Vibe coding security checklist

Key takeaways

• Vibe-coded apps introduce distinct and repeatable security risks.
• Hallucinated logic and reused secrets are often more dangerous than bad syntax.
• Manual code review does not scale with AI-driven development.
• A runtime validation approach is essential when code can change in minutes.
• Continuous dynamic testing on the Invicti Platform enables security to keep up with AI-fueled development innovation.

Vibe coding makes it possible to build and ship applications at a pace that was unheard of just a few years ago. By relying on conversational prompts and AI-generated code, both individuals and entire teams can move from idea to deployment in hours or days instead of weeks or months.

The velocity and opacity of AI-created code completely change the security equation. When applications are assembled and deployed faster than humans could reasonably review the generated code and test performance, traditional AppSec controls stop scaling. This vibe coding security checklist is designed to help teams secure AI-generated applications by focusing on validating real behavior at runtime instead of wrestling with the code or blindly trusting what the model produced.

Read Invicti research on vibe-coded app security and common secrets.

Why vibe coding requires a new security approach

Vibe coding dramatically accelerates application development, but it also shifts where risk is introduced and how it manifests. Today’s AI coding tools don’t just generate boilerplate but also make architectural and business-logic decisions on the fly, sometimes based on incomplete or ambiguous context.

The most significant security risks are not limited to obvious syntax errors or outdated libraries. Instead, they come from scale and subtle logic changes:

• AI-generated code can be deployed faster than it can be meaningfully reviewed.
• Hallucinated logic can weaken or bypass authentication and authorization without raising immediate red flags.
• Changes made through conversational prompts may leave behind exposed endpoints or unused logic paths.

The upshot is that code slips out of both developer and security control, so securing vibe-coded apps needs to center around validating how the application actually behaves in production-like conditions. Trusting that AI-generated code is implicitly secure is not a good idea.

Common security risks in vibe-coded applications

Analysis of real-world vibe-coded applications shows recurring AI security challenges that teams should expect rather than treat as edge cases:

• Authentication logic silently altered or partially removed during iterative prompting
• Authorization checks missing, weakened, or inconsistently applied across endpoints
• Exposed APIs or backend endpoints left active after UI changes
• Injection vulnerabilities introduced by generated input-handling logic
• Commonly known secrets and credentials propagated to client-side code or responses

Some of these issues may look minor in isolation, but in combination and at scale in an opaque code base, they create exploitable attack paths that are difficult to spot through code review alone.

Vibe coding security cheat sheet

The checklist below focuses on practical, runtime-focused checks that can be applied regardless of the language, framework, or AI coding tool used.

Authentication and access control

Authentication failures remain one of the most common and impactful issues in AI-generated applications. Main things to check for:

• Enforce authentication before any sensitive application logic executes.
• Ensure unauthenticated requests cannot reach backend endpoints directly.
• Validate authentication behavior at runtime, not just in generated code.
• Test for exposed or forgotten endpoints that bypass login flows.

Authorization and data access

Authorization logic is especially vulnerable to hallucinations and partial implementations. To prevent unauthorized access and data exposure:

• Verify role-based access control for every endpoint.
• Test for broken object-level authorization (BOLA).
• Confirm users cannot access peer or administrative data.
• Validate authorization consistently across APIs and internal services.

Endpoint and API exposure

Vibe coding makes it easy to (intentionally or not) create, modify, and abandon features, including API endpoints. To mitigate this:

• Inventory all active endpoints and APIs.
• Identify undocumented, legacy, or prompt-generated endpoints.
• Test APIs independently of UI logic.
• Ensure that removed UI features do not leave active endpoints behind.

Injection and code execution risks

User input handling is a risk point in any app, but with vibe coding, it should be treated as suspicious by default. To minimize injection risk:

• Test for SQL injection and ORM misuse.
• Validate protection against OS command injection.
• Identify paths that could lead to remote code execution.
• Assume all AI-generated input validation is incomplete until proven otherwise.

Secrets and sensitive data

Invicti research into vibe-coded applications shows that secrets reuse and leakage is a recurring and systemic issue, not an anomaly. To avoid data exposure and authentication failures:

• Check your code for common shared secrets, as identified by Invicti research.
• Scan for exposed API keys, tokens, and credentials.
• Ensure secrets are never returned in application responses.
• Validate that server-side keys cannot reach the frontend.
• Test third-party integrations for unintended data leakage.

Third-party dependencies

AI coding tools frequently introduce libraries without explaining why they were chosen. To minimize risk from those external dependencies:

• Identify all libraries and frameworks added by AI prompts.
• Monitor dependencies for known vulnerabilities.
• Validate the runtime behavior of third-party code.
• Avoid assuming that popular libraries are secure by default.

Transport and configuration security

Misconfigurations are easy to introduce when environments are spun up quickly and code is created without operational context. To minimize operational risks:

• Enforce HTTPS across all application components.
• Validate security headers.
• Ensure no debug or development settings are exposed.
• Confirm that environment-specific configurations are correctly applied.

Runtime behavior validation

Unexpected inputs and error conditions often reveal the most serious issues, especially for AI-generated code that’s more likely to be inconsistent across data flows. To cut down on runtime security gaps:

• Check at least every deployable build using a DAST scanner.
• Test application behavior under malformed or unexpected input.
• Validate that error handling does not expose sensitive data.
• Confirm logs do not leak secrets, tokens, or internal details.

Continuous testing and change management

With vibe coding, every subsequent prompt can materially change the application compared to the previous build. To maintain security coverage:

• Re-test applications after every AI-generated change.
• Integrate security testing into CI/CD pipelines.
• Treat every deployment as a new risk event.
• Don’t rely on point-in-time security reviews that assume stability.

Why code review alone doesn’t work for vibe coding

Manual code review breaks down quickly in environments where AI can generate tens of thousands of lines of application code in a single day. Even well-resourced teams cannot realistically review logic at the same pace that it is produced, especially when changes arrive incrementally through conversational prompts rather than traditional commits.

More importantly, the most dangerous issues in vibe-coded applications are often not obvious from reading the code itself. A single hallucinated condition, misplaced trust boundary, or missing authorization check can quietly bypass critical security controls without looking suspicious in isolation. In practice, problems such as exposed endpoints, broken access control, or leaked secrets are frequently easier to identify by observing how the application behaves at runtime than by inspecting generated source files.

This creates a fundamental mismatch between code-centric security practices and AI-driven development. Application security can no longer depend on understanding every generated line of code but needs to operate independently of the code base by validating what is actually reachable, accessible, and exploitable from an attacker’s point of view.

Why static analysis fails for vibe-coded applications

With full manual code reviews being unrealistic, developers turn to static application security testing (SAST) tools to check vibe-coded apps. However, Invicti research has shown that very few SAST findings are actually valid when it comes to vibe coding. There could be several reasons for this, starting with the fact that SAST tools are designed and refined on typical human-produced code.

Static analysis assumes that code structure and intent are stable enough to reason about in isolation. Vibe-coded applications break that assumption. When logic is generated, modified, and regenerated through conversational prompts, the resulting codebase is often inconsistent, highly repetitive, and full of indirect dependencies that static tools struggle to interpret accurately.

By its nature, static analysis cannot reliably account for how code behaves once it is running, and the unpredictability of AI-generated code makes this even worse. Authorization checks that exist in one execution path may be bypassed in another, unused endpoints may remain reachable, and secrets may only surface in specific responses or error conditions. These issues are mainly shaped by runtime context, including the request flows, data state, configuration, and environment, none of which static analysis can fully model.

As confirmed by research, static tools run on vibe-coded projects tend to produce especially large volumes of findings without any clear prioritization. This creates alert fatigue while still missing the critical question of what an attacker can actually reach and exploit. For AI-generated applications where code is often treated as disposable and rarely reviewed in full, understanding real behavior matters far more than understanding code-level issues, which is why runtime validation becomes even more important.

How Invicti supports vibe coding security

As entire teams adopt vibe coding and AI-driven development, security testing must scale without relying on assumptions about how applications are built. Invicti supports this shift by focusing on runtime behavior and real exploitability to allow security to keep pace with rapidly changing, AI-generated applications.

Detect vulnerabilities introduced by AI-generated code

Vibe coding can silently introduce vulnerabilities that are exploitable at runtime, even if the generated code itself appears clean or logically sound. Invicti uses proof-based DAST to detect these issues by testing running applications from the outside to identify injection vulnerabilities, authentication and authorization failures, exposed endpoints and APIs, and execution paths that could lead to remote code execution. Because testing is dynamic, the findings are independent of the programming language, framework, or AI tool used to generate the code.

Validate real exploitability

Noisy or speculative security findings quickly become unmanageable in AI-driven dev environments. Invicti addresses this by validating which vulnerabilities are actually exploitable through proof-based scanning. Instead of flagging potential issues in code, it confirms real attack paths that an attacker could use in practice. This significantly reduces false positives, allows security teams to prioritize with confidence, and removes the need for developers to spend time reproducing or questioning results before remediation can begin.

Scales with AI-driven development

AI-assisted development increases release frequency and shortens feedback cycles, which makes point-in-time security reviews impractical and ineffective. Invicti is built for continuous testing, with support for frequent scans and CI/CD integration that aligns security checks with deployment workflows. By testing applications as they behave in production-like conditions, Invicti ensures that security remains effective even as applications evolve rapidly through iterative prompting and automated code generation.

Conclusion: Start with checklists, move to automated security for vibe-coded applications

Vibe coding changes how applications are built, but it should not force teams to accept unknown risk. By shifting security from code review to runtime validation, organizations can keep pace with AI-driven development without compromising on security and losing visibility or control of their posture.

To see how your teams can continuously validate the actual security of AI-generated applications without slowing down releases, request a demo of the Invicti Application Security Platform.

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