Why Your AI Code Assistant Might Be Shipping CVEs

Experimentation with large language models (LLMs) like GPT-4 and Claude 4 Sonnet for personal projects has revealed that these models often import outdated open-source dependencies, some of which contain known vulnerabilities. This issue arises because LLMs rely on past training data and are not constantly updated with real-time information unless specifically connected to the internet and equipped with tools to fetch the latest data. A notable example includes the Axios library, where a critical vulnerability was not recognized by the model due to its training cutoff date. To address these challenges, developers and security teams are encouraged to enhance their security awareness by prompting models to use the latest versions, implementing rules for dependency checks in their IDEs, and utilizing security tools like the Endor Labs MCP Server to provide up-to-date security context. These measures ensure that code generated by LLMs is secure, up-to-date, and does not inadvertently introduce vulnerabilities into software projects.

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