Cybersecurity Risks of AI-Generated Code

Investment Rating - The report does not explicitly provide an investment rating for the industry. Core Insights - The report identifies three broad categories of cybersecurity risks associated with AI code generation models: 1) generating insecure code, 2) models being vulnerable to attacks, and 3) downstream cybersecurity impacts [2][4][26]. Summary by Sections Executive Summary - Recent advancements in AI, particularly large language models (LLMs), have enhanced the ability to generate computer code, which presents both opportunities and cybersecurity risks [2][12]. Introduction - AI code generation models are increasingly adopted in software development, with a significant percentage of developers using these tools [10][11]. Background - Code generation models include specialized models for coding and general-purpose LLMs, which have seen rapid improvements and adoption in recent years [14][15]. Increasing Industry Adoption of AI Code Generation Tools - The adoption of AI coding tools is driven by productivity gains, with studies indicating that developers can complete tasks significantly faster when using these tools [23][25]. Risks Associated with AI Code Generation - The report highlights the risks of insecure code generation, model vulnerabilities, and potential downstream impacts on cybersecurity as these models become integral to the software supply chain [26][27]. Code Generation Models Produce Insecure Code - Research indicates that a substantial percentage of code generated by AI models contains vulnerabilities, with various studies showing rates of insecure code ranging from 40% to over 70% [29][30][69]. Models' Vulnerability to Attack - AI models are susceptible to various types of attacks, including data poisoning and backdoor attacks, which can compromise their outputs [33][35]. Downstream Impacts - The increasing reliance on AI-generated code may shift the vulnerability landscape, potentially leading to new classes of vulnerabilities and impacting future model training [39][40]. Challenges in Assessing the Security of Code Generation Models - Evaluating the security of AI-generated code is complicated by factors such as programming language differences, model types, and the lack of standardized assessment tools [41][42]. Evaluation Results - The evaluation of five AI models revealed a high rate of unsuccessful verification, with approximately 48% of generated code snippets containing bugs [64][69]. Policy Implications and Further Research - The report emphasizes the need for proactive policy measures to address the cybersecurity risks associated with AI-generated code, including the responsibility of AI developers and organizations to ensure code security [83][84][86].