Llama-3.1-8B Robustness Against Obfuscated Code Vulnerabilities via Extended Context Fine-Tuning on Big-Vul

As large language models (LLMs) are increasingly adopted for code vulnerability detection, their reliability and robustness across diverse vulnerability types have become a pressing concern. In traditional adversarial settings, code obfuscation has long been used as a general strategy to bypass auditing tools, preserving exploitability without tampering with the tools themselves. Numerous efforts have explored obfuscation methods and tools, yet their capabilities differ in terms of supported techniques, granularity, and programming languages, making it difficult to systematically assess their

Research goal: Does increasing context length during fine-tuning improve the robustness of Llama-3.1-8B against obfuscated code vulnerabilities in the Big-Vul benchmark?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.7/10.