Vibe Coding Debt: The Security Risks of AI-Generated Codebases 🌊💻

In early 2025, former Tesla AI lead Andrej Karpathy popularized a term that perfectly captured the zeitgeist of the modern developer experience: “Vibe Coding.” Vibe coding is the practice of building entire applications using natural language prompts via Large Language Models (LLMs) and AI agents like Cursor, Windsurf, or Claude Engineer. In this paradigm, the developer often “forgets that the code even exists,” shifting their focus from syntax and logic to high-level intent and “vibes.”

While vibe coding has democratized software creation—allowing non-technical founders to ship MVPs in hours—it has introduced a silent, compounding crisis: Vibe Coding Debt. This isn’t just traditional technical debt; it is a massive wave of security debt that threatens the very foundation of the software supply chain.

What is Vibe Coding Debt?

Technical debt is a well-understood concept where developers trade long-term maintainability for short-term speed. Security debt, a subset of technical debt, refers to unresolved security flaws that persist in a codebase over time.

Vibe Coding Debt is the acceleration of this problem through AI. When an LLM generates a 500-line React component or a Python backend script, it prioritizes “working code” (code that runs without immediate errors) over “secure code.” Because vibe coders often lack the expertise—or the patience—to review these thousands of lines of machine-generated code, vulnerabilities are baked into the application’s DNA from day one.

According to the Veracode 2025 GenAI Code Security Report, nearly 45% of AI-generated code contains security flaws. More alarmingly, research indicates that when LLMs are given a choice between a secure and an insecure method to solve a problem, they choose the insecure path nearly half the time.

1. The CORS Trap: Over-Permission by Default

One of the most common “hallucinations” in AI-generated security logic isn’t a hallucination of a fact, but a hallucination of safety. LLMs often default to the most “convenient” settings to ensure the user’s app works immediately upon copy-pasting.

The Problem: Wildcard Origins

When a developer asks an AI to “fix my API connection issues,” the AI frequently suggests adding Cross-Origin Resource Sharing (CORS) headers. To ensure the code works regardless of the developer’s local environment, the AI often generates:

// AI-generated convenience code
app.use(cors({
  origin: '*', // The security "vibe" is off here
  credentials: true
}));

The Risk

The origin: '*' wildcard allows any website to make requests to your API. While this makes development easy, it is a critical security flaw in production. If a user is logged into your app and visits a malicious site, that site can use the user’s browser to make authenticated requests to your backend, leading to Cross-Site Request Forgery (CSRF) and data exfiltration.

The AI prioritizes the “vibe” of a working app over the “reality” of a secure one.

2. The Cryptographic Time Machine: Use of Deprecated Libraries

LLMs are trained on massive repositories of public code, much of which is outdated. This leads to a phenomenon where AI suggests cryptographic implementations that were considered “best practice” in 2015 but are dangerously obsolete today.

The Problem: Weak Hashing and Old Protocols

It is common to see AI suggest the MD5 or SHA-1 hashing algorithms for password storage or data integrity checks. In the Veracode study, 14% of AI-generated cryptographic implementations used weak or broken algorithms.

Example of AI-generated “Vibe” Crypto:

import hashlib
# AI suggests MD5 because it's common in its training data
def hash_password(password):
    return hashlib.md5(password.encode()).hexdigest()

The Risk

Algorithms like MD5 are susceptible to collision attacks and can be cracked in seconds using modern hardware. A “vibe coder” who doesn’t know the difference between MD5 and Argon2 will accept this code because it “works,” unknowingly leaving their users’ passwords vulnerable to data breaches.

3. The Placeholder Pitfall: Hardcoded Credentials

AI agents often have access to your local file system, including your .env files or configuration scripts. A major security risk in vibe coding is the “accidental leak” where the AI includes real API keys or “test” credentials directly in the generated snippets.

The Problem: “Test” Accounts and Exposed Keys

When generating a login system or a database connection, AI models frequently insert hardcoded strings as placeholders. Sometimes, these are real keys the AI “remembered” from your other files; other times, they are “test” credentials like:

const dbConfig = {
  host: "localhost",
  user: "admin",
  password: "password123", // AI "vibe" for "it's just a test"
};

The Risk

If the developer forgets to replace these placeholders—or if they assume the AI followed best practices by using process.env—these credentials get committed to version control (like GitHub). Once in the cloud, bots scan for these patterns in seconds. This has led to “Mass Credential Exposure,” where entire AWS clusters have been compromised due to AI-generated “test” configs left in production.

4. “Phantom” Supply Chain Risks: Hallucinated Packages

A unique danger of LLM-driven development is AI Package Hallucination. This happens when an AI suggests a library that doesn’t actually exist, often giving it a name that sounds highly plausible (e.g., fastapi-security-helper or react-native-auth-guard).

The Problem: Prompt-Induced Dependency Injection

If a developer prompts: “Give me a library to handle secure JWT tokens in Python,” the AI might suggest a non-existent package.

The Risk: Dependency Hijacking

Security researchers have found that attackers can monitor LLM hallucinations and register these “hallucinated” package names on registries like NPM or PyPI. When an unsuspecting vibe coder runs npm install <hallucinated-package>, they are actually installing a malicious payload—a “Trojan Horse” provided by an attacker who anticipated the AI’s mistake.

5. Logical Context Blindness

AI models are excellent at writing functions but terrible at understanding threat models. An AI doesn’t know if the app you are building is a simple “to-do” list for yourself or a medical records system for a hospital.

The Problem: Missing Authentication Gates

An AI might generate a beautiful dashboard for an admin panel but forget to wrap the API routes in an authentication middleware. To the AI, the task was “create a dashboard,” and it succeeded. To a security professional, the task was “create a secure dashboard,” and the AI failed.

Veracode Statistics on Logic Flaws:

• XSS (Cross-Site Scripting): AI fails to secure code against XSS 86% of the time.
• Log Injection: AI fails to sanitize logs 88% of the time.

How to Manage Vibe Coding Debt

We cannot—and should not—stop using AI to code. The productivity gains are too significant. However, we must evolve our “vibe” into a “Verified Vibe.”

1. The SHIELD Framework

As suggested by security researchers at Unit 42, organizations should adopt the SHIELD framework for AI-generated code:

• S - Separation of Duties: Don’t give AI agents access to production environments.
• H - Human in the Loop: Never merge AI code without a line-by-line human review.
• I - Input/Output Validation: Explicitly prompt AI to “use parameterized queries” and “validate all user inputs.”
• E - Environment Scoping: Keep sensitive .env files in a .gitignore and .cursorignore to prevent AI from reading them.
• L - Least Agency: Give your AI agents only the permissions they need.
• D - Defense in Depth: Use automated scanners (Snyk, SonarQube, Veracode) to check every AI-generated PR.

2. Secure Prompting Hygiene

Don’t just ask for a feature. Use Security-First Prompting:

• Bad: “Write a Python script to upload files to S3.”
• Good: “Write a secure Python script to upload files to S3. Include file type validation, size limits, and use environment variables for credentials. Do not use deprecated libraries.”

Conclusion: The “6-Month Wall”

Vibe coding feels like a superpower for the first three months of a project. But without rigorous security oversight, developers eventually hit the “6-Month Wall.” This is the point where the accumulated security debt and logical inconsistencies become so great that the app becomes unmaintainable and unfixable.

The future of development isn’t just about the “vibe”—it’s about Engineering Excellence. AI is a powerful co-pilot, but the human must remain the pilot, the navigator, and the safety inspector. If you vibe code today without a security check, you aren’t just building an app; you’re building a “welcome mat” for attackers.