As 2025 rolls out, the way we create software is evolving fast. Coding is no longer just endless lines and perfect semicolons. Developers now work side by side with AI-powered assistants—think GitHub Copilot, Amazon CodeWhisperer, ChatGPT—to build apps quicker than ever. This shift in workflow, sometimes called “vibe coding,” has raised developer productivity, but it’s also opened fresh doors for security headaches.
Take Sanket Saurav, founder of DeepSource. He’s worried that developers are leaning on AI-generated code without really checking what’s under the hood. His concern? Flaws might slip in undetected, much like the major SolarWinds breach of 2020. Thorough code review and static analysis, he argues, are now more vital than ever to catch risky coding patterns before they become big problems.
AI code generators can miss crucial details. They might misuse code libraries or even hallucinate—dreaming up libraries that don’t exist or suggesting old, dangerous ones. There’s also “slopsquatting,” where attackers insert malicious packages with names similar to popular libraries. If an AI pulls in the wrong dependency, it could open the gates to hackers.
Professor Rafael Khoury from Université du Québec en Outaouais has been digging into these issues. His research shows that, if you don’t give AI enough context, it’s quick to generate insecure code. But he sees hope: blending methods like FLAG (Finding Line Anomalies with Generative AI) with security checks. Essentially, FLAG spots suspect lines in AI-generated code, which teams can then double-check or ask the AI to refine. It’s not a silver bullet, but it makes a dent in the vulnerability problem.
Still, humans can’t just sit back. Kevin Hou, who leads product engineering at Windsurf, believes projects should be broken into smaller, digestible chunks. That makes it easier to review what the AI produces, test better, and catch anything odd before it spreads.
Windsurf’s team has seen over 5 billion AI-generated lines and knows it’s easy for quality to slip. To keep their developers informed and engaged, they focus hard on user experience—making tools clear, surfacing context at the right time, and ensuring every code change is easy to understand and review.
In this age of “vibe coding,” speed and convenience are tempting. But it’s critical not to trade away safety. Hallucinated libraries, slopsquatting, and old-fashioned mistakes are real risks, but so are the defenses: smart tools, vigilant review, and a healthy skepticism of what AI suggests.
The bottom line? Treat AI as a team member, not a replacement. Review what it writes. Test it. Trust, but always verify. That’s the best way forward as coding grows smarter—and riskier—every year.
Read the full story at: https://www.techtarget.com/searchsecurity/tip/Security-risks-of-AI-generated-code-and-how-to-manage-them
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