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A system that leverages LLMs to automatically generate IJON-style annotations is designed and it is observed that LLMs can generate annotations that achieve comparable results to human generated ones, opening the door for future research to perform further studies on the impact of annotations at scale.
Reproducibility in vulnerability datasets can now be achieved at scale, with ARVO demonstrating 81% success in reproducing real-world vulnerabilities.
LLM-based vulnerability repair can be significantly improved by focusing on root cause analysis, leading to more robust and less superficial patches than current methods.
Deobfuscation just got a whole lot easier: PUSHAN cracks virtualization-obfuscated binaries without relying on brittle trace analysis or expensive symbolic execution.
