Vibe Coding with LLMs: Security Flaws and Real-World Risks Revealed

Vibe coding, a workflow where developers prompt large language models (LLMs) to generate code with minimal review, is surging in popularity. But new research and industry analysis reveal that this approach is highly susceptible to common and severe security flaws, making it a growing concern for cybersecurity professionals and organizations alike.¹

What is Vibe Coding?

Coined by AI researcher Andrej Karpathy, "vibe coding" describes a trend where developers describe their intent—sometimes in vague or high-level terms—and let LLMs like OpenAI's o3 or Anthropic's Claude Sonnet generate the code, often using agentic code editors such as Windsurf Editor, Cursor, or the open source alternative, Void Editor. While this accelerates development, it often bypasses traditional code review and security checks, leading to fragile and insecure systems.², ³, ⁴, ⁵

Most Common Security Flaws in LLM-Generated Code

Insecure Code Generation

Studies show that 36–40% of LLM-generated code contains security vulnerabilities. Common issues include SQL injection, hardcoded secrets, improper authentication, and missing input validation.⁶, ⁷

Leaked Secrets and Credentials

Repositories using AI code assistants are 40% more likely to leak API keys, passwords, or tokens, increasing the risk of data breaches and unauthorized access.⁸

Prompt Injection and Model Exploitation

LLMs are vulnerable to prompt injection attacks, where malicious prompts manipulate the model into generating insecure or unintended code. This risk is especially pronounced in automated coding workflows.⁹

Lack of Security Best Practices

LLMs often omit critical security controls unless explicitly prompted, resulting in code that lacks secure defaults, proper output encoding, or up-to-date mitigations against known vulnerability classes.

Industry and Research Guidance

Security experts and organizations—including the Cloud Security Alliance and leading academic researchers—urge developers to:

• Always review and test LLM-generated code for security flaws
• Use static analysis and manual review alongside AI tools
• Avoid copying code with hardcoded secrets or without proper validation
• Stay informed on evolving LLM security risks via trusted sources

Final Thoughts

Vibe coding with LLMs offers speed and convenience, but it comes with significant security trade-offs. Organizations should treat LLM-generated code with caution, enforce security best practices, and use AI as an assistant—not a replacement—for secure software development.

Related Articles

• AI Revolutionizes Cybersecurity Training: Meet ARCeR, the Intelligent Cyber Range Creator
• ScamNet & Llama‑3: AI Tool Detects Fraudulent Shopping Sites with 95% Accuracy