  The significant advancements in Large Language Models (LLMs) have resulted in
their widespread adoption across various tasks within Software Engineering
(SE), including vulnerability detection and repair. Numerous recent studies
have investigated the application of LLMs to enhance vulnerability detection
and repair tasks. Despite the increasing research interest, there is currently
no existing survey that focuses on the utilization of LLMs for vulnerability
detection and repair. In this paper, we aim to bridge this gap by offering a
systematic literature review of approaches aimed at improving vulnerability
detection and repair through the utilization of LLMs. The review encompasses
research work from leading SE, AI, and Security conferences and journals,
covering 36 papers published at 21 distinct venues. By answering three key
research questions, we aim to (1) summarize the LLMs employed in the relevant
literature, (2) categorize various LLM adaptation techniques in vulnerability
detection, and (3) classify various LLM adaptation techniques in vulnerability
repair. Based on our findings, we have identified a series of challenges that
still need to be tackled considering existing studies. Additionally, we have
outlined a roadmap highlighting potential opportunities that we believe are
pertinent and crucial for future research endeavors.
