Look, anyone who has ever done coding knows it can be difficult and frustrating. Sometimes it feels like running a cheese-grater over your forehead.

Sometimes a cheese-grater would be preferrable.

This sentiment explains the rapidly-growing field of “vibe coding” - snippets of code that are generated by AI, using only a series or prompts. In fact, it’s growing so much that ninety-seven percent of enterprise developers now use Generative AI coding tools. However, many have no idea that doing so may compromise their systems. Popular tools like GitHub Copilot show a 4% error rate. That sounds good, right? Well, as it turns out, “success” is a very loose term here, because “success” measures whether or not the code works; what it fails to examine is code security. And that metric is grim: more than half of AI-generated code snippets either have or could have exploitable bugs.

So why is this so popular, you may ask yourself? Vibe coding makes development processes faster and more efficient. Rather than come up with code, type it in, test it, tweak it, test it again, and so on, you can enter it into the UI, and then copy and paste the response. You do need to test it, obviously, but this process removes the independent work of creating the code yourself. However, the speed and efficiency benefits come at a cost.

Much of the code generated is often insecure and highly-exploitable. And we don’t need to tell you that insecure and exploitable software can cost businesses millions - even trillions - of dollars.

One popular code generator is Lovable.dev. Today, we’re going to get into the critical security flaws found specifically in Lovable.dev's implementation. And while it’s always best to learn the hard work of coding yourself, we’ll also give you pointers on how to spot potential threats, add key security measures, and keep your AI-assisted development process secure, no matter the platform.

The Anatomy of Vibe Coding Security Flaws

AI coding assistants have polished interfaces, but they hide a troubling truth: their generated code introduces security flaws that compromise systems. Research shows 40% of LLM-generated code suggestions contain exploitable vulnerabilities. So how does that work? Let’s first see how the code generator works under the hood.

How AI generates vulnerable code patterns

AI code generators use probabilistic learning models that train on huge code repositories. Not all this code follows secure practices, which creates a weakness in their security approach. In other words, we trained these models to write code that works rather than code that's secure.

The learning process itself causes problems. AI tools like CoPilot now generate 46% of code on GitHub. New models learn from existing AI-generated vulnerabilities and create a dangerous cycle. Developers accept buggy code from LLMs 10% more often than they would write themselves, making things worse.

Human developers question security implications, but AI lacks understanding of:

• Authentication requirements and workflows
• Application-specific security models
• Compliance obligations and regulations
• Potential exploitation vectors

Common security gaps in Lovable.dev's output

Lovable.dev claims to use "industry-standard security measures" with encrypted data and regular audits. Yet their generated code contains major security gaps. For example, it was just recently discovered that Lovable releases API keys that give users access to the Supabase database and resources.

The code from Lovable.dev shows typical AI-generation flaws in password handling and input validation. Neither does their platform deal very well with secure deserialization and configuration management. This allows attackers to run code remotely on systems.

Why traditional security tools miss these vulnerabilities

Standard security tools look for known CVEs (Common Vulnerabilities and Exposures). AI-specific vulnerabilities go undetected because:

Standard AppSec controls were built for conventional software systems. They focus on known vulnerabilities and predictable attack patterns. AI applications work differently - they learn from data and work like 'black boxes' with complex decisions. This creates blind spots in standard security tools.

These "shadow vulnerabilities" rarely get CVE identifiers because traditional scanners can't see them and, as we noted earlier, code generation models focus on functional correctness instead of security. This trains AI to write working code rather than secure code.

Your increasing use of vibe coding means you must understand these basic security flaws to keep your cybersecurity defenses reliable.

Exposed Authentication and Data Leaks

Security researchers have found a worrying pattern: every 1 of 3 codes that AI creates has exploitable vulnerabilities, with authentication and data protection flaws leading the list. These weak spots give attackers easy access to break into systems through vibe coding.

Weak password handling in AI-generated code

AI code generators keep making basic mistakes with password security. These systems create authentication code that misses vital protections because they lack security context. Research shows AI models often write code that fails at basic password handling:

• Input validation for password strength
• Proper encryption of stored credentials
• Protection against brute force attacks

This happens because AI can't think like an attacker, and developers who don't fully understand their code will add (or keep) security holes.

Unintentional API key exposure

The scariest part of vibe coding is how it exposes sensitive credentials carelessly. Researchers found a 3x surge in repositories with exposed PII and payment data. We traced this back to AI-generated code that puts API keys right into applications.

This shows up in several ways:

• Hardcoded credentials within source code
• API keys stored in plain text files
• Sensitive keys compiled into JavaScript files (35% of all exposed secrets)

Lovable.dev's users often push these credentials to public repositories without knowing it. This creates huge security risks. One case showed how "API keys were scraped from client-side code that AI had carelessly left exposed." Talk about a bad day….

How hackers exploit these vulnerabilities

Attackers who spot these weaknesses can launch complex attacks. APIs missing authorization and input validation have jumped nearly 1000% (and, no, this isn’t a typo). This makes systems easy targets.

Authentication flaws let attackers pretend to be real users and access sensitive data. Security experts say this opens the door to:

1. Stealing personal identifiable information from databases
2. Taking over accounts through stolen credentials
3. Creating backdoors in compromised systems

Casual vibe coding can lead to security disasters in your development. And while AI tools can write secure code with the right prompts, the biggest problem is how they hide complexity and make everything look simpler than it really is. And that “simple” look can cover over some serious consequences for people who don’t truly understand how to code.

Hidden Backdoors in Generated Dependencies

Dependency chain attacks are among the most overlooked security threats in AI-assisted coding, even though they rank high in the OWASP Top 10 CI/CD Security Risks. In a dependency chain attack, bad actors exploit flaws in how AI tools fetch code dependencies.

Briefly, most coding now imports libraries and modules of existing code, creating a web of interconnected functions and methods. It doesn’t take a great deal of thought to realize that one compromised component can affect the entire network of dependences, including hidden backdoors.

The supply chain risk of AI-recommended libraries

AI coding assistants like Lovable.dev suggest third-party packages that pull dependencies from self-managed repositories and language-specific SaaS repositories. This setup creates several weak points that attackers can exploit:

• Confusion tactics where attackers release malicious packages with similar names to internal packages.
• Dependency hijacking happens when attackers take control of legitimate package maintainer accounts.
• Typosquatting involves releasing packages with names similar to popular libraries.

Companies without proper checks can find themselves using code with hidden backdoors or vulnerabilities. These problems often stay hidden until damage is done.

Case study: Broken code through Lovable.dev

While tracking actual compromises to code generated through Lovable.dev, Richard Kirk, an SaaS developer and metadata analyst, used Lovable to create a series of simple code as a test. What he found was fascinating: out of 18 prompts, 9 of them were broken, with no attempt by the program to fix them. Without going into the entire study, his basic conclusion was that building any sort of functional program would be very difficult - if not impossible - and he’d most likely spend more time fixing the issues than he would had he built the program himself.

Techniques to verify third-party code integrity

Here's how you can protect your systems:

1. Secure repository management stops unauthorized packages from joining your dependency chain.
2. Package integrity verification uses checksums and cryptographic signatures.
3. Version pinning works better than pulling the latest package versions automatically.
4. Separate execution contexts keep installation scripts away from secrets.

A full review before adding third-party components is your best defense. Look through source code, model documentation, and data lineage to check security standards. You might also want to try AI-specific vulnerability detection tools that can spot these unique security threats.

The simple fact remains that lovable.dev's built-in security features need extra validation. Their terms even state that "to the fullest extent permitted by law, lovable…explicitly disclaim[s] all warranties…of merchantability, fitness for a particular purpose, title, and non-infringement. We make no guarantee regarding the accuracy, reliability, or usefulness of the platform, services, or any lovable content, and your use of these is entirely at your own risk.” And while this is obviously typical legalese, it’s a warning that should give everyone pause.

Tools that detect AI-specific vulnerabilities

These specialized tools help spot vulnerabilities unique to AI-generated code:

NB Defense works as both a JupyterLab extension and CLI tool. It spots everything from secrets and PII data to CVEs in third-party licenses. Garak comes with pre-defined vulnerability scanners for LLMs that check for hallucinations and prompt injection vulnerabilities.

The best results come from mixing tools from the OWASP Web Application Vulnerability Scanners list with AI-specific security tools. These Dynamic Application Security Testing (DAST) solutions scan automatically without needing source code access.

Creating secure prompts for better code generation

The way you structure prompts affects security outcomes directly. Research shows well-crafted prompts reduce common weaknesses in tested LLMs. Here's what you should do:

Use Recursive Criticism and Improvement (RCI) prompting. This method has cut down security weaknesses, like hardcoded credentials (CWE-259). You should also try Role-Based prompting by asking the AI to "act as a software security expert" when creating security-critical components.

Your prompts should clearly state security requirements. Tell the AI to follow secure coding practices that focus on input validation, authentication security, and protection against known vulnerabilities.

Conclusion

Vibe coding tools can boost efficiency significantly. But security concerns need our attention. Studies show that 40% of AI-generated code has exploitable vulnerabilities, which makes security validation crucial.

Security risks exist throughout the AI coding process. These range from weak authentication setups and exposed API keys to compromised dependencies. Users of Lovable.dev face specific challenges with authentication flows and dependency management that need extra security oversight.

Your systems' protection requires multiple security layers. The first step is to improve code review processes for AI outputs. Tools like NB Defense help detect AI-specific vulnerabilities. You should also write security-focused prompts that guide the AI to generate safer code.

AI coding assistants create functional code quickly but often miss security aspects. As a developer, you must check every AI suggestion carefully and keep strong security practices. With proper security measures and constant watchfulness, you can make use of vibe coding and keep your systems secure.

FAQs

Q1. What are the main security risks associated with AI-generated code? AI-generated code often contains vulnerabilities such as weak password handling, exposed API keys, and inadequate input validation. Research shows that nearly 40% of AI-generated code snippets contain exploitable bugs, which can lead to data breaches and system compromises.

Q2. How can developers ensure the security of AI-generated code? Developers should implement enhanced code review processes, conduct thorough testing (including static and dynamic analysis), verify third-party dependencies, and use specialized tools designed to detect AI-specific vulnerabilities. It's also crucial to craft security-focused prompts when using AI coding assistants.

Q3. What are the risks of using AI-recommended libraries in development? AI-recommended libraries can introduce supply chain risks, including confusion tactics, dependency hijacking, and typosquatting. These vulnerabilities can lead to the integration of malicious code or backdoors into your system, potentially compromising your entire application.

Q4. How does vibe coding impact application security? Vibe coding, or relying heavily on AI-generated code, can lead to increased security vulnerabilities if not properly managed. It often results in developers implementing code they don't fully understand, which can introduce hidden flaws and make systems more susceptible to attacks.

Q5. Are there specific tools to help detect vulnerabilities in AI-generated code? Yes, there are specialized tools designed to identify AI-specific vulnerabilities. Examples include NB Defense, which can detect secrets and PII data in code, and Garak, which probes for hallucinations and prompt injection vulnerabilities in LLM-generated content. Additionally, traditional DAST (Dynamic Application Security Testing) tools can be useful when adapted for AI-generated code.