AI-assisted development is changing software development as a whole. “Vibe coding” is new trending term that means fast, AI-driven approach focuses on results rather than understanding code. With great power also comes great responsibility, since there are lots of risks involved.

This blog will show: What it is, why it’s popular, what can go wrong, and a checklist for safely.

What Is “Vibe Coding

“Vibe coding” is programming using natural language. AI generates the code and the developer iterates based on the results. In this approach. “the coder does not need to understand how or why the code works” (Merriam-Webster, 2025) The term became mainstream in 2025 and is now widely used to describe building software by prompting AI and iterating quickly, often with minimal manual code review.

Instead of understanding code, developers when vibe coding focus on a simple question
“Does it work?”

Traditional coding focuses on understanding, structure, and design, while vibe coding focuses on speed, iteration, and end-result.

The Vibe Coding Loop

Vibe coding typically follows a simple loop:

1. Describe feature in natural language
2. AI generates code
3. Run it and test it
4. paste errors back
5. Fix and repeat

This loop further emphasizes the speed of iteration vibe coding allows its users. Results are produced faster and the developer can focus more on refinement of the results or extending features.

Why People Love It: Speed

The biggest reason developers vibe code is speed.
As they say time equals money. AI assistants can greatly reduce task completion time. Its been measured that developers using GitHub Copilot finished a coding task ~55.8% faster
(Peng et al., 2023). Naturally this number can change depending on the scenario but it clearly shows that Ai assistance accelerates implementation.

Reality Check: “Looks Right” ≠ “Is Right”

Results can be misleading, just because code looks correct does not mean it actually is correct. The more “long-term maintenance” and “high stakes,” the more we must shift from vibes to verification. A well known phenomenon hallucination can occur in AI-generated code. While the code can be syntactically correct it can contain factual inaccuracy. ” (Alansari & Luqman, 2025)

Bugs may also be subtle:

• Edge cases may not be handled
• Race conditions may exist
• Wrong assumptions may be built into the logic
• Maintenance becomes risky if nobody understands the code later

This leads to systems that work initially but become fragile and difficult to maintain.

Real-World Security Risks

Often AI-generated code contain serious vulnerabilities for example: Data exfiltration (exportation), unsafe actions by AI agents injecting instructions.

Schreiber & Tippe (2025) — Analysed 7,703 AI-generated files and found thousands of CWE-mapped vulnerabilities.
Sabra, Schmitt & Tyler (2025) — SonarQube analysis showed hard-coded credentials and other severe issues even when unit tests passed. SonarQube is a tool for Static Code Analysis for all languages.
Tihanyi et al. (2024) — Large-scale model study found ~62% of generated code was insecure by formal verification. This formal verification is done through Efficient SMT-based Context-Bounded Model Checker (ESBMC).
Fu et al. (2025) — Copilot and similar assistants produced code with ~24–30% serious Common Weakness Enumeration (CWE) security flaws across languages.

Even if your own code is fine, your tooling and agent workflow can be the attack surface especially when the AI has “powers” (terminal access, file writes, network).

Security Risks in Vibe Coding

Prompt Injection

LLM app guard rails can be manipulated by prompt injection. OWASP lists prompt injection as a top LLM app risk category (OWASP, 2024). UK NCSC warns prompt injection is not like SQLi and may be harder to fully “fix” with a single mitigation (UK National Cyber Security Centre (NCSC), 2025).

Insecure Code Generation

LLMs may generate code with known vulnerabilities Prone to insecure code, but can improve with feedback/hints (Yan et al., 2025)

Guardrails: How to Vibe Code Responsibly

Vibe coding can be powerful but only with proper guardrails.
Responsible use should include:

• Generate tests first
• Force model to justify by explanations
• Use linters + type checking
• Prompt “avoid CWE top issues; use safe defaults”
• Never let an agent run with broad permissions by default
• Code review: auth, input validation, file/network access

Using AI-assisted development should be done with taking the necessary precautions. Keep a fast loop but add verification gates. Effort from  typing code to validating it.

Takeaways

Vibe coding is a new way to code that is becoming more and more standardised. It is fast, iterative, and AI-driven. It helps in rapid development and experimentation, but it also introduces risks in quality, security, and long-term maintainability.

Used responsibly, vibe coding can be a powerful tool in modern development. Without guardrails, it can quickly become hidden tech-debt and security issues.

References

Alansari, A., & Luqman, H. (2025). Large Language Models Hallucination: A Comprehensive Survey (No. arXiv:2510.06265). arXiv. https://doi.org/10.48550/arXiv.2510.06265

Collins Dictionary. (2025). Collins—The Collins Word of the Year 2025 is… https://www.collinsdictionary.com/woty

Fu, Y., Liang, P., Tahir, A., Li, Z., Shahin, M., Yu, J., & Chen, J. (2025). Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study (No. arXiv:2310.02059). arXiv. https://doi.org/10.48550/arXiv.2310.02059

Merriam-Webster. (2025). Vibe coding. https://www.merriam-webster.com/slang/vibe-coding

OWASP. (2024, 2025). LLMRisks Archive. OWASP Gen AI Security Project. https://genai.owasp.org/llm-top-10/

Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The Impact of AI on Developer Productivity: Evidence from GitHub Copilot (No. arXiv:2302.06590). arXiv. https://doi.org/10.48550/arXiv.2302.06590

Sabra, A., Schmitt, O., & Tyler, J. (2025). Assessing the Quality and Security of AI-Generated Code: A Quantitative Analysis (No. arXiv:2508.14727). arXiv. https://doi.org/10.48550/arXiv.2508.14727

Schreiber, M., & Tippe, P. (2026). Security Vulnerabilities in AI-Generated Code: A Large-Scale Analysis of Public GitHub Repositories (Vol. 16219, pp. 153–172). https://doi.org/10.1007/978-981-95-3537-8_9

Tihanyi, N., Bisztray, T., Ferrag, M. A., Jain, R., & Cordeiro, L. (2024). How secure is AI-generated code: A large-scale comparison of large language models. Empirical Software Engineering, 30. https://doi.org/10.1007/s10664-024-10590-1

UK National Cyber Security Centre (NCSC). (2025). Prompt injection is not SQL injection (it may be worse). https://www.ncsc.gov.uk/blog-post/prompt-injection-is-not-sql-injection

Yan, H., Vaidya, S. S., Zhang, X., & Yao, Z. (2025). Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation (No. arXiv:2506.23034). arXiv. https://doi.org/10.48550/arXiv.2506.23034