%201.svg)
.svg)
Introduction
Vibe coding enables developers to move faster than ever — but that speed comes with trade-offs. As teams adopt AI assistants like Copilot, Cursor, and Windsurf, traditional security controls are often skipped or deferred. In this environment, bugs aren’t just introduced more frequently — they’re harder to detect, reproduce, and remediate.
This article outlines the primary security risks associated with AI-assisted coding workflows and how security teams can stay ahead of them.
1. Insecure Defaults and Blind Copying
AI tools generate code based on patterns in public repositories, many of which contain outdated or insecure logic. Developers often accept these suggestions without fully understanding their implications.
Example: AI-generated server configurations may disable authentication or allow open CORS access — exposing sensitive endpoints.
How to Mitigate: Integrate pre-commit linters and baseline secure templates for commonly scaffolded code blocks.
2. Missing Input Validation
AI lacks application context. That means code generated for form handlers, APIs, or file uploads often skips critical input validation — leading to injection or denial-of-service vulnerabilities.
Example: An LLM generates an Express.js route that accepts untrusted JSON but doesn’t sanitize or restrict incoming data.
How to Mitigate: Require validation libraries in core dependencies, and scan for unsafe patterns using SAST tools.
3. Improper Use of Third-Party Dependencies
AI suggestions frequently include installing third-party packages — but developers may not verify if the package is maintained, reviewed, or safe.
Example: Adding a logging or crypto library with known CVEs simply because it solved a narrow task.
How to Mitigate: Use automated dependency analysis in CI/CD. Gate high-risk dependencies with security scoring tools like OWASP Dependency-Check or Scorecards.
4. Over-Scoped Permissions and Secrets Exposure
When AI generates infrastructure-as-code or cloud policies, it often defaults to permissive access controls. If secrets are embedded in the code or stored insecurely, attackers can escalate privileges quickly.
Example: Copilot generates a Lambda function that includes plaintext API keys and grants admin IAM privileges.
How to Mitigate: Scan IaC for permission bloat and integrate secret detection into commit hooks.
5. Lack of Ownership and Review
AI-generated code can move from dev to prod without any human review. In fast-paced teams, this lack of oversight increases the likelihood that insecure or noncompliant code reaches production.
Example: Developers bypass PRs or merge AI-generated code based solely on passing tests — even when logic is flawed.
How to Mitigate: Enforce peer review or automated policy-based gating for high-impact files and risky changes.
Conclusion
Vibe coding introduces a new development model — but not a new threat model. The same vulnerabilities persist — they’re just harder to catch at speed. Security teams must adapt by embedding detection, validation, and remediation into the tools and workflows developers already use.
Mobb helps close the gap by automatically fixing known vulnerabilities directly in the code repository.
To determine if your team is already vulnerable, review the signs of vibe coding in action, or start securing your AI workflows with this guide.
in 60 seconds or less.
That’s the Mobb difference