Smart Contract Fuzzing Towards Profitable Vulnerabilities (2501.08834v2)

Abstract: Billions of dollars are transacted through smart contracts, making vulnerabilities a major financial risk. One focus in the security arms race is on profitable vulnerabilities that attackers can exploit. Fuzzing is a key method for identifying these vulnerabilities. However, current solutions face two main limitations: a lack of profit-centric techniques for expediting detection, and insufficient automation in maximizing the profitability of discovered vulnerabilities, leaving the analysis to human experts. To address these gaps, we have developed VERITE, a profit-centric smart contract fuzzing framework that not only effectively detects those profitable vulnerabilities but also maximizes the exploited profits. VERITE has three key features: 1) DeFi action-based mutators for boosting the exploration of transactions with different fund flows; 2) potentially profitable candidates identification criteria, which checks whether the input has caused abnormal fund flow properties during testing; 3) a gradient descent-based profit maximization strategy for these identified candidates. VERITE is fully developed from scratch and evaluated on a dataset consisting of 61 exploited real-world DeFi projects with an average of over 1.1 million dollars loss. The results show that VERITE can automatically extract more than 18 million dollars in total and is significantly better than state-of-the-art fuzzer ITYFUZZ in both detection (29/10) and exploitation (134 times more profits gained on average). Remarkably, in 12 targets, it gains more profits than real-world attacking exploits (1.01 to 11.45 times more). VERITE is also applied by auditors in contract auditing, where 6 (5 high severity) zero-day vulnerabilities are found with over $2,500 bounty rewards.

• The paper introduces a profit-centric fuzzing framework that leverages DeFi action-based mutators and profitability criteria to identify vulnerabilities.
• It employs gradient descent optimization to fine-tune transaction parameters, significantly outperforming existing fuzzing tools.
• Empirical evaluation on 61 DeFi projects revealed multi-million-dollar vulnerability exploits, enhancing smart contract security assessments.

An Analysis of "Smart Contract Fuzzing Towards Profitable Vulnerabilities"

The paper "Smart Contract Fuzzing Towards Profitable Vulnerabilities" addresses a significant issue within the decentralized finance (DeFi) ecosystem: the exploitation of profitable vulnerabilities in smart contracts. With billions of dollars transacted through these contracts, the potential implications of such vulnerabilities pose a substantial financial risk. The researchers introduce a novel framework, \tool{}, designed to effectively discover and maximize profits from these vulnerabilities, overcoming limitations observed in existing fuzzing tools.

Methodological Innovations

\tool{} introduces a profit-centric fuzzing framework with several key innovations:

1. DeFi Action-Based Mutators: Unlike traditional fuzzers that operate on an API level, \tool{} employs action-based mutators. These mutators group contract APIs into logical transaction steps, significantly improving the generation of valid transactions and enhancing the exploration of diverse fund flows. This approach is grounded in the high-level semantic operations typical of DeFi applications, such as token swapping and provisioning or withdrawal of liquidity, distilling them into actionable items that streamline fuzz input generation.
2. Profitability Recognition Criteria: This component checks for abnormal fund flow properties during the fuzzing process, identifying seeds that may lead to profitable exploits. \tool{} evaluates several factors, including net positive asset changes, imbalance in Uniswap pairs, and unauthorized token manipulations, to filter potentially profitable paths.
3. Gradient Descent-Based Profit Maximization: Once a profitable sequence is identified, \tool{} employs a gradient descent strategy to optimize transaction parameters for maximum profit extraction. This optimization process exploits the fund flow characteristics unique to each candidate, refining the input parameters to enhance the economic outcome relative to the detected vulnerability.

Empirical Evaluation

The effectiveness of \tool{} is thoroughly evaluated against the state-of-the-art fuzzer ItyFuzz and actual real-world attack data. The research tests \tool{} on a dataset encompassing 61 exploited DeFi projects, within which losses averaged over $1.1 million. \tool{} significantly surpasses ItyFuzz in detecting profitable vulnerabilities and maximizes the exploits to a greater extent. Notably, it demonstrated the ability to extract more than \$18 million in total, outstripping real-world attack optimizations in numerous instances.

Implications and Future Directions

The introduction of \tool{} has both theoretical and practical implications for smart contract security. Theoretically, the framework provides a robust methodology for identifying and optimizing profits from vulnerabilities inherent in smart contracts. Practically, its deployment can potentially transform contract auditing processes by automating the detection and exploitation phases, thereby assisting auditors in evaluating vulnerability severity and formulating mitigation strategies efficiently.

Furthermore, the findings suggest several avenues for future research. Enhancing the automation of action synthesis could broaden the applicability of \tool{} across a wider spectrum of smart contract ecosystems. Additionally, integrating adaptive methods into the fuzzing strategy could further improve the identification of diverse vulnerability types, thereby solidifying \tool{}'s position as a critical tool in the cybersecurity arsenals of DeFi stakeholders.

In conclusion, the research delineates a commendable pathway towards mitigating financial risks posed by vulnerable smart contracts, fostering a more secure DeFi landscape. As blockchain technology and decentralized applications continue to expand, frameworks like \tool{} become indispensable in safeguarding the integrity and stability of these systems.