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There is burgeoning interest in designing AI-based systems to assist humansin designing computing systems, including tools that automatically generatecomputer code. The most notable of these comes in the form of the firstself-described `AI pair programmer', GitHub Copilot, a language model trainedover open-source GitHub code. However, code often contains bugs - and so, giventhe vast quantity of unvetted code that Copilot has processed, it is certainthat the language model will have learned from exploitable, buggy code. Thisraises concerns on the security of Copilot's code contributions. In this work,we systematically investigate the prevalence and conditions that can causeGitHub Copilot to recommend insecure code. To perform this analysis we promptCopilot to generate code in scenarios relevant to high-risk CWEs (e.g. thosefrom MITRE's "Top 25" list). We explore Copilot's performance on three distinctcode generation axes -- examining how it performs given diversity ofweaknesses, diversity of prompts, and diversity of domains. In total, weproduce 89 different scenarios for Copilot to complete, producing 1,689programs. Of these, we found approximately 40% to be vulnerable.

Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions