Are LLMs Equally Good (or Bad) at Building Secure Software?

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In the tricky business of choosing the right artificial intelligence coding models for security and cost, development teams need to keep in mind two important factors. First, even the best and best-known AI coding assistants vary widely in performance, both between and within individual frameworks. Each has strengths and weaknesses, and there is no across-the-board best of the bunch; a model that leads the pack in one framework may be in last place in another. And second, when it comes to generating secure code, you don’t necessarily get what you pay for. The real costs of using AI agents can mount quickly depending on an agent’s use of tokens, and some models are more inclined than others to make extensive use of them, but there’s no correlation between cost and the level of security provided. Those are among the findings of an in-depth study conducted by Secure Code Warrior and the Royal Melbourne Institute of Technology (RMIT) to evaluate the security behavior of leading AI coding models. With many software engineering teams moving from AI coding assistants into full agentic AI code generation and increasing the amount of code they produce exponentially, ensuring the security of that code must be a top priority. The study produces practical guidance for organizations that are getting on board the AI-assisted or agentic code development train. Clear Results, but No Clear Winner For the initial study, we tested six frontier large language models (LLMs), producing 660 complete application codebases across eleven language/framework combinations. Each codebase was scanned by …

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