The Anti-virus Times

Tuesday, September 22, 2026

In recent years, neural networks powered by large language models have rapidly transitioned from being a fun text-based conversation partner to a full-fledged professional tool that is actively used in a wide range of fields. The alarming rate of AI’s development has sparked heated debate, while the results of its work are already visible everywhere—from images on local service providers' flyers to impacting global processes. Of course, as products of the digital industry, neural networks have had a primary and direct impact on the IT sector, including the software development process.

Within the professional community, the software development lifecycle is safeguarded by stringent standards: mandatory code reviews, multi-stage testing, and codebase audits. In this environment, neural networks have become an excellent tool for automating routine tasks—for instance, they can write boilerplate code or assist in generating automated tests. However, beyond major corporations and old-school enthusiasts, the rapid development of AI has spawned a completely different phenomenon known as "vibe coding" (relaxed programming). This term describes an approach in which a person—often someone far removed from traditional programming—creates fully effective software without ever manually writing a single line of code. All this individual must do is formulate tasks for the AI assistant in the form of text in ordinary human language.

The euphoria surrounding this "democratization" of development extended beyond home experiments and effectively created a market for rapid development. Turning an idea into a working product has become easier than ever, enabling anyone to build functional web services, mobile apps, or chatbots on their own in just a few hours. At the same time, the field of information security has faced an avalanche-like increase in vulnerabilities in the generated software. One of the problems is that AI assistants are focused exclusively on immediate functionality—they generate code designed simply to fulfill a user's specific request. With this approach, the security, stability, and overall efficiency of algorithms fade into the background, and the project’s author is simply incapable of assessing hidden architectural flaws.

In today's issue of the Antivirus Times blog, we will explore how the craze for AI-assisted coding affects the digital ecosystem’s security and the risks that the vulnerabilities in such software pose to end users.

Black box operator

To understand why solutions created through "vibe coding" pose fundamental threats, we need to look at the very root of the problem. This vulnerability stems from two key flaws—one on the human side and the other on the part of the neural network itself.

The first factor is the program author's absolute incompetence when faced with their "own" code. The vibe-coder operates in a state of total information blindness, interacting with the program as one would with an absolute black box. This approach differs fundamentally from the work of a professional programmer even when that person is interacting with closed APIs and libraries. Any third-party interface always comes with rigorous documentation, fixed data types, and clear specifications; but above all, the developer possesses an engineering foundation and an understanding of cause-and-effect relationships.

However, an AI-generated program has no such boundaries. The author sees only the external shell (the working interface, buttons, and forms) but has absolutely no understanding of the internal data-processing logic. Without knowing exactly how the function works, such a person is physically incapable of modelling a threat and testing how the application responds to non-standard or malicious actions. They cannot ask the question, "What happens if an attacker passes a negative number or a string here instead of the expected JSON?" The program operates normally but is completely defenseless against the unconventional actions of cybercriminals.

The second factor lies in the very nature of artificial intelligence. It is important to remember that a neural network is not a programmer and has no ability to think like an engineer; it is a probabilistic compiler with probabilistic compiler with a vast knowledge base, and its logic is built upon mathematical weights upon mathematical weights. AI does not create code from scratch based on security and optimization considerations; instead, it generates the most probable sequence of characters based on the terabytes of data it was trained on. In fact, it makes no difference to a neural network whether it is writing critical code, a blog post, or a recipe for a non-existent dish. A direct mathematical relationship applies here: the larger the database, the higher the probability of obtaining a flawed and unsafe result. The neural network avarages the experience of millions of open repositories on the Internet, where outdated approaches, gross errors, and legacy code have accumulated for years. As a result, AI produces a boilerplate solution that accomplishes the user's task but simultaneously contains vulnerabilities from 20 years ago.

The digital heritage of the past: where AI gets its bad habits

There exists the dangerous illusion that the neural networks of tech giants are trained exclusively on pristine, exemplary code. In reality, the databases used to train LLMs represent the colossal repository of the entire IT industry's digital heritage accumulated over recent decades.

An attentive reader might ask: can’t AI creators simply purge the dataset of "junk" and outdated code? The fact is that it is impossible to do this at the system level. For a large language model to understand programming syntax and semantics, translate from Python to C++, and maintain context, it needs trillions of tokens of data. If AI were to be trained solely on "ideal" code (such as the Linux kernel, anonymized corporate databases, or reference libraries), the size of the training dataset will be compressed thousands of times over. The model simply won't accumulate a sufficient mass of mathematical weights to become flexible and universal. It will turn into a rigid reference tool capable of generating only a narrow set of templates and will lose the ability to solve non-standard user tasks creatively.

Moreover, "junk code" is also a valuable textbook. For a neural network, bad or broken code is not an error to be removed from the database but crucial context. For a neural network to be flexible, understand this context, and—above all—act as a reviewer capable of finding and fixing bugs written by others, it must know what errors look like. The model must process millions of examples of diverse code, insecure configurations, and outdated approaches from technical forums in order to learn to recognize them.

However, such an architecture has a downside. Even after the rigorous filtering that developers naturally employ, AI remains a probabilistic compiler rather than a generator of perfect logic. A neural network has no ability to think like an engineer and does not understand safety at a conceptual level; it merely seeks statistical patterns and calculates word sequences based on their mathematical weights. When a vibe coder formulates a superficial task (such as "quickly sketch out a function for sending data to the server"), the AI follows the path of least resistance and outputs the most mathematically average option from its database, where security checks are often disabled for the sake of simplicity and immediate functionality.

As a result, system errors from over 20 years ago—or solutions doomed to fail—are migrating en masse into applications created by neural networks, just because the algorithm assembled a "probable" result from digital noise in response to the user's query. Let's talk about some of them:

• Hardcoded credentials. AI can insert test passwords, secret encryption keys, or API tokens directly into program code. The vibe coder copies this bit of code into the work environment without giving a second thought to the fact that any attacker who gains access to the application files can instantly compromise the associated.
• Outdated cryptography and weak algorithms. Neural networks regularly suggest using outdated hash function standards (such as MD5 or SHA-1) for data protection. Moreover, AI often generates this code in a highly simplified form—without using "crypto salt" (random data added to the password before hashing). As a result, the protection becomes weak: hashes of simple and medium-strength passwords can be recovered by attackers using pre-existing databases, while the algorithms themselves are vulnerable to collision attacks.
• Drastic simplifications for the sake of functionality. To ensure the code works on the first try and doesn't overwhelm the user with errors, the AI may disable certain functions—such as SSL certificate verification during network communication. The project’s author will never notice the vulnerabilities, but for a hacker, this vastly expands the attack.

Thus, while a program built by a probabilistic compiler is functional, it often has extremely weak implementation, which turns the finished product into an easy target.

Code avalanche: Why redundancy is killing audits

Another fundamental problem with vibe coding is that it drastically distorts what development entails. Old-school programmers and professional engineers vividly remember the period when every byte of memory was worth its weight in gold, and the most complex space programs or iconic applications were packed into a few kilobytes of code. That required strict discipline, a deep understanding of the architecture, and thoughtful optimization of every decision.

In the era of AI coding, the concept of algorithmic compactness has been completely erased. Neural networks generate thousands of lines of code in seconds, enabling a single person to create in a day the amount of software that would previously have taken an entire team a month to write. Based on a text prompt, AI instantly generates bulky and redundant code structures. As a result, a project that is "modest" at its core grows to colossal proportions within a couple of days—giving rise to the so-called "code avalanche" effect.

From an information security perspective, this redundancy constitutes a major vulnerability, as the codebase becomes completely non-transparent. And while in one of our previous issues, we examined in detail just how important a source code review is for product security, vibe coding completely bypasses this protective barrier. Even a real developer is physically incapable of reading and processing the massive volume of text that a neural network has generated for them in half an hour. Logical errors, hidden implementation flaws, and auxiliary functions inevitably emerge from these endless, sprawling heaps of code. Essentially, the project loses not only the programmer but also testing and auditing—and, along with that, security.

An ideal base for a Supply Chain Attack

A Supply Chain Attack has always been considered one of the most dangerous cybersecurity incidents. The insidious nature of this method lies in the fact that the attacker does not compromise the finished product itself but instead infiltrates the production process and the automated update-delivery mechanism. And projects created using vibe coding are prime targets in this regard.

That’s because vibe coding relies entirely on cloud technologies: AI platforms automatically save code to online repositories and with a single click deploy finished websites or chatbots to remote production servers. Ongoing software updates and improvements also take place via the cloud. And this is precisely where a loophole opens up for cybercriminals. If a hacker steals a vibe coder's cloud-based development account password, they can easily access the project and insert a malicious backdoor into the repository. The attacker needs only to use AI to generate malicious code designed to perfectly mimic a specific project. The vibe coder acts as a blind link in the chain here and will never notice extraneous lines within the stream of code. During the next automatic cloud-based update, the backdoor will infiltrate the work environment, and the project creator won't have a clue as to when their website or program became a weapon in the hands of attackers.

However, there is another far more sophisticated way to attack a supply chain—one specific to the modern era: infection via AI model "hallucinations". Neural networks often assemble programs like a construction kit, using thousands of pre-existing modules created by other people. At the same time, AI is prone to inventing names for non-existent libraries and advising the user to install them to solve a problem. Attackers use special scripts that continuously query the AI and compile a database of such fabricated names. Then attackers register malicious packages under those exact names in official public repositories. A vibe coder, blindly following instructions from an AI chatbot, copies the installation command, and the cloud-based development pipeline automatically downloads the hacker-infected module directly into the project. Thus, blind trust in algorithms and a lack of expertise create ideal conditions for malicious attacks.

Why vibe coding has a right to exist

The threats described above appear significant, however, there is no need to treat vibe-coding vulnerabilities as an insolvable problem and give in to panic. In the cybersecurity industry, code-based attacks are well examined, and modern security strategies are steadily reducing their effectiveness—even as the volume of such "AI-generated" programs continues to grow.

First, let us reassure the reader right away: serious, large-scale commercial software is never created using vibe coding. Professional software development is governed by strict architectural standards. Consequently, hand-scribbled applications simply have no chance of making it into the infrastructure of major companies or onto the servers of critical services. The professional IT community knows how to clearly delineate trusted environments and promptly cut off potential threats.

Second, the passive invisibility of vulnerable software generated by AI instantly vanishes the moment anomaly analysis and behavioural monitoring take effect. This is where the end user’s primary line of protection comes into play—comprehensive antivirus software. It couldn’t care less who wrote a program—a qualified engineer or a neural network acting on a text prompt. An antivirus evaluates a program based on its actual actions within the system rather than the elegance of its code. As soon as a user-launched program attempts to engage in suspicious activity—such as accessing system folders or secretly downloading third-party modules from the network—protective algorithms use heuristic analysis to instantly block the threat.

However, the main protection against the risks of vibe coding lies not in software filters but in shifting attitudes toward the technology itself. Artificial intelligence and large language models offer ideal, unprecedented environments for learning and professional growth. But this is true only if the process is approached thoughtfully. Learning and creating are fundamentally different from generating raw results for a quick profit. The widespread craze of blindly copying AI-generated answers has led to a sad trend: the devaluation of engineering work, when the creator produces digital garbage without even attempting to understand how it actually functions.

However, if one engages with an AI model thoughtfully, the results change dramatically. When a person possesses the necessary core competencies and speaks the same professional language as the neural network—i.e., the person clearly defines architectural boundaries, requests that security standards be followed, requires explanations for its choice of algorithms, and demands that tasks be decomposed—the model’s mathematical weights adjust accordingly. AI begins to generate code of superior quality that is both optimized and secure. A neural network is a mirror: a shallow vibe breeds uncontrollable chaos, whereas a rigorous engineering approach transforms the algorithm into a powerful and secure tool.

Information security in the age of artificial intelligence follows a simple rule: if you cannot control how a program was created, you must control how it runs. In this confrontation, adhering to digital hygiene and employing comprehensive antivirus protection remain the most reliable tool for restoring the user's control over their digital environment.