Smart Contract Fuzzing Towards Profitable Vulnerabilities

• URL: http://arxiv.org/abs/2501.08834v2
• Date: Wed, 12 Feb 2025 08:58:44 GMT
• Title: Smart Contract Fuzzing Towards Profitable Vulnerabilities
• Authors: Ziqiao Kong, Cen Zhang, Maoyi Xie, Ming Hu, Yue Xue, Ye Liu, Haijun Wang, Yang Liu,
• Abstract summary: VERITE is a profit-centric smart contract fuzzing framework. It detects profitable vulnerabilities and maximizes the exploited profits. It can extract more than 18 million dollars in total and is significantly better than state-of-the-art fuzzer ITYFUZZ in both detection and exploitation.
• Score: 10.908512696717724
• License:
• 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.

Related papers

• Do Automated Fixes Truly Mitigate Smart Contract Exploits? [7.570246812206772]
  This paper introduces a novel and systematic experimental framework for evaluating exploit mitigation of program repair tools for smart contracts. We qualitatively and quantitatively analyze 20 state-of-the-art APR tools using a dataset of 143 vulnerable smart contracts. Our findings reveal substantial disparities in the state of the art, with an exploit mitigation rate ranging from a low of 27% to a high of 73%.
  arXiv  Detail & Related papers  (2025-01-08T16:31:59Z)
• Retrieval Augmented Generation Integrated Large Language Models in Smart Contract Vulnerability Detection [0.0]
  Decentralized Finance (DeFi) has been accompanied by substantial financial losses due to smart contract vulnerabilities. With attacks becoming more frequent, the necessity and demand for auditing services has escalated. This study builds upon existing frameworks by integrating Retrieval-Augmented Generation (RAG) with large language models (LLMs)
  arXiv  Detail & Related papers  (2024-07-20T10:46:42Z)
• All Your Tokens are Belong to Us: Demystifying Address Verification Vulnerabilities in Solidity Smart Contracts [24.881450403784786]
  Vulnerabilities in the process of address verification can lead to great security issues. We design and implement AVVERIFIER, a lightweight taint analyzer based on static EVM opcode simulation. After a large-scale evaluation of over 5 million smart contracts, we have identified 812 vulnerable smart contracts that were undisclosed by our community.
  arXiv  Detail & Related papers  (2024-05-31T01:02:07Z)
• Intention Analysis Makes LLMs A Good Jailbreak Defender [79.4014719271075]
  We present a simple yet highly effective defense strategy, i.e., Intention Analysis ($mathbbIA$) $mathbbIA$ works by triggering LLMs' inherent self-correct and improve ability through a two-stage process. Experiments on varying jailbreak benchmarks show that $mathbbIA$ could consistently and significantly reduce the harmfulness in responses.
  arXiv  Detail & Related papers  (2024-01-12T13:15:05Z)
• Vulnerability Scanners for Ethereum Smart Contracts: A Large-Scale Study [44.25093111430751]
  In 2023 alone, such vulnerabilities led to substantial financial losses exceeding a billion of US dollars. Various tools have been developed to detect and mitigate vulnerabilities in smart contracts. This study investigates the gap between the effectiveness of existing security scanners and the vulnerabilities that still persist in practice.
  arXiv  Detail & Related papers  (2023-12-27T11:26:26Z)
• G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering [116.4277292854053]
  Federated Learning (FL) offers collaborative model training without data sharing. FL is vulnerable to backdoor attacks, where poisoned model weights lead to compromised system integrity. We present G$2$uardFL, a protective framework that reinterprets the identification of malicious clients as an attributed graph clustering problem.
  arXiv  Detail & Related papers  (2023-06-08T07:15:04Z)
• Smart Contract and DeFi Security Tools: Do They Meet the Needs of Practitioners? [10.771021805354911]
  Attacks targeting smart contracts are increasing, causing an estimated $6.45 billion in financial losses. We aim to shed light on the effectiveness of automated security tools in identifying vulnerabilities that can lead to high-profile attacks. Our findings reveal a stark reality: the tools could have prevented a mere 8% of the attacks in our dataset, amounting to $149 million out of the $2.3 billion in losses.
  arXiv  Detail & Related papers  (2023-04-06T10:27:19Z)
• ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning [80.85273827468063]
  Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable. We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts. We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
  arXiv  Detail & Related papers  (2021-03-23T15:04:44Z)
• Online Adversarial Attacks [57.448101834579624]
  We formalize the online adversarial attack problem, emphasizing two key elements found in real-world use-cases. We first rigorously analyze a deterministic variant of the online threat model. We then propose algoname, a simple yet practical algorithm yielding a provably better competitive ratio for $k=2$ over the current best single threshold algorithm.
  arXiv  Detail & Related papers  (2021-03-02T20:36:04Z)
• Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning [100.73223416589596]
  We propose a cost-sensitive portfolio selection method with deep reinforcement learning. Specifically, a novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations. A new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning.
  arXiv  Detail & Related papers  (2020-03-06T06:28:17Z)

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.