to substantial financial losses. A centralization defect refers to any error, f…
SmartReco: Detecting Read-Only Reentrancy via Fine-Grained Cross-DApp Analysis
Jingwen Zhang, Zibin Zheng, Yuhong Nan +4
Despite the increasing popularity of Decentralized Applications (DApps), they are suffering from various vulnerabilities that can be exploited by adversaries for profits. Among suc…
MCGMark: An Encodable and Robust Online Watermark for Tracing LLM-Generated Malicious Code
Kaiwen Ning, Jiachi Chen, Qingyuan Zhong +8
With the advent of large language models (LLMs), numerous software service providers (SSPs) are dedicated to developing LLMs customized for code generation tasks, such as CodeLlama…
Efficiently Detecting Reentrancy Vulnerabilities in Complex Smart Contracts
Zexu Wang, Jiachi Chen, Yanlin Wang +3
Reentrancy vulnerability as one of the most notorious vulnerabilities, has been a prominent topic in smart contract security research. Research shows that existing vulnerability de…
CRPWarner: Warning the Risk of Contract-related Rug Pull in DeFi Smart Contracts
Zewei Lin, Jiachi Chen, Zibin Zheng +3
In recent years, Decentralized Finance (DeFi) grows rapidly due to the development of blockchain technology and smart contracts. As of March 2023, the estimated global cryptocurren…
Unity is Strength: Enhancing Precision in Reentrancy Vulnerability Detection of Smart Contract Analysis Tools
Zexu Wang, Jiachi Chen, Zibin Zheng +3
Reentrancy is one of the most notorious vulnerabilities in smart contracts, resulting in significant digital asset losses. However, many previous works indicate that current Reentr…