Vibe coding has created a perfect storm for CVEs. When developers describe what they want to AI models (rather than writing the code themselves), these AI models typically skip simple security measures like input sanitization and proper access controls. The numbers paint an odd (eh, more like scary) picture: over half of organizations fix only about 10% of the vulnerabilities they find.

This piece gets into how vibe-coded applications become security risks and what makes these vulnerabilities so dangerous. You'll also learn practical steps to protect your applications while making the most of AI assistance in development.

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How vibe coding introduces vulnerabilities

Research from multiple sources shows that AI code generators create insecure outputs at alarming rates. Between 34.8% and 68.9% of AI-generated code has functional errors. Studies reveal that AI-generated code is often vulnerable to items on MITRE's "Most Dangerous Software Weaknesses" list.

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Low security standards in AI-generated code

The biggest problem lies in the training approach of AI tools. These models learn from massive repositories of human-written code that (often) contain vulnerabilities. And since AI models can't tell the difference between secure and insecure patterns as they generate recommendations…well, you can see how that’s a recipe for disaster.

As a result, the code often has:

• Hardcoded credentials right in source files
• Outdated or unpatched libraries with known vulnerabilities
• Too many permissions that break the principle of least privilege
• Unsafe cryptographic algorithms

Things get dangerous quickly when teams deploy this low quality code without proper review, as they run on the assumption that AI-generated solutions are secure.

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From insecure code to CVEs: how it happens

Low quality code deployed to production can evolve into formal security vulnerabilities with assigned CVE (Common Vulnerabilities and Exposures) identifiers. The CVE program acts as an official dictionary of identified vulnerabilities and provides unique identifiers that security professionals use worldwide.

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Common CVEs found in vibe-coded apps

Several common patterns repeatedly appear in security databases as vibe coding speeds up vulnerability introduction. Studies show that AI models suggest vulnerable code 30% of the time in test cases of all types, and they show a higher susceptibility to suggesting insecure code.

The most prevalent issues include:

• Missing input sanitization: Most LLMs skip this vital security foundation that enables SQL injection and XSS attacks.
• Improper access controls: Client-side authentication implementation uses easily manipulated checks.
• Hard-coded credentials: AI models expose database credentials directly in application files.
• Insufficient error handling: Error messages reveal excessive system information^.

These weaknesses become especially dangerous with developers who implement vibe-coded solutions without deep understanding of the generated code. Security experts call this a "comprehension gap."

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Examples of AI-generated vulnerabilities

Production systems already show vulnerabilities from vibe coding. Recent research discovered eight significant vulnerabilities in the AI development supply chain. One vulnerability had critical severity (CVSS 9.8), while seven others had high severity ratings.

Notable examples include MLFlow's arbitrary file write vulnerabilities (CVE-2023-6975) and Hugging Face Transformers' remote code execution (CVE-2023-6730). Malicious actors have posted harmful code to Hugging Face while evading security checks through a technique called "NullifAI."

Detection becomes difficult as AI-generated code shows recognizable patterns. These include overly generic variable names, excessive comments explaining simple logic without security checks, and missing error handling.

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Why these issues go unnoticed until it's too late

"Data breaches can occur even with the best defenses if vulnerabilities in code go unchecked." — GVisor AI Security Overview, Google's gVisor project, security documentation

Code generation now moves much faster than security oversight can keep up, which creates an ideal breeding ground for vulnerabilities. Forbes' research reveals that developers have become so comfortable with AI that they don't take time to review the AI's output. This dangerous practice explains why many security flaws remain hidden until attackers exploit them.

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Developers skipping code reviews

Vibe coding has started to erode the traditional code review process. Developers now blindly accept AI-generated changes, with some openly admitting, "I don't read diffs anymore – I just accept all." This disconnect results in codebases that developers can neither explain, adapt, nor audit properly. Developers choose speed over security and create more credential exposure risks, yet paradoxically, 99% believe AI coding tools will enhance security.

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No testing or documentation in place

Pure vibe coding usually skips testing until bugs surface, allowing security vulnerabilities to hide beneath functional-looking interfaces. Ardor Cloud warns about this dangerous trend: "A functioning application can create a false sense of security. Without rigorous audits, exploitable flaws may remain hidden until attackers strike."

Documentation quality has declined as AI usage grows, which leads to poor understanding of the system's architecture. The workload has actually increased for most developers, who now spend more time fixing AI-generated code and addressing security issues.

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Security teams overwhelmed by volume

AI-accelerated code production has put security professionals in an impossible position. Organizations typically have just one AppSec engineer for every 100 developers.

Did you get that? That’s one person trying to evaluate security for 100 people’s daily work. It’s no surprise that security teams struggle with:

• Too many tools: Over 80% of organizations use between six and twenty different security testing tools. That’s a lot of tools to learn and juggle.
• Too much noise: Anywhere from 21-60% of security test results are false positives, duplicates, or conflicts.
• Too much code: A single AI-assisted pull request can generate thousands of lines of new code. And remember, there’s often only one person evaluating the security of it.

There’s one final…uh, happy…little point to make: outdated security processes cannot handle AI's scale. Traditional manual review methods simply fail to catch up with this volume, which allows CVEs to pile up unnoticed.

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What can be done to prevent CVEs in vibe coding

So, if your team is insistent on using vibe coding, what do you do? As attacks grow more sophisticated, we want security to increase, not decrease, right? So how do we handle this? How can we be proactive, rather than reactive (i.e., finding and fixing vulnerabilities before release instead of after)?

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Use of secure coding checklists

Clear guidelines for AI use will create safer vibe coding. OWASP's Secure Coding Practices checklist is a great way to get guidance on critical areas like input validation, authentication, and error handling. Developers should avoid blindly trusting AI outputs and:

• Explicitly request secure patterns in prompts (parameterized queries, input validation)
• Verify that AI-generated code follows the principle of least privilege
• Review all security-critical sections manually

Following these guidelines is a great start to preventing blind trust, keeping human judgment as part of the security process.

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Training AI models with secure codebases

Safer vibe coding starts with the models. Research shows AI security gets better through models that learn secure practices from the start. A good practice for companies who build their own ML models is to study past vulnerabilities, allowing the AI to predict and suggest preventive steps.

Google suggests documenting model and dataset biases, showing licenses clearly, and measuring hallucination risks. A complete security policy helps tackle user concerns about AI-generated code's safety.

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Integrating security tools into AI workflows

Immediate security scanning proves most effective at preventing CVEs in vibe coding. Studies show 80% of developers skip AI code security policies, which makes automated safeguards necessary.

AI-powered static application security testing (SAST) tools find vulnerabilities during development. AI-assisted remediation studies, finds and creates ready-to-use code fixes. However, these tools should provide suggestions for developers to review, rather than auto-commit changes.

Security integration in existing workflows helps teams clear vulnerability backlogs quickly. This early-stage approach secures AI-generated code right as it's created.

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Conclusion

AI-powered coding promises quick development, but security vulnerabilities are rising fast. Vibe coding accelerates the process, and applications are becoming more vulnerable as time passes.

Developers who skip code reviews and testing face multiplying security risks. AI models often suggest unsafe patterns in the code. Success comes from balancing AI assistance with human oversight instead of trusting AI blindly.

Your applications need proper security measures from day one. Security checklists, better model training, and automated tools cut down vulnerability risks by a lot. These protective measures combined with detailed code reviews help detect potential CVEs before production deployment.

AI works best as your coding assistant, not a replacement for security knowledge. You can utilize AI's advantages while keeping your applications secure through careful security practices and regular vulnerability checks. The key is to examine AI-generated code as thoroughly as human-written code. This approach will give a secure codebase without slowing down development.

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FAQs

Q1. What is vibe coding and why is it concerning? Vibe coding refers to the practice of developers describing their needs to AI models to generate code. It's concerning because it often leads to low-quality applications with hidden security flaws, as AI models frequently fail to implement basic security measures.

Q2. How does vibe coding introduce vulnerabilities? Vibe coding introduces vulnerabilities through lack of secure defaults in AI-generated code, overreliance on natural language prompts, and missing input validation and error handling. These issues stem from AI models being trained on potentially insecure code and developers trusting AI outputs without proper verification.

Q3. What are some common security issues found in AI-generated code? Common security issues in AI-generated code include missing input sanitization, improper access controls, hard-coded credentials, and insufficient error handling. These vulnerabilities can lead to various attacks, including SQL injection and cross-site scripting (XSS).

Q4. Why do security issues in vibe-coded applications often go unnoticed? Security issues often go unnoticed because developers skip code reviews, assuming AI-generated code is secure. Additionally, there's often a lack of testing and documentation, and security teams are overwhelmed by the volume of code being produced, making it difficult to catch all vulnerabilities.

Q5. What can be done to prevent security vulnerabilities in vibe coding? To prevent vulnerabilities, developers should use secure coding checklists, train AI models with secure codebases, and integrate security tools into AI workflows. It's also crucial to maintain human oversight, conduct thorough code reviews, and implement automated security scanning to catch potential issues early in the development process.