"So developers draw on existing libraries-often open source projects-to get various basic software components in place. While this approach is efficient, it can create exposure and lack of visibility into software. Increasingly, however, the rise of vibe coding is being used in a similar way, allowing developers to quickly spin up code that they can simply adapt rather than writing from scratch."
"We're hitting the point right now where AI is about to lose its grace period on security. And AI is its own worst enemy in terms of generating code that's insecure. If AI is being trained in part on old, vulnerable, or low-quality software that's available out there, then all the vulnerabilities that have existed can reoccur and be introduced again, not to mention new issues."
Reusing libraries and open source projects accelerates development but increases exposure and reduces visibility into software. Vibe coding enables rapid generation of rough-draft code that developers adapt instead of writing from scratch, potentially missing context-specific considerations. AI training on old, vulnerable, or low-quality code risks reintroducing past vulnerabilities and creating new ones. Local models and natural-language goals do not eliminate reliance on human reviewers to detect flaws or incongruities in AI-generated code. Growing use of plug-and-play code and AI-driven generation complicates software-supply-chain security and requires engineering teams to rethink development lifecycles and security practices.
Read at WIRED
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