Open-source software is the backbone of the internet, but finding and fixing security flaws is a constant challenge. Fuzzing tools like OSS-Fuzz do a great job of uncovering vulnerabilities, but patching them remains largely manual, time-consuming, and prone to delays. Could AI help automate this critical process? Researchers investigated using an LLM-powered agent called CodeRover-S to automatically repair security bugs detected by OSS-Fuzz. They adapted a general-purpose coding agent, AutoCodeRover, to the specific task of fixing security vulnerabilities, augmenting it with dynamic call graphs for richer context and type analysis for improved patch compilation. The results are promising. CodeRover-S successfully repaired over half of the tested vulnerabilities, showing the potential of AI to significantly accelerate the patching process and enhance software security. However, the research also reveals challenges. CodeRover-S struggles with more nuanced memory management bugs like use-after-free vulnerabilities, which require a deeper understanding of program semantics. Furthermore, common metrics for evaluating patch quality, such as CodeBLEU scores, proved unreliable in predicting the effectiveness of a security fix. Instead, the researchers emphasize the importance of test-based validation in security contexts. This study highlights the emerging potential of AI agents to play a pivotal role in securing the software we rely on. By integrating AI into the continuous fuzzing and patching pipeline, we could move towards a future where vulnerabilities are detected and fixed automatically, minimizing the window of exposure and bolstering defenses against malicious attacks. While challenges remain, this research offers a glimpse into a future where AI significantly elevates software security.