10 Major Vibe-Coding Bugs & Problems (and Proven Ways to Avoid or Fix Them)
What Is Vibe Coding?
“Vibe coding” (coined by Andrej Karpathy, 2025) is the practice of describing what you want in plain English, pressing ▶️ on an AI coding assistant, and accepting the generated code with minimal human review. It’s great for weekend prototypes, but the lack of diagnostics, architecture, and QA can turn small experiments into maintenance nightmares.
This article distills real-world post-mortems, academic studies, and industry blog posts into 10 high-impact problems you’re likely to face—and a battle-tested playbook for preventing or fixing them on Replit or any cloud IDE.
Quick Reference Table
# | Problem | Fast Symptom Check | Critical Risk |
|---|---|---|---|
1 | Security vulnerabilities | SQL-injection, hard-coded secrets | Data breach, compliance fines |
2 | Missing architecture / planning | Circular dependencies, feature re-writes | Cost overruns, blown deadlines |
3 | No documentation | On-boarding takes days, not hours | High turnover, tribal knowledge |
4 | Inconsistent style & duplication | "mysterious" functions, dead code | Scalability bottlenecks |
5 | Technical debt snowball | Latency spikes, fragile deploys | Total rewrite required |
6 | Debugging black box | "Regenerate until it works" loop | Days lost chasing heisenbugs |
7 | Prompt fragility & context limits | LLM forgets earlier decisions | Chaotic regressions |
8 | Version-control disasters | Accidental file nukes, merge hell | Data loss, broken main branch |
9 | Dependency hell | Deprecated libs, supply-chain risk | Production outages |
10 | Legal / IP issues | Unlicensed or GPL code snippets | Lawsuits, forced open-sourcing |
1. Security Vulnerabilities
Why It Happens
LLMs train on public code (including bad code) and happily suggest outdated crypto, raw SQL, or even malicious payloads. New vibe coders rarely review lines individually.
How to Avoid
- Static analysis early & often
# Replit shell
pip install bandit safety
bandit -r src/
safety check - Threat-model prompts: add a system instruction “reuse battle-tested libraries, reject insecure patterns.”
- Automated dependency scanning via GitHub Dependabot or Snyk.
How to Fix Existing Code
- Run OWASP dependency-check & Bandit, export HTML reports.
- For each finding, create an issue labeled security.
- Patch with least-privilege principles; write regression tests.
2. Missing Architecture & Planning
Symptoms
Frontend references non-existent API routes; DB schema rewrites every sprint.
Prevention Blueprint
• Product Requirement Document (PRD) + System Diagram before first prompt.
• Break features into atomic tickets; feed one ticket at a time to the LLM.
Remediation Steps
- Draw current vs. target architecture (C4 diagram).
- Incrementally refactor—start at boundaries (e.g., API layer), add tests, then refactor internals.
3. No Documentation
Solution
• Ask AI to generate docstrings while writing code
• Host docs in /docs folder and auto-publish with MkDocs.
Retrofitting: run a script that parses modules and prompts ChatGPT: “Explain intent, parameters, returns of this function.”
4. Inconsistent Style & Code Duplication
Enforce a single linter+formatter combo (e.g., Black + isort + Flake8). Add a pre-commit hook:
pip install pre-commit
pre-commit install
Run jspcd or pmd-cpd to spot duplicates, then consolidate utilities.
5. Technical-Debt Snowball
Vibe codingTrack debt like any feature:
[tech-debt] Replace ad-hoc caching with Redis
Schedule a “Debt Day” each sprint; cap total open debt points (e.g., ≤20).
6. Debugging Black Boxes
- Force unit tests first: pytest -cov before running the main app.
- Use Python’s pdb, JS’s node --inspect; step through AI code and annotate findings.
- Teach the LLM: “Here’s the stack trace, why did it fail?”—but always verify.
7. Prompt Fragility & Context Limits
Store canonical decisions (naming, db schema, auth flow) in a contract.md. Prepend this file to every chat session to keep the AI on-track.
8. Version-Control Disasters
Golden Rules
• Never run AI on an uncommitted working tree.
• Branch per feature (git checkout -b feat/payment-flow)
• Tag stable checkpoints: git tag v0.1-prototype.
If catastrophe strikes: git reflog → hard-reset.
9. Dependency Hell & Supply-Chain Risk
Pin versions in requirements.txt /package.json. Automate weekly updates:
# .github/workflows/updates.yml
schedule: ['0 3 * * 0']
Lockfiles + SBOM (Software Bill of Materials) via Syft or CycloneDX protect against typosquatting packages.
10. Legal / IP Landmines
Ask the LLM: “Cite license for every 3rd-party snippet you propose.” Run FOSSA or ClearlyDefined. Remove or re-implement GPL code if your project is closed-source.
End-to-End Hardening Checklist
- PRD and architecture diagram committed.
- Pre-commit with lint/format/test hooks.
- Static analysis + dependency scanning CI jobs.
- Contract.md for canonical decisions.
- Weekly “Debt Day” on sprint board.
- SBOM generated on release.
- License scan passes.
Conclusion
Vibe coding lets you ship a prototype in hours, but unchecked it also ships liabilities.
By layering conventional engineering hygiene—planning, tests, security scans, version control—on top of AI tooling, you get the best of both worlds: fast iteration and robust, scalable software.