  In the face of growing vulnerabilities found in open-source software, the
need to identify {discreet} security patches has become paramount. The lack of
consistency in how software providers handle maintenance often leads to the
release of security patches without comprehensive advisories, leaving users
vulnerable to unaddressed security risks. To address this pressing issue, we
introduce a novel security patch detection system, LLMDA, which capitalizes on
Large Language Models (LLMs) and code-text alignment methodologies for patch
review, data enhancement, and feature combination. Within LLMDA, we initially
utilize LLMs for examining patches and expanding data of PatchDB and SPI-DB,
two security patch datasets from recent literature. We then use labeled
instructions to direct our LLMDA, differentiating patches based on security
relevance. Following this, we apply a PTFormer to merge patches with code,
formulating hybrid attributes that encompass both the innate details and the
interconnections between the patches and the code. This distinctive combination
method allows our system to capture more insights from the combined context of
patches and code, hence improving detection precision. Finally, we devise a
probabilistic batch contrastive learning mechanism within batches to augment
the capability of the our LLMDA in discerning security patches. The results
reveal that LLMDA significantly surpasses the start of the art techniques in
detecting security patches, underscoring its promise in fortifying software
maintenance.
