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Codex, a large language model (LLM) trained on a variety of codebases,exceeds the previous state of the art in its capacity to synthesize andgenerate code. Although Codex provides a plethora of benefits, models that maygenerate code on such scale have significant limitations, alignment problems,the potential to be misused, and the possibility to increase the rate ofprogress in technical fields that may themselves have destabilizing impacts orhave misuse potential. Yet such safety impacts are not yet known or remain tobe explored. In this paper, we outline a hazard analysis framework constructedat OpenAI to uncover hazards or safety risks that the deployment of models likeCodex may impose technically, socially, politically, and economically. Theanalysis is informed by a novel evaluation framework that determines thecapacity of advanced code generation techniques against the complexity andexpressivity of specification prompts, and their capability to understand andexecute them relative to human ability.

A Hazard Analysis Framework for Code Synthesis Large Language Models