Binary Vulnerability Detection Using Execution-Guided Neural Control Flow
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Solution Overview
Problem
Existing methods for detecting vulnerabilities in binary code are inefficient and inaccurate due to the loss of information during compilation, making it difficult to detect vulnerabilities in complex software systems, especially when manual security patches are slow to deploy.
Innovation Solution
An agent-based implicit neural network that mimics the execution path of a program using reinforcement learning to enhance branching decisions and capture global information, employing a state-dependent adjacency matrix and implicit Graph Neural Network for vulnerability detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If binary code analysis is performed using traditional static or dynamic methods, then vulnerability detection can be achieved, but the process becomes manually expensive and inefficient
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated neural network system that processes binary code through learned patterns, substituting human expert analysis with an intelligent automated system capable of high-speed processing
Solution Approach 2:
The patent introduces assembly code as an intermediary representation between binary code and source code, creating a middle layer that preserves enough structural information for effective vulnerability detection while being more accessible than raw binary
2Measurement precision
If source code level analysis is used for vulnerability detection, then accurate and complex vulnerability detection is achieved, but it requires access to original source code which is often unavailable
Solution Approach 1:
Instead of starting from source code and compiling to binary, the patent inverts the approach by analyzing binary code and using neural networks to infer vulnerability patterns that would traditionally require source code inspection
Solution Approach 2:
The patent creates a virtual representation of source code structures through assembly code analysis, copying the essential structural information needed for vulnerability detection without requiring the actual source code
3Adaptability or versatility
If binary code is analyzed directly, then vulnerability detection can proceed without source code, but information loss during compilation makes detection much harder
Solution Approach 1:
The patent adds a new dimension to binary code analysis by incorporating assembly code as an intermediate representation layer, transforming the analysis from direct binary inspection to a multi-layer approach that recovers lost structural information
Data Source
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AI summary
There is provided methods and apparatuses for a control flow execution-guided deep learning framework for binary code vulnerability detection. Reinforcement learning is to enhance the branching decision at every program state transition and create a dynamic environment to learn the dependency between a vulnerability and certain program states. An implicitly defined neural network enables state transition until convergence, which captures the structural information at a higher level.