Generative AI Model Merging for Smart Contract Security

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Solution Overview

Problem

Smart contracts on distributed ledger networks are vulnerable to hacking and unintended outcomes due to flaws in logic design or programming errors, and off-blockchain solutions like cross-chain bridges are high-value targets for hacking.

Innovation Solution

A method using a generative artificial intelligence model, constructed from a base GPT model and a secondary model trained on smart contract attributes, to generate and validate smart contract code, including the creation, validation, and optimization of cross-chain bridges, leveraging synthetic data to simulate interactions and hacking scenarios.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If smart contracts are made immutable after deployment to ensure trust and security, then the reliability of the distributed ledger network is improved, but the ability to fix vulnerabilities and logic errors is lost

Engineering Contradiction:

Improvetrust and security of smart contractsVSAvoidability to modify and fix smart contracts

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent segments the smart contract lifecycle into distinct phases: development, validation/testing, and deployment. By separating the validation phase before deployment, the system allows for thorough testing and vulnerability detection while maintaining immutability after deployment. This segmentation enables fixing issues in the development environment without compromising the security of deployed contracts.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements preliminary validation and testing actions before smart contract deployment. The system performs static analysis, formal verification, and simulated execution in a controlled environment prior to deployment, allowing vulnerabilities to be identified and corrected in advance. This preliminary action ensures that only validated contracts are deployed to the immutable blockchain.

Inventive Principle:

Principle #10Preliminary action

2Manufacturing precision

If smart contracts are manually created by skilled programmers to ensure code quality, then the manufacturing precision is improved, but the productivity of smart contract deployment is reduced

Engineering Contradiction:

Improvecode quality and logic designVSAvoidsmart contract deployment speed

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The patent introduces an intermediary validation system that acts as a mediator between manual programming and automated deployment. This intermediary layer performs automated static analysis, formal verification, and security checking on manually created contracts, combining the quality assurance benefits of expert review with the efficiency of automated validation, thereby improving both code quality and deployment speed.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual mechanical review processes with automated computational validation methods. Instead of relying solely on skilled programmers to manually review and test contracts, the system employs automated static analysis tools, formal verification engines, and simulated execution environments that can rapidly validate contract logic and security, significantly improving productivity while maintaining or enhancing code quality.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If cross-chain bridges are created to enable off-blockchain solutions and asset transfer, then the adaptability of the distributed ledger network is improved, but the vulnerability to hacking and malfeasance increases

Engineering Contradiction:

Improveoff-blockchain solution capabilityVSAvoidhacking risk and malfeasance in bridges

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The patent implements beforehand cushioning by deploying monitoring agents and validation mechanisms in advance on cross-chain bridges. These agents continuously monitor bridge operations, validate transaction integrity, and detect potential hacking attempts or malfeasance before they can cause significant damage. The system prepares defensive measures proactively rather than reactively.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

Solution Approach 2:

The patent establishes feedback loops through monitoring agents that continuously observe bridge operations and report anomalies. When potential security issues or unusual patterns are detected, the system provides feedback that triggers alerting, investigation, and corrective actions. This continuous feedback mechanism enables real-time detection and response to hacking attempts on cross-chain bridges.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20240394705A1Smart contract creation and management using generative artificial intelligence with model merging

Publication Date: 2024.11.28 US BANK NATIONAL ASSOCIATION

AI summary

Methods and systems for smart contract creation, validation, and monitoring using generative artificial intelligence models are described. In example implementations, such models may be constructed as merged models from base models and secondary models. The secondary models may be fine-tuned and task-specific. By way of example, the merged models may be used for generation of smart contract code, generation of synthetic data used to validate or test smart contract code, or in cooperation with agents to monitor execution of smart contract code once published on the blockchain.