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Open-Source LLMs for Vulnerability Classification Under Code Obfuscation: Efficiency Benchmarks

Authors: Assignee Research;

Open-Source LLMs for Vulnerability Classification Under Code Obfuscation: Efficiency Benchmarks

Abstract

This report synthesises findings from 8 peer-reviewed papers addressing the following research question: How does the inference efficiency (latency, throughput) of Llama3, Codestral, and Deepseek R1 compare when classifying vulnerabilities in the Big-Vul dataset under increasing levels of code. 7 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.5/10. This report is a machine-generated literature synthesis and does not constitute original research.Research goal: How does the inference efficiency (latency, throughput) of Llama3, Codestral, and Deepseek R1 compare when classifying vulnerabilities in the Big-Vul dataset under increasing levels of code obfuscation?Autonomous literature synthesis. Automated review score: 7.5/10. Full text and citation available at Assignee Research.

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