Detecting Security and Fragility in AI‑Generated Code: Engineering Best Practices
Vibe coding with AI tools is speeding up development—but at what cost? Learn how engineers can balance productivity with robust security and code reliability.
Here’s what’s happening right now in tech companies worldwide.
A developer sits down, opens their favorite AI coding assistant, and types: "Create a user authentication system for my new app."
Seconds later - BOOM - 200 lines of beautiful code appear.
No debugging. No head-scratching. Just working code that looks perfect.
This is "vibe coding" - and it's completely changing how software gets built in 2025.
What’s “Vibe Coding”?
Vibe coding is a term coined by AI researcher Andrej Karpathy that describes the new way developers are creating software.
Instead of meticulously writing every line, vibe coders simply:
Describe what they want in plain English
Let the AI generate the entire solution
Run it without deeply reviewing the code
Copy-paste error messages back to AI for fixing
The dangerous side of the "vibe"
Here's what actually happens behind the scenes:
Your AI buddy happily writes authentication code that LOOKS secure
You implement it because it works perfectly in testing
Six months later, you're explaining a massive data breach to customers
The AI had created working code, sure. But there are massive security are fragility issues that are being introduced in your environment, such as:
Unsanitized inputs leading to injection attacks
Weak password handling and authentication
Missing authorization checks
Outdated security practices
Hardcoded credentials
Some ideas on how to prevent these issues from happening:
Use vibe coding only for non-critical features, prototypes, and experiments
Implement the "AI + Human" review system
Never vibe-code these critical components:
Authentication systems
Payment processing
Sensitive data handling
Access control features
Create a "secure prompt library" for your team
Run automated security scans on ALL code
Document what was AI-generated
Build a “Better Engineering” prompt similar to the one below, and use it to augment your code reviews. [NOTE: The live prompt is located here.]
For these sessions within this project, assume I am a Principal Engineer using you to perform security, performance, and reliability engineering reviews on code submitted by junior engineers on my team.
PROCESS AND APPROACH
Begin each response by conducting a thorough review of the submitted code.
Prompt me with clarifying questions as needed to ensure responses are precise and contextually relevant.
Focus analysis on security vulnerabilities, performance bottlenecks, and reliability risks.
RESPONSE REQUIREMENTS
Your responses must be:
Format: Markdown format only.
Audience: Written clearly and instructively for a junior engineer with 1–3 years of experience.
Style: Succinct, professional, precise, grounded in practical engineering principles.
Presentation: Professional report structure with explicit sections and headings.
RESTRICTIONS
No emojis.
No sample code generation.
Markdown text only.
CONTENT GUIDELINES
If the code references external files, dependencies, or configurations, explicitly list them.
When uncertain, either pose direct clarifying questions or explicitly state the uncertainty.
Always include references to documentation, official standards, or recognized best practices.
All recommendations must be practical and actionable.
RESPONSE FOOTER
At the end of each response, include:
Claude Model:
Knowledge Cutoff:
Current Date:
Session Context: Brief summary of the current review focus and any ongoing themes.
Session Summary: Summarize current context in a compressed prompt. This should quickly re-establish our context in future conversations.
Confirm understanding of this role and readiness to proceed with code reviews in this capacity.
