AI tools are changing the game for coding, making it easier than ever to build websites, apps, and software without needing to be a tech wizard. For example, tons of website builder companies like Webflow and Wix now let people use AI to create digital products with little to no coding experience.

Meanwhile, professional developers are getting a huge productivity boost from tools like GitHub Copilot. Sounds great, right? Unfortunately, this comes with a concerning downside.

 

The Double-Edged Sword of AI in Coding

While AI can speed up coding and even help find and fix security issues, it also introduces new risks. A report from the Center of Security and Emerging Technology breaks down three big risks linked to AI-generated code:

1. Insecure Code Generation – Sometimes the AI spits out code that looks right but isn’t secure.
2. Vulnerability to Attacks – Hackers could exploit flaws in generated code.
3. Cybersecurity Feedback Loops – Mistakes in generated code could influence future AI systems, making them less secure over time.

The problem is that most code-generating AIs focus on functionality, not security. They’re great at writing code that works but not always code that’s safe.

 

Outdated Knowledge Equals Outdated Code

Another big flaw in Large Language Models (LLMs) like the ones powering these AI tools is that they’re trained on data up to a certain point in time. They don’t know about security vulnerabilities discovered after their training. Not helpful when threat actors are finding new ways every day to bypass security measures.

AI tools are undeniably powerful and can save developers tons of time. But as they become more integrated into the coding process, it’s important to stay vigilant about security. Double-checking generated code and keeping up with the latest security practices can go a long way.

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