Machine Learning Model Scanning for Pre-Execution Threat Detection
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Solution Overview
Problem
Existing machine learning systems lack effective methods to detect threats before or during execution, allowing attacks to go undetected until damage is done.
Innovation Solution
A scanning system performs file format checks, vulnerability checks, tamper checks, and steganography checks on machine learning models, generating a signature based on the results to identify and potentially embed threats, which can be executed in an isolated environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine learning models are deployed without pre-execution scanning, then deployment speed is improved, but security and reliability deteriorate
Solution Approach 1:
The patent implements pre-execution scanning of machine learning models before they are deployed to production environments. The scanning module performs multiple checks including file format validation, vulnerability detection, tamper detection, and steganography detection on the model file and its dependencies. This preliminary security assessment ensures that threats are identified and mitigated before the model can cause harm, resolving the contradiction by maintaining both deployment speed (through automated scanning) and security (through comprehensive pre-checks).
Solution Approach 2:
The system applies preliminary anti-action by proactively detecting and blocking potential threats before they can execute or cause damage. The scanning module identifies vulnerabilities, tampered components, and hidden malicious code in the model file and its dependencies, preventing these threats from materializing in the production environment. This approach counters the natural progression of attacks by establishing security barriers before deployment.
2Measurement precision
If comprehensive threat scanning is performed, then security detection capability is improved, but system complexity increases
Solution Approach 1:
The patent divides the threat scanning process into distinct modular components: file format checking module, vulnerability checking module, tamper checking module, and steganography checking module. Each module performs a specific type of analysis on the model file or its dependencies. This segmentation allows the system to maintain high detection precision through comprehensive checks while managing complexity through modular design, where each module can be independently developed, tested, and maintained.
Solution Approach 2:
The scanning module is designed as a universal security infrastructure that can scan multiple types of machine learning models and detect various categories of threats using a unified framework. The same core scanning architecture handles different model formats, vulnerability types, and attack vectors, reducing overall system complexity while maintaining comprehensive detection capability across diverse threats.
3Reliability
If models are scanned and validated before execution, then model integrity is improved, but processing time increases
Solution Approach 1:
The system performs all security scanning and validation operations in advance, during the model upload or deployment phase, before the model enters production execution. By completing file format checks, vulnerability scans, tamper detection, and steganography analysis beforehand, the system ensures model integrity is verified once during deployment rather than repeatedly during each execution, thus maintaining high reliability while minimizing ongoing processing time losses.
Data Source
AI summary
A machine learning model is scanned to detect actual or potential threats. The threats can be detected before execution of the machine learning model or during an isolated execution environment. The threat detection may include performing a machine learning file format check, vulnerability check, tamper check, and stenography check. The machine learning model may also be monitored in an isolated environment during an execution or runtime session. After performing a scan, the system can generate a signature based on actual, potential, or absence of detected threats.