How AI hallucinations are becoming the new major security risk

Introduction: When artificial intelligence invents software packages that don't exist

How AI Hallucinations Are Becoming the New Major Security Risk? In the world of modern technology, artificial intelligence models have become indispensable tools for developers, researchers, and IT professionals. But as the adoption of these technologies grows exponentially, so too does a less visible, but extremely dangerous threat: AI hallucinations. These are situations where a large language model (LLM) generates false information, but presents it with a high level of confidence and plausibility. In the context of cybersecurity, this phenomenon has evolved from a simple technical curiosity into a concrete and sophisticated attack vector, with real consequences for IT infrastructures around the world.

The year 2026 brought to the forefront a new exploitation technique based on precisely this fundamental weakness of AI models: slopsquattingThis attack method exploits the tendency of LLM models to recommend non-existent software packages, which attackers then register in public registries to distribute malicious code. It is a threat that combines social engineering, supply chain attacks, and intrinsic AI vulnerabilities in an extremely ingenious and difficult-to-detect way.

What are AI hallucinations and why are they a security issue?

Technical definition of hallucinations in LLM models

Large-scale language models, such as GPT-4, Claude, Gemini, or Llama, are trained on massive amounts of textual data. The inference process of these models does not involve real-time factual checking, but rather statistical prediction based on the probabilities associated with sequences of tokens. This fundamental architecture makes hallucinations possible: the model generates answers that appear linguistically and contextually correct, but are factually incorrect or completely fabricatedIn the field of programming, this manifests itself by recommending libraries, packages, or APIs that simply do not exist in reality.

Recent studies show that the rate of software package hallucinations among popular LLM models can reach 20% or even more, depending on the technical domain and the specificity of the query. In other words, one in five packages recommended by an AI model might not exist, thus providing a perfect opportunity for attackers to monitor these false recommendations and turn them into real traps.

How the slopsquatting technique works

Slopsquatting is the natural evolution of classic typosquatting, adapted to the AI ​​era. In traditional typosquatting, attackers registered domains or packages with names similar to legitimate ones (for example, requests instead of requests), relying on user typing errors. Slopsquatting eliminates the reliance on human error and replaces it with the systematic error of the AI ​​model.

The attack process takes place in several well-defined steps:

The attacker repeatedly queries various LLM models with questions like “Which Python package should I use to implement X functionality?” The AI ​​model confidently recommends a package that is not in PyPI, npm, NuGet, or another public registry. The attacker registers that package in that registry, inserting disguised malicious code inside it. The next developer who receives the same recommendation from the AI ​​installs the package without further verification, thus infecting their development or production environment. The malicious code can extract credentials, install backdoors, initiate attacks on the software supply chain, or compromise the entire CI/CD pipeline.

What makes this attack particularly dangerous is the credibility that the AI ​​gives to the recommendationA developer who receives a suggestion from a trusted AI assistant is much less likely to verify the authenticity of that package, compared to a recommendation found on an anonymous internet forum.

The scale of the problem in 2026: Alarming data and statistics

Exponential growth of AI-based hallucination attacks

According to security reports from the first quarter of 2026, the number of security incidents directly related to AI hallucinations increased by over 350% compared to the same period last yearThis dramatic increase reflects two simultaneous trends: the massive adoption of AI assistants by software development teams and the maturation of exploitation techniques used by malicious actors.

The organizations affected come from diverse sectors, including fintech, digital health, critical infrastructure, and defense. In all of these cases, the common vector was uncontrolled reliance on the recommendations of an AI model, without implementing proper validation and verification processes. Supply chain attacks Slopsquatting attacks have become the third most common cause of compromise of software development environments globally, surpassing classic phishing attacks in certain sectors.

The most vulnerable software ecosystems

Not all software package ecosystems are equally vulnerable. Analysis of incidents from 2026 shows that certain registries are more exposed:

PyPI (Python Package Index) – the most affected ecosystem, due to Python's popularity among AI/ML projects and the ease with which anyone can publish packages. npm (Node Package Manager) – the second most targeted, with millions of active packages and a minimally restrictive publishing process. NuGet (.NET) – growing as a target, especially in the context of enterprise applications developed with AI assistance. RubyGems and Cargo (Rust) – less affected at the moment, but the upward trend is clear.

The problem is amplified by the fact that many organizations have not yet implemented clear policies regarding checking the origin of packages (package provenance verification) and do not use Software Bill of Materials (SBOM) tools to control dependencies introduced into their projects.

Implications for software supply chain security

Why supply chain security becomes critical in the AI ​​era

Software supply chain attacks are not a new concept. The 2020 SolarWinds incident and the 2024 XZ Utils library compromise demonstrated how devastating the consequences can be when malicious code enters development ecosystems. But slopsquatting adds an additional layer of complexity to the problem, as the initial attack vector is not a conventional technical vulnerability, but a cognitive limitation of AI systems.

In a context where more and more organizations are adopting AI-assisted development, where developers use assistants like GitHub Copilot, Cursor, Tabnine, or similar platforms to speed up the coding process, the attack surface expands considerably. Every interaction with an AI model that produces a non-existent package recommendation represents a potential security breach, especially in organizations that do not have strict dependency control policies.

Risks for CI/CD environments and infrastructures cloud

A particularly worrying aspect of these attacks is their potential impact on automated CI/CD pipelinesIn the environments DevOps In modern pipelines, code is compiled, tested, and deployed automatically, often without direct human intervention. If a malicious package is introduced into such a pipeline, the consequences can spread quickly and silently:

Exfiltration of secrets from build environments (API tokens, credentials) cloud, SSH keys) Infecting build artifacts distributed to customers or partners Installing persistence in Kubernetes or containerized infrastructures Compromising private container registries (Docker Hub, AWS ECR, Google Artifact Registry) Lateral movement to other systems in the organization's network

The speed with which such an attack can propagate through the infrastructure of a modern organization based on cloud is one of the biggest concerns for security teams in 2026.

Defense and risk mitigation strategies

Systematic verification of AI recommended packages

The first and most important line of defense against slopsquatting is implementing a rigorous verification process for any package recommended by an AI model, before installing it. This check should include several levels:

Confirming the existence of the package in the official registry and checking its release history Analyzing the number of downloads, reviews, and maintenance activity Verifying the digital signature and provenance of the package (using tools such as Sigstore or cosign) Scanning the source code or binary artifact with specialized malware analysis tools Correlating AI recommendations with official documentation or independently verified sources

Implementing security policies at the organizational level

Beyond individual checks, organizations must implement systemic security policies that limit the risk of AI hallucinations on a large scale. These policies should include:

Using private package registries (Artifactory, Nexus, AWS CodeArtifact) that serve as a proxy and filter allowed dependencies Implementing the principle allowlist for dependencies: only explicitly approved packages can be used in the organization's projects Automatic generation and validation of SBOMs (Software Bill of Materials) for each build Integration of software composition analysis (SCA) tools into CI/CD pipelines, with automatic blocking upon detection of unknown or suspicious packages Continuous training of development teams on the specific risks of AI-assisted development

The role of safer AI models and grounding techniques

In addition to the defensive measures adopted by organizations, AI model makers have a responsibility to reduce the rate of hallucinations through advanced techniques. Retrieval-Augmented Generation (RAG) represents one of the most promising approaches: instead of generating answers exclusively from parameters learned during training, the model consults updated and verified databases in real time, thus significantly reducing the probability of recommending non-existent packages.

Other relevant techniques include specialized fine-tuning on clean and verified data sets from package registries, implementing mechanisms Constitutional AI which penalizes unverifiable recommendations and the use of AI agents capable of performing external verifications in real time before providing a final answer to the user.

The outlook for regulations and liability in 2026

The rapid increase in security incidents related to AI hallucinations has caught the attention of regulators in Europe and the United States. EU AI Act, AI systems used in high-risk contexts, including those used in software development for critical infrastructure, are subject to strict transparency and auditability requirements. Providers of LLM models used in professional environments will be required to document hallucination rates and implement technical measures to limit them.

At the same time, there is increasing discussion about legal responsibility of organizations adopting AI without implementing adequate oversight measures. A major security incident caused by a malicious package installed based on a non-critical AI recommendation could result in legal liability for both the affected organization and, under pending legislative proposals, the provider of the AI ​​model in question.

Conclusion: Blind trust in AI is the most dangerous attack vector in 2026

AI hallucinations are no longer a mere academic curiosity or a tolerable limitation of technology. In 2026, they represent a active, systematic and rapidly growing attack vector, exploited with increasing sophistication by malicious actors around the world. Slopsquatting is just the latest example of how the fundamental limitations of LLM models can be turned into effective cyberweapons.

The answer to this challenge cannot be simplistic. We cannot afford to forgo the real benefits of AI-assisted development, but we cannot afford to adopt these technologies without a robust cybersecurity culture which should include critical review, clear policies and continuous investment in the education of technical teams. Security in the AI ​​era means, more than ever, combining artificial intelligence with human intelligence and well-defined organizational processes.

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