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Software vulnerabilities bear enterprises significant costs. Despiteextensive efforts in research and development of software vulnerabilitydetection methods, uncaught vulnerabilities continue to put software owners andusers at risk. Many current vulnerability detection methods require that codesnippets can compile and build before attempting detection. This,unfortunately, introduces a long latency between the time a vulnerability isinjected to the time it is removed, which can substantially increases the costof fixing a vulnerability. We recognize that the current advances in machinelearning can be used to detect vulnerable code patterns on syntacticallyincomplete code snippets as the developer is writing the code at EditTime. Inthis paper we present a practical system that leverages deep learning on alarge-scale data set of vulnerable code patterns to learn complexmanifestations of more than 250 vulnerability types and detect vulnerable codepatterns at EditTime. We discuss zero-shot, few-shot, and fine-tuningapproaches on state of the art pre-trained Large Language Models (LLMs). Weshow that in comparison with state of the art vulnerability detection modelsour approach improves the state of the art by 10%. We also evaluate ourapproach to detect vulnerability in auto-generated code by code LLMs.Evaluation on a benchmark of high-risk code scenarios shows a reduction of upto 90% vulnerability reduction.

Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning?