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Software, while beneficial, poses potential cybersecurity risks due toinherent vulnerabilities. Detecting these vulnerabilities is crucial, and deeplearning has shown promise as an effective tool for this task due to itsability to perform well without extensive feature engineering. However, achallenge in deploying deep learning for vulnerability detection is the limitedavailability of training data. Recent research highlights the deep learningefficacy in diverse tasks. This success is attributed to instructionfine-tuning, a technique that remains under-explored in the context ofvulnerability detection. This paper investigates the capability of models,specifically a recent language model, to generalize beyond the programminglanguages used in their training data. It also examines the role of naturallanguage instructions in enhancing this generalization. Our study evaluates themodel performance on a real-world dataset to predict vulnerable code. Wepresent key insights and lessons learned, contributing to understanding thedeep learning application in software vulnerability detection.

Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection