Dall-E for coders? That’s the promise behind vibe coding. This is a term that describes the use of natural language to create software. This leads to a new era of AI-generated code, but introduces the vulnerability of “silent killer.” Despite its perfect test performance, it is an exploitable flaw that circumvents traditional security tools.
A detailed analysis of safe vibe coding practices is available here.
TL;DR: Safe Vibe Coding
Vibe coding, which uses natural language to generate software with AI, is revolutionizing development in 2025. However, while accelerating prototyping and democratizing coding, it also introduces vulnerabilities of “silent killers.”
In this article,
Real-world examples of code generated in generated AI shock statistics: Why 40% higher secret exposure in AI-assisted repos LLM is the reason LLM omitted security unless it explicitly encourages a comparison of secure prompt technology and tools (GPT-4, Claude, Cursor, etc.).
Conclusion: AI can write code, but it doesn’t protect it unless you ask. Still, it needs to be verified. Speed without security is a quick obstacle.
introduction
Vibe coding exploded in 2025. It was created by Andrej Karpathy. The idea is that anyone can explain what they want and regain functional code from a large language model. In Karpathy’s words, Vibe Coding means “suffling into the atmosphere, accepting exponential functions, and even forgetting that codes exist.”
From prompts to prototypes: new development models
This model is no longer theoretical. Pieter Level (@Levelsio) has famously launched the multiplayer flight SIM, Fly.pieter.com, using AI tools such as Cursor, Claude and Grok 3.
“Create a 3D flying game in your browser.”
Ten days later, he made $38,000 from the game and by March 2025 he was making about $5,000 a month from the ads as the project had expanded to 89,000 players.
But it’s more than just a game. Vibe Coding is used to build early versions of MVPs, internal tools, chatbots, and even full stack apps. According to recent analysis, almost 25% of Y-combinator startups use AI to build their core codebases.
Before dismissing this as a ChatGpt hype, consider the scale. We’re not talking about toy projects or weekend prototypes. These are funded startups that process real user data, process payments, and build production systems that integrate with critical infrastructure.
promise? Faster iterations. More experiments. There is little gatekeeping.
However, this speed has a hidden cost. The code generated by AI creates what security researchers call “silent killer” vulnerabilities. This creates code that works perfectly in testing, but bypasses traditional security tools and contains exploitable flaws that withstand CI/CD pipelines and reach production.
Problem: Security is not automatically generated
Catching is easy: AI generates what you want. In many cases, this means that critical security features are excluded.
The problem is not just a naive prompt, it’s systematic.
LLMS is trained to complete, not protect. It is usually ignored unless security is explicitly present at the prompt. Tools like GPT-4 may suggest redundant patterns that mask deprecated libraries or subtle vulnerabilities. Sensitive data is often hardcoded because the model “sees it that way” in training examples. Prompts such as “Build a Login Form” often produce unstable patterns. This is password storage, MFA, and broken authentication flow.
According to this new, secure vibe coding guide, this leads to what they call “security by omission.” In the case cited, the developer used AI to get the stock price from the API and committed the mistakenly hardcoded key to GitHub. A single prompt resulted in a real vulnerability.
Another real example is: The developers have urged AI to “create a password reset feature that emails reset links.” AI generated working code that successfully sent emails and verified tokens. However, we used time string comparisons that are not compatible with token validation to create a timing-based side-channel attack that allows attackers to brute-force reset the token by measuring response times. This function passed all functional tests, worked perfectly for legitimate users, and was impossible to detect without a specific security test.
Technical reality: AI needs guardrails
This guide will delve deep into how various tools handle safe code and how to properly inspire them. for example:
Claude tends to be more conservative, and often flags dangerous code in comments. Cursor AI is great for real-time lint and can highlight vulnerabilities during refactors. GPT-4 requires certain constraints such as: [feature] Protection for the OWASP Top 10. Includes rate limiting, CSRF protection, and input validation. ”
It also includes a secure prompt template such as:
#Anxiety: Building a file upload server
Lesson: If you don’t say that, the model won’t do that. And even if you say it, you still need to check.
Regulatory pressure is rising. The EU AI Act categorizes the implementation of several atmospheric coding as “high-risk AI systems” that require conformance assessments, particularly in critical infrastructure, healthcare and financial services. Organizations need to document AI’s involvement in code generation and maintain an audit trail.
Real safe atmosphere coding
For those deploying atmospheric coding during production, the guide suggests a clear workflow.
Prompts using security context – Writes prompts like threat modeling. Multi-Step Prompt – Generate first and then ask the model to check for its own code. Automated Testing – Integrate tools such as Snyk, Sonarqube, Gitguardian and more. Human Review – Assume that output generated for all AI is not safe by default. #Anxiety AI output: token == expect_token: #secure version: hmac.compare_digest(token, expects_token): if hmac.compare_digest:
Accessibility Security Paradox
Vibe coding democratizes software development, but without guardrails, democratization creates systematic risks. The same natural language interface that allows non-technical users to build applications is also removed from understanding the security implications of requests.
Organizations address this through a layered access model. It’s a surveillance environment for domain experts, guided development for citizen developers, and full access only for security-trained engineers.
Vibe coding ≠ code replacement
The smartest organizations treat AI as an enhancement layer rather than a substitute layer. They use vibe coding below
Prototype experimental features for early testing by learning new frameworks with guided scaffolds to accelerate boring boilerplate tasks
But they still rely on experienced engineers in architecture, integration and final polish.
This is a new reality in software development. English is becoming a programming language, but only if you still understand the underlying system. Organizations that succeed in atmospheric coding are not replacing traditional development, but augmenting it with security-first practices, proper surveillance, and the perception that no security speed is a fast failure. The choice is not whether to adopt AI-assisted development, but whether to do it safely.
For those looking to dive deep into coding practices with a safe atmosphere, the complete guide offers extensive guidelines.
Security-centric analysis of major AI coding systems
AI System Key Strengths Security Features Limitations Optimal Use Cases Security Considerations Openai Codex/GPT-4 Versatility, Strong Understanding Code Vulnerability Detection (Copilot) suggests that full stack web development of obsolete libraries, complex algorithm redundant code can obscure security issues. Strong explanations of weak system-level security claud, less specialized in coding security critical apps with a lot of natural language risk recognition documents, and excellent at explaining the meaning of security. Weaker Logical Security Defect Detection Github Copilot IDE Integration, Repository Context Real-time Security Scan, Quick Prototyping of OWASP detection contexts, Developer Workflows are suitable for detecting known unstable patterns. Edit, Refactoring Integrated Security Lint Lint is not suitable for iterative improvements in new large codebases. Security Audit Identifies vulnerabilities in existing codebases
The complete guide includes 15 application patterns, tool-specific security configurations, and secure prompt templates for enterprise implementation frameworks, as well as reading essential for teams deploying AI-assisted development.
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