Vibe coding allows teams to build applications at high speed, but security can lag behind. This checklist helps teams protect programs created by artificial intelligence.
You can read about common issues with AI-powered applications in this article.
The checklist below focuses on practical checks that can be applied regardless of programming language or framework.
Authentication and Access Control
Authentication failures remain one of the most common and challenging issues in AI-powered applications.
Key steps to take:
- Enforce authentication before executing any sensitive application logic.
- Verify authentication behavior at runtime, not just in generated code.
- Ensure that unauthenticated requests cannot directly reach server endpoints.
- Check for open or forgotten endpoints that bypass login flows.
Authorization and Data Access
Authorization logic is particularly vulnerable to exploitation.
To prevent unauthorized access and data disclosure, it is necessary to:
- Verify role-based access control for each endpoint.
- Test the application for broken object-level authorization (BOLA).
- Ensure that users cannot access prohibited data.
- Validate authorization in APIs and internal services.
Endpoint and API Exposure
The following steps should be taken:
- Inventory all active endpoints and APIs.
- Identify undocumented, deprecated, or prompt-generated endpoints.
- Test APIs independently of UI logic.
- Ensure that removed user-interface functions do not leave active endpoints.
Injection and Code Execution Risks
Processing user input is a risk point in any program.
What can be done to minimize risks:
- Test applications for SQL injection and incorrect use of ORM (object-relational mapping).
- Check protection against OS command injection.
- Identify paths that could lead to remote code execution.
- Check the quality of input data validation.
Secrets and Sensitive Data
The reuse and leakage of secrets is a recurring and systemic problem in AI-generated code. To avoid this, teams can:
- Check code for common secrets identified by the Invicti study.
- Scan for exposed API keys, tokens, and credentials.
- Ensure that secrets are never returned in application responses.
- Make sure that server-side keys cannot reach the frontend.
- Check third-party integrations for unintentional data leaks.
Third-party Dependencies
AI tools often use libraries without explaining why they were chosen. To minimize the risk of these external dependencies, it is necessary to:
- Identify all libraries and frameworks added by AI.
- Check dependencies for known vulnerabilities (using SCA tools, such as Mend.io).
Transport and Configuration Security
To minimize operational risks, it is necessary to:
- Enforce HTTPS for all program components.
- Check security headers.
- Ensure that no debug or development settings are exposed.
- Make sure that environment-specific configurations are applied correctly.
Check code and runtime behavior
Unexpected incoming data and errors often reveal the most serious problems, especially for an AI-generated application. Therefore, in addition to scanning source code during development, teams should also remember about runtime testing.
Recommended practice: regularly using SAST, such as that from Mend.io, and DAST, like Invicti (based on Acunetix and Netsparker).
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