The rapid proliferation of the Internet of Things (IoT) has introduced significant security challenges, primarily due to the widespread reuse of open-source software (OSS) components. This practice leaves IoT projects particularly vulnerable to 1-day vulnerabilities especially when developers customize the reused OSS code, rendering template patches inapplicable.

In this thesis, we propose 1DRep, a repair tool designed to automatically detect and repair 1-day vulnerabilities in reused C/C++ IoT OSS components. First, 1DRep integrates with the vulnerability detection tool V1SCAN to identify vulnerable code snippets in target programs. It then employs a large language model (LLM), GPT-4o, to generate and apply tailored fixes, addressing both exactly reused and modified vulnerable code without altering developer-customized functionality.

Our evaluation demonstrates that 1DRep effectively repaired 39 out of 40 CVEs
(97.5\%) in 11 target vulnerable IoT projects, including 14 out of 15 modified CVEs (93.3\%), and effectively fixed 81 out of 90 artificially created vulnerable reuses (90\%). We constructed an IoT-specific dataset containing 1,020 C/C++ libraries which supplements an exisiting dataset Centris to enhance the detection of OSS components commonly reused in IoT projects.
Additionally, we provided security reports containing customized patches for the modified CVEs by creating GitHub issues or pull requests in the affected projects.

The results indicate that 1DRep is a promising tool for automatically repairing 1-day vulnerabilities in IoT projects, particularly in scenarios where developers' customized reuses make traditional patching techniques ineffective.

Lastly, despite the promising outcomes, limitations such as reliance on the precision of the detection model and challenges with complex vulnerabilities highlight areas for future research. Enhancing the detection mechanisms, expanding the CVE dataset, and refining repair strategies are critical steps toward improving the tool's effectiveness.