Vibe coding is a recent term for a style of software development where the programmer relies heavily on artificial intelligence to generate, revise, explain, debug, and extend code from natural-language instructions. The phrase was popularized by Andrej Karpathy in early 2025, when he described a mode of coding where the human “gives in to the vibes,” talks to the AI, and lets the model produce much of the actual code. The idea is not simply autocomplete. It is closer to conversational software development: the human describes the desired behavior, the AI writes a first version, and the human guides the result through prompts, corrections, testing, and review.
At its best, vibe coding makes software creation more accessible. A person who understands the problem but does not know every syntax detail can build a prototype quickly. A business owner can sketch an internal tool. A security analyst can create a log parser. A student can build a simple web app while learning how the pieces fit together. For experienced developers, AI-assisted coding can reduce boilerplate, speed up experimentation, explain unfamiliar libraries, and help generate tests or documentation. But vibe coding also creates a dangerous illusion: because the code appears quickly and confidently, users may assume it is correct, secure, maintainable, and production-ready when it is not.
What Vibe Coding Looks Like in Practice
A traditional developer might begin by choosing a language, creating a project structure, reading documentation, writing functions, testing behavior, fixing errors, and gradually building toward a working application. In vibe coding, the user often begins with a plain-language request:
“Build me a simple web page where users can enter tasks, mark them complete, and save them in the browser.”
The AI might generate HTML, CSS, and JavaScript for a local to-do list application using browser storage. The user then continues:
“Make it look cleaner. Add a due date. Sort overdue tasks first. Add a dark mode button.”
The AI modifies the code. The user tests it, finds a problem, and replies:
“The due dates are not sorting correctly. Fix that and explain what changed.”
This workflow can be useful because it lowers the cost of experimentation. The user does not need to know every implementation detail before beginning. The application takes shape through conversation. Instead of writing every line by hand, the human steers the model toward the desired result.
A more technical example might involve a security administrator asking:
“Write a Python script that reads a CSV of usernames and last-login dates, then outputs accounts inactive for more than 90 days.”
The AI may produce a working script using Python’s csv and datetime modules. The administrator can then ask for refinements:
“Add error handling for missing dates. Accept the inactivity threshold as a command-line argument. Output the results to a new CSV.”
Used carefully, this can be extremely helpful. It turns vague operational needs into working tools faster than starting from a blank file.
Why People Like It
The appeal of vibe coding is speed. A user can move from idea to prototype in minutes. The AI can produce boilerplate, suggest architecture, explain unfamiliar errors, and translate intent into code. It can also help bridge knowledge gaps. Someone who understands what they want but does not know JavaScript, Python, SQL, or PowerShell well enough to write it from scratch can still begin building.
For experienced developers, the benefit is different. The AI is less of a replacement and more of an accelerator. It can generate repetitive code, produce unit tests, convert code from one language to another, explain old code, create mock data, or suggest refactoring options. In that sense, vibe coding is not necessarily anti-professional. It can be a powerful assistant when the human still understands the system, reviews the output, and remains responsible for the final result.
The Core Limitation: The AI Does Not Truly Understand Your System
The central problem with vibe coding is that the AI does not understand your environment the way a competent developer, engineer, or administrator does. It does not automatically know your production constraints, security requirements, network architecture, compliance obligations, authentication model, data sensitivity, logging standards, or deployment process. It predicts likely code based on patterns. Sometimes those patterns are useful. Sometimes they are outdated, insecure, incomplete, or simply wrong.
This means the AI can produce code that looks convincing while hiding serious problems. It may use deprecated functions. It may mishandle errors. It may skip authentication. It may expose secrets. It may trust user input. It may choose a library without checking whether that library is maintained, vulnerable, or even real. Security researchers and vendors have warned that AI-generated code can introduce insecure dependencies, hard-coded secrets, unsafe configurations, and supply-chain risks when used without review.
Common Errors and Deficiencies
One common problem is hallucinated code. The AI may invent a function, library, command-line flag, API endpoint, or configuration option that does not actually exist. This can waste time, but it can also become dangerous. If an AI suggests a plausible-sounding package name that does not exist, an attacker could potentially create a malicious package with that name and wait for users to install it. This type of dependency confusion or package hallucination is one of the more serious risks in AI-assisted coding.
Another problem is insecure defaults. A vibe-coded web application may work in a demo but fail basic security expectations. It might store passwords incorrectly, skip rate limiting, expose debugging information, allow cross-site scripting, use weak session handling, or accept SQL queries built from raw user input. These errors are not always obvious to a non-developer because the application may appear to function correctly.
A third issue is shallow correctness. The code may pass the one example the user tried, but fail edge cases. Dates may break across time zones. File paths may fail on another operating system. Input validation may reject legitimate data. A script may work for ten records but fail on ten thousand. An API integration may work when the response is perfect but crash when a field is missing.
A fourth issue is maintainability. Vibe-coded projects can grow quickly into messy, inconsistent codebases. The user asks for one more feature, then another, then another. The AI patches the existing code repeatedly. Over time, the structure can become brittle. Variables are duplicated. Functions become too large. Error handling is inconsistent. The application still “works,” but each new change becomes riskier.
Research has also begun examining how repeated AI-driven “improvements” can degrade security over time, especially when the human user accepts changes without careful intervention. One 2025 paper described this as “feedback loop security degradation,” where iterative AI modifications may introduce new vulnerabilities even when the starting point was secure.
Example: A Helpful Use Case
A safe and practical vibe-coding task might be:
“Create a Python script that renames image files in a folder by date and outputs a log of old and new filenames.”
This is a good use case because the scope is limited, the risk is manageable, and the user can test it on copied files before using it on real data. A responsible follow-up prompt would be:
“Add a dry-run mode so I can preview the changes before renaming anything.”
Then:
“Add error handling and make sure files are not overwritten if two images have the same date.”
This is vibe coding at its best: fast, iterative, practical, and bounded. The human still tests the result and asks for safeguards.
Example: A Risky Use Case
A risky prompt would be:
“Build me a login system for my business website with user accounts, password reset, and payment integration.”
The AI can produce something that looks real. It may generate registration forms, password fields, database tables, and payment API calls. But authentication and payment systems are security-sensitive. Small mistakes can expose passwords, customer data, session tokens, or financial information. This is not the kind of system that should be blindly vibe-coded and deployed without professional review.
A better version would be:
“Explain the safest architecture for adding user login and payments to a small business website. Recommend established services and describe what code should and should not be custom-built.”
That shifts the AI from being an unsupervised code generator to being a planning and education assistant.
Security Concerns
The biggest concern with vibe coding is not that AI writes bad code every time. It does not. The concern is that AI can generate large amounts of code faster than humans can properly review it. This creates scale problems. Organizations may ship features quickly while vulnerability review, dependency management, and secure design lag behind. Recent reporting has highlighted concerns that AI-generated and lightly reviewed applications are already exposing sensitive data, while security vendors warn that exploitation timelines are shrinking as attackers also use AI.
For cybersecurity and IT teams, this matters because vibe coding changes the software risk landscape. Internal tools, scripts, dashboards, automations, and web apps may now be created by employees who are not trained developers. Some of those tools may handle credentials, customer data, health information, logs, or administrative access. A script written for convenience can become a security incident if it stores tokens in plaintext, sends data to the wrong place, or runs with excessive privileges.
Best Practices for Responsible Vibe Coding
Vibe coding should be treated as assisted development, not magic. The human remains responsible for the output. A safer workflow looks like this:
- Start with a clear description of the goal, environment, constraints, and risk level.
- Ask the AI to explain its design before generating code.
- Generate small pieces instead of one massive application.
- Test each piece independently.
- Ask for error handling, logging, input validation, and edge cases.
- Review every dependency before installing it.
- Never paste real secrets, passwords, API keys, private customer data, or regulated data into prompts.
- Run security scanning where appropriate.
- Use version control so changes can be reviewed and reversed.
- Have a qualified human review anything that affects authentication, authorization, payments, production systems, sensitive data, or infrastructure.
The most important mental shift is this: AI-generated code should be considered a draft. It may be a useful draft. It may even be an excellent draft. But it is still a draft until it has been tested, reviewed, secured, and understood.
Conclusion
Vibe coding is not a fad in the sense that AI-assisted development is likely to disappear. The term may change, but the practice is now part of modern software creation. It allows people to build faster, learn faster, and experiment more freely. For small scripts, prototypes, internal utilities, and learning projects, it can be genuinely powerful.
But vibe coding also lowers the barrier to creating insecure, fragile, and poorly understood software. The danger is not merely bad syntax. The danger is working code that no one fully understands, deployed into environments where security, reliability, privacy, and maintainability matter. The future of vibe coding should not be “prompt and pray.” It should be disciplined AI-assisted development: fast where speed is useful, cautious where risk is high, and always grounded in human review, testing, and responsibility.