Secure AI-Assisted Development: Five Critical Practices for Vibe Coding
Developing with AI has become the natural route to move an idea from concept to working code quickly. The problem is that most flaws in applications created this way do not originate from an error in the model but from an assumption made by the developer. The AI delivers exactly what was requested, and security is almost never part of that request.
Five points concentrate the majority of problems. Developers are advised to describe what the application must not do. Prompts usually detail functionality while ignoring restrictions. The AI implements the happy path with precision but does not imagine a malicious user on its own. When requesting a feature, teams should also specify who cannot access it, which values are invalid, and what must happen when someone attempts to bypass the flow. An undeclared restriction is a non-existent restriction.
Authentication and authorization are not the same. The AI implements login without difficulty, which is precisely where the trap lies. Authentication confirms who the user is; authorization defines what that user may access. Without explicit instruction, applications commonly verify only that someone is logged in and fail to check whether the record belongs to that user. Changing a number in the URL and viewing another client’s data remains the most frequently observed flaw in newly built applications.
Teams must review every dependency the AI selects. Each suggested library enters the project carrying its own history of vulnerabilities. Models tend to recommend packages that appear frequently in training data, which does not guarantee they are actively maintained or updated. Checking the last update date of each dependency and running an automated scan before release is recommended, because an inherited flaw is as exploitable as one written by the developer.
A secret removed from code does not disappear from the repository. An API key remains in commit history and stays accessible to anyone with repository access, as well as to automated scans that target public repositories. When a credential leaks, the only safe action is to revoke it and generate a new one rather than editing the file.
Business logic is the blind spot. No model knows the rules of a specific business. The AI does not understand that a coupon cannot be applied twice, that a balance should not accept a negative value, or that a cancelled order cannot generate repeated refunds. These flaws pass every automated scan because the code is technically correct. Only someone who understands the business flow can identify them.
Applications developed with AI have already entered the sights of cybercriminals, mainly because they repeat flaws that can be identified and exploited at scale. In addition to good practices during development, submitting the application to a pentest before production is advised. In this scenario, the HackerSec Pentest Platform has become an alternative used by developers and vibe coders seeking to test the cybersecurity of their applications with quality, agility, and a more accessible model. Rapid development is part of this new way of creating software.
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