5 Signs Your Team Is Already Vibe Coding (And What to Do About It)

Introduction

The shift toward AI-accelerated development has made it easier than ever for teams to build and deploy quickly. But with this velocity comes risk — particularly when developers begin adopting workflow patterns that deprioritize manual validation, peer review, or security checks. These behaviors are increasingly common in organizations adopting what’s now called “vibe coding.”

If you're concerned that your team may already be operating in this mode, here are five indicators to watch for — and how to address them.

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1. AI-Generated Pull Requests Are the Norm

When a majority of pull requests include significant contributions from tools like GitHub Copilot, Cursor, or ChatGPT — and they’re being merged with minimal oversight — that’s a clear sign of vibe coding. While AI accelerates productivity, it can also introduce insecure or poorly structured code.

What to do: Integrate automated security scanning into pull request workflows. Use tools like Mobb to auto-remediate issues before code merges.

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2. Manual Code Review Has Slowed or Stopped

Traditional code reviews are often seen as blockers in fast-moving teams. But skipping them entirely increases the likelihood of undetected logic errors and security flaws. If peer review becomes rare or ceremonial, security debt accumulates.

What to do: Shift left — but don’t skip steps. Adopt a dual-review model where security and code quality are evaluated by automation, supplemented by strategic human review.

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3. Third-Party Dependencies Are Added Without Vetting

Vibe coders often install packages suggested by AI tools without checking for version age, vulnerability history, or maintenance status. These components can carry critical risks.

What to do: Use a dependency scanner in your CI/CD pipeline. Consider setting up policies that require approval or automated risk assessment before new libraries are merged.

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4. Developers Are Shipping Code Without QA Involvement

When developers are empowered to write, test, and ship code on their own — with no integration into QA or staging environments — there’s a greater chance that broken logic or vulnerable code reaches production.

What to do: Introduce gates for high-risk workflows. Even if QA is limited, ensure functional and security tests run in CI and block promotion if issues are detected.

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5. Security Incidents Are Being Found Post-Deployment

If the only time security teams discover vulnerabilities is during production scans or breach response, the development process is likely skipping key security practices. This reactive model is a hallmark of ungoverned vibe coding.

What to do: Introduce remediation tooling early in the pipeline. Mobb’s auto-remediation engine can reduce time-to-fix without interrupting developer velocity.

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Conclusion

Vibe coding is not inherently bad — but it does require a deliberate approach to security. By identifying the patterns early and implementing automated safeguards, organizations can benefit from the speed of AI development while reducing exposure.

To explore the core security risks of vibe coding in more depth, read our breakdown here.