Passing the Security Vibe Check: Security Guardrails for AI-Generated Code and Vibe Coding

TL;DR

Vibe coding, building software mainly through prompts and AI-generated code, can ship working features quickly, but it also increases the odds of classic security failures hiding in plain sight. Common risks include secrets leakage, injection flaws, broken auth, unsafe deserialization, and risky dependencies. The fix is not banning AI. The fix is treating AI output like a junior developer’s draft and adding lightweight guardrails: security prompts, checklists, automated scanning, and mandatory review for high-risk areas.

What “vibe coding” means, and why security teams care

Vibe coding is an AI-assisted development approach where developers describe what they want in natural language and rely on AI-generated code, often moving faster than they can manually review. This shifts security risk from implementation time to review, governance, and the defaults your team enforces.

Vibe coding is the habit of describing what you want in natural language and letting an AI assistant generate the code, then iterating until it runs. It shifts the developer’s job from “write everything” to “review and refine.” Legit Security frames it as AI-assisted development inside the IDE, where suggestions can be accepted and committed rapidly, sometimes without deep inspection.

That speed creates a predictable problem: code can look correct and still be unsafe. Databricks’ AI Red Team highlights that vibe-coded projects can introduce critical vulnerabilities like arbitrary code execution and memory corruption, even when the app “works.”

The real risks: what goes wrong in AI-generated code

If you are trying to assess “how much can we trust AI code,” focus on failure modes that repeat across teams.

1) Insecure deserialization and remote code execution (RCE)

A concrete example: Databricks describes a vibe-coded multiplayer game where the AI chose Python pickle for network serialization. pickle on untrusted input is a known path to arbitrary remote code execution. The app functioned, which made the risk easy to miss.

2) Injection flaws: SQL injection, XSS, template injection

Vidoc’s breakdown maps common vibe-coding failures directly to familiar OWASP categories like input validation issues (SQLi, XSS). These show up when AI generates concatenated SQL strings, weak escaping, or unsafe rendering defaults.

3) Auth and authorization gaps

AI often scaffolds endpoints and forgets to enforce auth consistently. Vidoc calls out “AuthN/AuthZ omissions” as a frequent PR-level failure: missing checks, bypassable role validation, or incomplete session handling.

4) Secrets leakage and unsafe data handling

Secrets leak in two ways:

• The code literally contains secrets (API keys, tokens) because a developer pasted them into prompts or accepted a snippet.
• The code logs sensitive info, stores it incorrectly, or mishandles error messages.

Legit Security explicitly lists leaked secrets and insecure patterns as major risks in AI-assisted workflows.

5) Dependency risks and supply chain traps

Vibe coding can introduce “dependency drift” and risky packages, especially when developers accept suggestions that “seem right.” Vidoc also highlights “slopsquatting,” where the model suggests a plausible but non-existent package name, and an attacker later publishes a malicious package under that name.

Why “guardrails” matter more than perfect prompts

A single “be secure” instruction is not enough. Databricks tested prompting approaches and found that techniques like self-reflection, language-specific prompts, and generic security prompts can significantly reduce insecure code generation, with minimal trade-offs in code quality.

The key insight: you want a system that reliably pushes AI output toward safe defaults, and catches what slips through.

A practical guardrail stack that works

Use this as a minimal baseline for teams that rely on AI coding assistants.

1) A security-oriented system prompt (load once per workspace)

Keep it short and operational. Legit Security recommends setting security-oriented system prompts so the assistant flags risky constructs and must justify dangerous choices.

Example “security prompt” elements (conceptual, adapt to your stack):

• Never use insecure deserialization for untrusted input.
• No dynamic eval, no shell exec with untrusted data.
• Enforce auth and authorization on every route by default.
• Use parameterized queries only.
• Never include secrets in code, tests, logs, or examples.
• Prefer standard, maintained libraries. Avoid new dependencies unless necessary, and explain why.

2) A pull request checklist for AI-generated code

This is the “vibe check” your reviewers run every time AI touched the diff.

AI code security checklist:

• Inputs: Where does user-controlled data enter. Is it validated, length-limited, normalized, and safely encoded.
• Auth: Are all new endpoints protected. Are role checks server-side and consistent.
• Data: Are sensitive fields masked in logs. Are error messages safe.
• Crypto: Are you using proven libraries and safe defaults, not custom crypto.
• Serialization: Is any deserialization happening on untrusted data. If yes, stop and redesign.
• Dependencies: Any new packages. Are versions pinned. Are maintainers reputable. Any unexpected transitive deps.
• Secrets: Any hardcoded keys, tokens, credentials, or “temporary” env values.

3) Mandatory review ownership for high-risk areas

Vidoc recommends mandatory review and domain ownership for risky areas like auth flows, payments, and data handling.
Rule of thumb: if the feature touches money, identity, access control, or sensitive data, it does not merge on AI output alone.

4) Automated scanning in the developer loop

Automate what humans miss:

• Secret scanning (repo, CI, pre-commit hooks)
• SAST for common injection and auth issues
• Dependency scanning and SBOM monitoring
• Policy checks for new packages

Vidoc specifically calls out organization-wide secret hygiene and dependency policy as guardrails that reduce real-world incidents.

5) Test like an attacker, not just like a user

For code that processes untrusted input, add:

• Negative tests (malformed payloads, boundary values)
• Fuzzing where practical
• Basic threat modeling: “How would I break this endpoint.”

Databricks’ examples show that “it works” is not evidence of safety.

What to say when someone asks “can we trust AI code”

AI-generated code can be secure, but it is not designed to optimize for security by default. Trust comes from process, not from the model. If you use guardrails like secure defaults, scanning, and mandatory review for high-risk areas, vibe coding stays productive without becoming a security liability.

Conclusion

Vibe coding is not the problem. Shipping AI-generated code without a safety net is. The same shortcuts that make AI-assisted development feel productive can also smuggle in high-impact issues like insecure deserialization, injection, broken authorization, leaked secrets, and risky dependencies. The most reliable way to trust AI code is to stop treating it as an authority and start treating it as a draft. Add guardrails that force secure defaults, use a simple checklist in every PR, automate scanning for secrets and dependency risk, and require strong human review for auth and data-critical changes. When you build that routine, you can keep the speed benefits of vibe coding and still pass a real security vibe check.