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 have resulted in their widespread adoption across various tasks within Software Engineering (SE)%2C 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%2C there is currently no existing survey that focuses on the utilization of LLMs for vulnerability detection and repair. In this paper%2C 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%2C AI%2C and Security conferences and journals%2C covering 36 papers published at 21 distinct venues. By answering three key research questions%2C we aim to (1) summarize the LLMs employed in the relevant literature%2C (2) categorize various LLM adaptation techniques in vulnerability detection%2C and (3) classify various LLM adaptation techniques in vulnerability repair. Based on our findings%2C we have identified a series of challenges that still need to be tackled considering existing studies. Additionally%2C we have outlined a roadmap highlighting potential opportunities that we believe are pertinent and crucial for future research endeavors.?quality=80&w=800)
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