Vibe Coding : Cybersecurity Risks and Best Practice Recommendations
How AI Is Changing App Development—and What You Need to Watch Out For
Vibe coding is an emerging trend where users build apps using natural language prompts instead of traditional code. Platforms like Lovable are enabling non-developers to generate working applications at scale.Recent studies by Veracode and researchers behind BaxBench show that while AI-generated code often works, it frequently contains serious security flaws. This blog explores how vibe coding works, what makes it different from traditional software development, and the growing security risks it introduces. It also outlines practical steps to make vibe coding safer—especially for teams adopting AI-first workflows.
Whether you're building with AI or planning to, this guide will help you understand the benefits, the risks, and the security fundamentals required to keep pace. Lets start with basics
What Is Vibe Coding?
Vibe coding is the emerging practice of building apps using natural language prompts instead of manual code.
Instead of writing code line-by-line, users describe what they want—like “build a project management app with Kanban view and email reminders”—and the AI generates it.
One of the breakout platforms in this space is Lovable.
In just 8 months, Lovable surpassed $100M ARR and now sees 70,000 apps created every day. These apps aren’t written by traditional developers—they’re being built by founders, product managers, marketers, and students using prompts.
The result?
Fast prototyping. Minimal setup. No engineering bottlenecks.
Lovable is part of a larger trend across the industry—what some call “build with AI.”
According to multiple vendors, 25–41% of production code is now AI-generated.
This includes code written by AI tools like Cursor, Windsurf, and Replit, and prototypes built using AI-first platforms like v0.dev, Base44, and Bolt.
What Makes Vibe Coding Different?
Traditional software engineering has always involved human-written logic.
Even with code generators, the developer was in control.
Vibe coding flips this.
It introduces a new development loop:
You write a natural language prompt
The AI generates working code or UI
You test it and adjust your prompt
You ship
Vibe coding can generate:
Entire UI components
Database models
API endpoints
Business logic
Infrastructure configurations
This makes it incredibly powerful for:
MVP development
Internal tools
User onboarding flows
Experimentation
But the simplicity comes with tradeoffs—especially when it comes to control and security.
Evolution of Software Engineering
To understand where vibe coding fits, it helps to look at the broader evolution.
1. Traditional Software Development (Past)
All code is written manually
Engineers understand every line
SDLC is predictable and structured
Full human control over app behavior
Security is built into architecture from the start
2. AI-Augmented Software Engineering (Present)
AI tools assist developers with code, tests, docs
Tools like Copilot and CodeWhisperer are mainstream
Vibe coding platforms generate large blocks of logic
Developers validate, tweak, and ship
Security depends on the vigilance of the human in the loop
3. AI-Native Software Engineering (Emerging Future)
AI takes the lead; humans supervise
Agents handle large parts of the SDLC
Multi-agent systems translate intent into working software
Spec Agent
Architecture Agent
Coding Agent
Testing Agent
Deployment Agent
In this model, you describe your goal, and the system assembles the app—choosing libraries, wiring APIs, even deploying code.
Vibe coding is the first visible step toward this world.
Key Characteristics of Vibe Coding
Input is a prompt, not a design doc or codebase
Code is accepted quickly, often without deep inspection
Iteration is natural language–based, not code-based
Feedback loop is prompt-driven, not ticket-driven
Users include non-developers, which changes the expectations for security and performance
This makes building easier—but it also makes security harder to track.
Security Concerns: What the Data Shows
Veracode GenAI Code Security Report (2025)
This report examined whether LLMs can write secure code without specific guidance.
Methodology:
Tested 100+ LLMs across 80 coding tasks
Four languages: Java, JavaScript, Python, C#
Four CWEs:
CWE-89: SQL Injection
CWE-80: Cross-site Scripting
CWE-117: Log Injection
CWE-327: Insecure Cryptography
Each coding task could be solved either securely or insecurely
Veracode’s SAST engine evaluated each response
Key Findings:
Many LLMs produced working but insecure code
No consistent trend in security performance across model size
Security vulnerabilities were common in default outputs
Without explicit secure coding prompts, models defaulted to unsafe practices
This raises an important point:
AI doesn’t know when it’s making insecure choices unless you tell it.
BaxBench: Can LLMs Build Secure Backends?
BaxBench is a research benchmark built by teams at ETH Zurich, LogicStar.ai, and UC Berkeley.
It contains 392 real-world backend tasks, such as:
Creating auth systems
Storing sensitive data
Validating user input
Generating server responses
Findings:
Only 35% of generated backend solutions were both correct and secure
Over 50% of otherwise functional solutions were exploitable
In short: AI-generated backends work—but many are insecure by default.
Securing Vibe Coding Workflows
Vibe coding needs security-by-default practices, especially since many users are non-developers.
Here’s how to reduce the risk:
1. Treat AI Code as a Draft
AI-generated code should never be shipped without human review.
It must be treated like a first draft—useful, but not final.
Make this a non-negotiable policy:
Every AI code block must be reviewed by a human
Reviews should focus on correctness and security
2. Apply Secure Coding Practices
Use the basics:
Avoid hardcoded credentials
Validate all input (client + server side)
Use HTTPS
Set proper access controls
Implement CORS with allowlists only
Sanitize output to prevent injection attacks
These basics still matter—even when code is generated by AI.
3. Secure the Prompts
Prompts often define what’s included—or missed—in the final code.
Make your prompts specific about security:
“Create an API endpoint that authenticates with an API key stored in an environment variable.”
“Validate user input to prevent SQL injection and log injection.”
“Configure CORS to allow only internal domains and disallow wildcard access.”
If your prompt doesn’t say it, the AI may skip it.
4. Use DevSecOps Tools
AI-generated code should still go through your security pipeline.
Tools that help:
SAST/DAST/IAST
CI/CD scanning
Open-source license scanning
Secrets detection (e.g., GitLeaks, TruffleHog)
Make AI coding tools part of the same SDLC pipeline as traditional dev.
5. Protect the Codebase and Data
Security doesn’t stop at code.
Ensure:
Version control access is restricted
Database queries are parameterized
Sensitive data is encrypted at rest and in transit
User input is logged and monitored
APIs are authenticated and rate-limited
These controls still matter—especially when code is being generated fast.
6. Address AI-Specific Risks
Use frameworks like:
OWASP LLM Top 10
Secure Prompting Guides
Model risk scoring tools
Look out for:
Prompt injection
Training data leakage
Hallucinated business logic
Model behavior
Final Takeaways
Vibe coding is here.
It's fast, exciting, and powerful—but it also introduces a new set of responsibilities for developers, PMs, and security teams.
New skills will be needed:
AI-native product managers
Secure prompt engineers
AppSec experts for generative pipelines
Security is not built into the vibe.
We have to add it.
Sources
Veracode GenAI Code Security Report (2025)
BaxBench Paper (ETH Zurich, LogicStar.ai, UC Berkeley)
Thanks for Reading
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