Security by obscurity – A classical Vanilla experience. Code Vanilla in the age of AI & Design your own architecture.
When an AI generates code for you, it knows the secret vulnerabilities of your system as it built it, then when a hacker wants to use AI to attack you, the same AI that built your system has the blueprint for the hacker.
It may sound like science fiction, but it is a real and emerging cybersecurity concern. Modern AI tools, such as OpenAI’s ChatGPT, are designed to write code efficiently, quickly generating entire scripts, modules, or even complex systems with just a few prompts. Developers rely on these systems to save time, avoid repetitive tasks, and leverage best practices. However, the very intelligence that allows these AI models to generate code is the same intelligence that can be repurposed to analyze and exploit it. In other words, the same AI that helps you build your website could, in theory, know exactly how to break it.
When an AI writes code, it creates a blueprint of your system, detailing how functions interact, how databases are accessed, and how authentication flows are structured. Even if you do not intentionally include sensitive information, the generated code can reveal patterns, dependencies, and architectural decisions. For instance, an AI might suggest certain naming conventions, library choices, or authentication methods. If an attacker has access to the same AI technology, they can simulate an understanding of your system based on these patterns, effectively turning the AI from a tool for creation into a tool for attack.
This concept is particularly alarming because AI excels at pattern recognition and optimization. The same system that can write an efficient API can also generate the most likely ways to exploit that API. In other words, the AI knows your system’s strengths and weaknesses because it helped build them. A hacker using AI can analyze code for misconfigurations, unprotected endpoints, or subtle vulnerabilities that a human might overlook. In this scenario, the AI is not a neutral assistant; it is a dual-use tool, capable of both building and deconstructing your digital infrastructure.
The risk increases when developers over-rely on AI-generated code without careful review. If you copy code directly from an AI output into production without auditing it, you might inadvertently leave security gaps. These gaps are not theoretical—they are practical vectors for attack, and the same reasoning that made the AI capable of writing your code can make it capable of probing for those gaps. In effect, the AI has intimate knowledge of your system simply because it created it.
Mitigating this risk requires treating AI-generated code as a starting point rather than a finished product. Every line should be reviewed for security, logic, and compliance. Sensitive credentials should never be included in prompts, and static code analysis tools should be used alongside human oversight. Recognizing that the same intelligence that built your system can also be turned against it is crucial in an age where AI is ubiquitous.
Ultimately, the idea that the same AI can both create and hack your systems is a wake-up call. It is a reminder that every powerful tool carries dual-use potential. The systems that are most secure will be those where developers combine AI efficiency with rigorous security practices, ensuring that the very intelligence that builds their code cannot be used to destroy it.
This is why the most secure code is not built with AI at all… The level of risk ranges between the popularity and sensitivity of the system data or uniqueness of the functionality…Fundamentally a very popular system would be more at risk then your average internal business tool.
One must take this into consideration when building a system. For this reason, relying on opensource projects and General Purpose Frameworks is not a good idea, because the AI has the blueprint and knows all the system vulnerabilities.
For example, node.js npm and several other large opensource projects have had to recently produce emergency patches due to AI introduced vulnerabilities.
