Smart Contract Vulnerability Management via AI Sandbox Analysis
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Solution Overview
Problem
Existing solutions for smart contract vulnerabilities in financial institutions are inadequate, as they often rely on manual expert auditing, which is prone to errors, and lack systematic design and maintainability, failing to effectively detect and manage vulnerabilities in a continuous and automated manner.
Innovation Solution
A continuous vulnerability management system using a sandbox engine, knowledge base engine with artificial intelligence, and risk scoring engine with machine learning to scan for vulnerabilities, acquire threat intelligence, and assign risk scores, enabling automated action on identified vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual expert auditing is used to detect smart contract vulnerabilities, then vulnerability detection can be performed, but the process is prone to human errors and lacks consistency
Solution Approach 1:
The patent replaces manual expert auditing with an automated machine learning-based system. The ML model automatically analyzes smart contract code, executes it in sandbox environments, and identifies vulnerabilities without human intervention, thereby eliminating human errors and improving detection reliability while reducing operational complexity.
Solution Approach 2:
The system performs self-service by automatically scanning, analyzing, and reporting vulnerabilities in smart contracts. The machine learning model continuously learns from new vulnerability patterns and automatically updates its detection capabilities, enabling the system to maintain high reliability without requiring manual re-auditing.
2Reliability
If formal verification processes are used to prove code correctness, then mathematical proof can be provided, but the process is highly complex and requires manual annotations
Solution Approach 1:
The patent substitutes complex formal verification processes with machine learning-based automated analysis. Instead of requiring manual annotations and mathematical proofs, the ML model automatically learns vulnerability patterns from training data and applies them to detect issues in smart contracts, significantly reducing process complexity while maintaining reliability.
Solution Approach 2:
The system changes the approach from symbolic mathematical verification to statistical pattern recognition. The machine learning model processes code through numerical representations and learns vulnerability patterns from data, transforming the verification process from complex mathematical proofs to more manageable data-driven analysis.
3Productivity
If existing open source tools are used to analyze code text, then some vulnerability detection is possible, but they lack systematic design and maintainability
Solution Approach 1:
The patent segments the vulnerability detection system into distinct modular components: machine learning model for pattern recognition, sandbox environment for code execution, and reporting module for vulnerability communication. This segmentation provides systematic design that improves maintainability while preserving code analysis productivity.
Solution Approach 2:
The system implements dynamic adaptability through continuous machine learning model training on new vulnerability patterns. The model automatically updates its detection capabilities without requiring system redesign, providing both systematic maintainability and evolving productivity as new threats emerge.
Data Source
AI summary
A continuous vulnerability management system for identifying, analyzing, protecting, and reporting on digital assets is disclosed. The continuous vulnerability management system comprises a sandbox engine configured to scan digital assets for vulnerabilities, including a static analysis unit for static code scanning, a dynamic analysis unit for analyzing compiled code, and a statistical analysis unit for processing a risk score and generating an audit report. A knowledge base is also disclosed, including a knowledge base engine configured to acquire information related to new vulnerabilities to digital assets. A risk scoring engine is configured to analyze sandbox engine outcomes and assign a risk score to each vulnerable asset. A security control system is configured to act on an identified vulnerable asset based on the risk score assigned to the identified vulnerable asset.