ContractTinker: LLM-Empowered Vulnerability Repair for Real-World Smart Contracts

• URL: http://arxiv.org/abs/2409.09661v1
• Date: Sun, 15 Sep 2024 08:24:01 GMT
• Title: ContractTinker: LLM-Empowered Vulnerability Repair for Real-World Smart Contracts
• Authors: Che Wang, Jiashuo Zhang, Jianbo Gao, Libin Xia, Zhi Guan, Zhong Chen,
• Abstract summary: Smart contracts are susceptible to being exploited by attackers, especially when facing real-world vulnerabilities. To mitigate this risk, developers often rely on third-party audit services to identify potential vulnerabilities before project deployment. Existing pattern-based repair tools mostly fail to address real-world vulnerabilities due to their lack of high-level semantic understanding.
• Score: 8.756175353426304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Smart contracts are susceptible to being exploited by attackers, especially when facing real-world vulnerabilities. To mitigate this risk, developers often rely on third-party audit services to identify potential vulnerabilities before project deployment. Nevertheless, repairing the identified vulnerabilities is still complex and labor-intensive, particularly for developers lacking security expertise. Moreover, existing pattern-based repair tools mostly fail to address real-world vulnerabilities due to their lack of high-level semantic understanding. To fill this gap, we propose ContractTinker, a Large Language Models (LLMs)-empowered tool for real-world vulnerability repair. The key insight is our adoption of the Chain-of-Thought approach to break down the entire generation task into sub-tasks. Additionally, to reduce hallucination, we integrate program static analysis to guide the LLM. We evaluate ContractTinker on 48 high-risk vulnerabilities. The experimental results show that among the patches generated by ContractTinker, 23 (48%) are valid patches that fix the vulnerabilities, while 10 (21%) require only minor modifications. A video of ContractTinker is available at https://youtu.be/HWFVi-YHcPE.

Related papers

• AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents [84.96249955105777]
  LLM agents may pose a greater risk if misused, but their robustness remains underexplored. We propose a new benchmark called AgentHarm to facilitate research on LLM agent misuse. We find leading LLMs are surprisingly compliant with malicious agent requests without jailbreaking.
  arXiv  Detail & Related papers  (2024-10-11T17:39:22Z)
• The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach [56.4040698609393]
  Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies. We propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings.
  arXiv  Detail & Related papers  (2024-09-10T10:12:37Z)
• How Well Do Large Language Models Serve as End-to-End Secure Code Producers? [42.119319820752324]
  We studied GPT-3.5 and GPT-4's capability to identify and repair vulnerabilities in the code generated by four popular LLMs. By manually or automatically reviewing 4,900 pieces of code, our study reveals that large language models lack awareness of scenario-relevant security risks. To address the limitation of a single round of repair, we developed a lightweight tool that prompts LLMs to construct safer source code.
  arXiv  Detail & Related papers  (2024-08-20T02:42:29Z)
• WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models [66.34505141027624]
  We introduce WildTeaming, an automatic LLM safety red-teaming framework that mines in-the-wild user-chatbot interactions to discover 5.7K unique clusters of novel jailbreak tactics. WildTeaming reveals previously unidentified vulnerabilities of frontier LLMs, resulting in up to 4.6x more diverse and successful adversarial attacks.
  arXiv  Detail & Related papers  (2024-06-26T17:31:22Z)
• Static Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We? [14.974832502863526]
  In recent years, the importance of smart contract security has been heightened by the increasing number of attacks against them. To address this issue, a multitude of static application security testing (SAST) tools have been proposed for detecting vulnerabilities in smart contracts. In this paper, we propose an up-to-date and fine-grained taxonomy that includes 45 unique vulnerability types for smart contracts.
  arXiv  Detail & Related papers  (2024-04-28T13:40:18Z)
• 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)
• Two Timin': Repairing Smart Contracts With A Two-Layered Approach [3.2154249558826846]
  This paper proposes a novel, two-layered framework for classifying and repairing smart contracts. Slither's vulnerability report is combined with source code and passed through a pre-trained RandomForestClassifier (RFC) and Large Language Models (LLMs) Experiments demonstrate the effectiveness of fine-tuned and prompt-engineered LLMs.
  arXiv  Detail & Related papers  (2023-09-14T16:37:23Z)
• CONTRACTFIX: A Framework for Automatically Fixing Vulnerabilities in Smart Contracts [12.68736241704817]
  ContractFix is a framework that automatically generates security patches for vulnerable smart contracts. Users can use it as a security fix-it tool that automatically applies patches and verifies the patched contracts.
  arXiv  Detail & Related papers  (2023-07-18T01:14:31Z)
• 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)

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.