The problem Greybox solves

Greybox addresses the critical need for secure Ethereum smart contracts by providing an easy-to-use, extensible framework for vulnerability detection.

It combines static and dynamic analysis to identify and confirm vulnerabilities, making it a powerful tool for builders and auditors looking to enhance the safety and reliability of Solidity contracts. With YAML-based templates, users can add custom checks, facilitating personalised and up-to-date security assessments, retrofit for their project & protocol.

Use Cases

• Developers: Quickly identify security issues in contracts during development, minimising risks before deployment.
• Auditors: Use Greybox to perform in-depth vulnerability assessments, combining static patterns and dynamic testing for thorough coverage. Build a suite of proprietary tests that allow for fine-tuned detection of novel smart contract vulnerabilties.
• Security Teams: Integrate Greybox into CI/CD pipelines to ensure only secure code is deployed.

Challenges I ran into

Hardhat Compiliation Environment

One of the main challenges was setting up Hardhat to compile and deploy contracts for dynamic testing, especially with the time constraints of the hackathon. Integrating contract compilation with Greybox's YAML-based analysis workflow was complex and led to repeated compilation issues.

Solution:

I resolved this by fine-tuning Hardhat’s configurations and using its Runtime Environment (HRE) to deploy contracts programmatically. This approach enabled reliable dynamic testing on deployed contracts, allowing Greybox to perform both static and dynamic analysis effectively.

Limited Time and Resources

Building a fully functional security tool within the limited timeframe and available resources of a hackathon presented additional challenges. I had to prioritise core features, making trade-offs on advanced functionalities to ensure a stable and usable product by the deadline.

Solution:

I focused on creating a robust, extensible framework by leveraging modular YAML templates, which allowed the essential vulnerability scans to work seamlessly while leaving room for future feature expansion. This approach maximized impact within the constraints, delivering a tool that meets immediate security needs and can grow post-hackathon.