SEMA-GUARD: Semantic and Graph-Based Vulnerability Detection in Assembly Code
SEMA-GUARD uses semantic analysis and graph neural networks to detect vulnerabilities in assembly code, achieving 85.1% accuracy on a Juliet-derived benchmark.
SEMA-GUARD is a framework that detects vulnerabilities in compiled programs when source code is unavailable, targeting malware, firmware, and embedded systems analysis. It enriches control flow graphs with low-level execution semantics including stack manipulations, memory accesses, and data flow. Evaluated on a Juliet Test Suite set compiled to assembly and split into function-level chunks, it achieves 85.1% accuracy and an F1 score of 0.801, outperforming purely statistical or structural approaches.
- Targets assembly-level vulnerability detection where source code is unavailable
- Augments control flow graphs with stack, memory, and data-flow semantics
- Achieves 85.1% accuracy and 0.801 F1 on Juliet-derived assembly benchmark
- Uses graph neural networks for binary and firmware analysis
From the original article
1 min read at arxiv.orgIn cases where source code is not available, such as malware analysis, firmware analysis, and embedded systems analysis, vulnerability detection in compiled programs has gained importance. Current methods are heavily reliant on syntactical regularities or higher level representations that are vulnerable to changes in the compiler and may not be readily applicable to assembly code.
By Halil Dursunoglu · arxiv.org
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