Being able to program without extensive knowledge of the code is possible with “vibe coding,” a growing trend that junior developers have embraced to compensate for their lack of experience and skills in certain tasks.
While it accelerates product development, vibe coding carries concrete risks that threaten your entire business infrastructure. We identified the most critical vulnerabilities to improve our auditing strategy. Today, we’re sharing everything with you.
How Does Vibe Coding Threaten Your Business?
At first, it was a novel way of programming, but continuous errors revealed the reality of vibe coding: a silent multiplier of technical debt that hits exactly where it hurts most—security, stability, and delivery speed in production.
In high-pressure environments, juniors supported by AI can produce many lines of code that appear to work, but that don’t follow internal standards or account for edge cases and real failure scenarios.
According to data from Second Talent, junior developers show mixed results with vibe coding, with 40% stating that they use it to develop code they do not fully understand.
The result? A more fragile system as it grows, with incidents that are increasingly costly to diagnose and fix. We’re talking about a context where you can’t be strategic—you can only be reactive.
Security Vulnerabilities
When teams prioritize “just making it work now,” the first casualties are the most basic security controls.
Misuse of AI makes it easier for implementations that fail to robustly validate user input to enter your codebase, opening the door to SQL injections, XSS, deserialization attacks, and privilege escalation.
At the same time, it’s common to find poor handling of sensitive information:
- Credentials embedded directly in the code.
- Access tokens copied from public examples.
- API keys without periodic rotation or centralized management in a vault.
All of this dramatically increases your attack surface and makes a security incident a matter of time, not probability.
A Veracode study revealed a worrying fact. In their research, 45% of AI-generated code had serious digital security flaws, confirming that this way of programming is dangerous without proper oversight.
Duplicated Code
Without a full contextual understanding of your project or existing libraries, AI often recreates functionality that has already been implemented, unnecessarily increasing the size of your codebase.
This slows compilation times, increases resource consumption, and complicates testing because instead of having a single source of truth covered by solid automated tests, the team designs and maintains tests for multiple versions of the same flow.
This problem is particularly severe in large, complex projects where the codebase is already extensive. The accumulation of identical or very similar functions with slight variations creates a code labyrinth that becomes extremely difficult to maintain.
Fragile Integrations
AI lacks the holistic vision of a software architect. It is unable to fully understand interdependencies, existing API contracts, error-handling expectations, or communication patterns between microservices.
As a result, the generated code works only under ideal conditions and fails dramatically under load spikes, changes in connected systems, or other unexpected incidents.
These integrations also lack idempotency in critical operations such as:
- Payments.
- Order creation.
- Status updates.
When automatic or manual retries occur, transactions are duplicated, records become inconsistent, or states become impossible to reconcile—directly impacting accounting, customer support, and accountability.
Maintenance and Scalability Issues
Vibe coding optimizes the time from idea to first MVP, but extends the time the team needs to change, extend, or fix the system. This is the metric that truly matters when your product enters its growth phase.
Without a clear architectural guide, features are added without a vision of domains, bounded contexts, or clearly defined responsibilities. The result is a chaotic, emergent architecture that is extremely sensitive to change.
This inconsistency forces developers to relearn how the system is built in every folder or service, increasing friction and the time required to implement safe changes.
| Aspect | Expert developer code | Junior code with vibe coding |
|---|---|---|
| Readability | Prioritizes clarity and meaningful naming, applying clean code principles so the code is easy to read. | Accepts most of the AI-generated code as-is, with generic names and structures they don’t always fully understand. |
| Maintainability | Designs with the future in mind: refactors, reduces dependencies, and organizes modules to support long-term changes. | Focuses on making the code work now. Accumulates technical debt by not reviewing architecture or simplifying the AI-suggested solution. |
| Reusability | Extracts common patterns into generic functions and components, creating well-encapsulated reusable pieces. | Often copies and pastes fragments from previous prompts or generates similar versions without consolidating them into reusable components. |
| Robustness | Identifies vulnerabilities, bottlenecks, and edge cases in generated code, adjusting it to be secure and stable. | Doesn’t always recognize subtle security or performance issues and relies too heavily on artificial intelligence. |
| Design | Uses experience to guide the AI: defines clear boundaries, layers, and responsibilities, and corrects incoherent designs. | Lets the emerging structure depend on model responses, leading to inconsistent architectures. |
| Testability | Plans testing points from the start, creates small modules, and ensures generated code is easy to cover with tests. | Relies on manual execution and exploratory testing, without ensuring a structure that supports unit testing and automated workflows. |
Our Framework for Auditing and Fixing AI-Caused Issues
Resolving code errors created by artificial intelligence requires applying a systematic framework that subjects the output to rigorous diagnosis. Only then can it be transformed into a stable, secure foundation ready to scale according to your needs.
At Crazy Imagine Software, we developed a comprehensive audit and correction plan designed to turn AI-generated code from a potential liability into a strategic asset aligned with current guarantees and standards.
We integrate performance analysis, architectural auditing, and metric-driven refactoring—not intuition or individual perception—offering you a clear path to continuous improvement free from inexperienced programming errors.
Performance Diagnosis
The first step is to answer a seemingly simple but critical question with data: where in the system are the most time and resources being consumed?
From there, a precise picture of production behavior is built. This starts with deep observability: systematic review of logs, distributed traces, and metrics from application monitoring tools.
This analysis reveals not only what breaks, but also what is about to break under certain load conditions.
Structural and Architectural Audit
Once we understand how the system behaves at runtime, the next step is to evaluate its shape: how the code is organized and how its parts relate to one another.
The structural audit begins with mapping modules and dependencies. The goal is to identify:
- Core services.
- Satellite services.
- External dependencies.
- Critical data flows.
- Points where coupling is especially high.
This view makes it possible to understand how information moves through the system, where complexity concentrates, and which parts bear the most functional and technical pressure.
Refactoring Plan Design
With performance and architectural information on the table, a plan is designed to reduce risk without paralyzing the product roadmap.
The key is prioritization by risk and value: ordering interventions by their impact on security, stability, user experience, and alignment with your business’s strategic objectives.
From that prioritization, refactorable units are defined: the system is broken down into manageable pieces such as specific services, bounded contexts, critical components, or domain modules—each with clear technical and business completion criteria.
Solution Implementation
We apply the plan directly to your codebase, guiding the process with testing as a core principle.
Before touching critical modules, we reinforce the automated test suite—unit, integration, and end-to-end—so every change is validated with confidence and regressions are detected early.
Duplications are eliminated, scattered business rules are centralized, and improvised patterns are replaced with clear, documented implementations aligned with the target architecture.
The goal is simple: assign a defined responsibility to each module and keep dependencies under control.
Knowledge Transfer
A rigorous audit is not complete until the internal team can sustain the new level of quality without permanently relying on external consultants. That’s why the knowledge transfer phase is critical.
This stage includes technical workshops tailored to the client’s stack, architecture, and domain, explaining what was fixed, why it was done that way, and which patterns the team is expected to replicate going forward.
Clear guidelines are also established for responsible AI usage in your development cycle. We define:
- Which types of tasks are suitable for generative tools.
- Which require more careful manual design.
- Which practices are mandatory before merging AI-generated code.
- Which reviews must be performed on every pull request.
