AI Hallucination Squatting: The New Frontier of Supply Chain Attacks 🤖📦

The software supply chain is under siege by a new, invisible enemy. For years, developers have been warned about typosquatting—the art of registering requessts to catch a developer who mistypes requests. But a more sophisticated and insidious threat has emerged, born not from human error, but from the creative failures of Artificial Intelligence.

This is AI Hallucination Squatting (often called “AI Package Hallucination” or “Slopsquatting”).

It creates a “Vibe-to-Malware” pipeline where attackers no longer need to wait for a developer to make a mistake; they simply wait for an AI to lie. As millions of developers turn to Large Language Models (LLMs) like ChatGPT, Claude, and GitHub Copilot to write code, they are inadvertently inviting a new breed of supply chain attacks into their corporate networks.

What is AI Hallucination Squatting?

AI Hallucination Squatting occurs when an attacker identifies a non-existent software library or package that an LLM frequently “hallucinates” (invents) and then registers that package name on public repositories like NPM (Node.js), PyPI (Python), or RubyGems.

When a developer subsequently asks the AI for a solution to a specific problem, the AI confidently suggests this “phantom” package. The developer, trusting the AI’s “vibe” and authoritative tone, copies the installation command (e.g., npm install hallucinated-lib). Since the attacker has recently registered the name, the package manager successfully installs the malicious code, compromising the developer’s environment instantly.

The “Vibe-to-Malware” Pipeline

The attack vector is terrifyingly automated and efficient. It relies on the implicit trust developers place in AI-generated code.

1. The Prompt: A developer asks an LLM, “How do I interface with the X API in Python?”
2. The Hallucination: The LLM, trying to be helpful but lacking a direct answer in its training data, predicts a plausible-sounding package name: x-api-connector-py.
3. The Squat: Attackers, who have already automated the monitoring of LLMs for these specific hallucinations, have already registered x-api-connector-py on PyPI.
4. The Infection: The developer runs pip install x-api-connector-py. The package manager finds the library (which now exists, thanks to the hacker) and installs a payload that steals API keys, environment variables, or SSH credentials.

Why Do LLMs Invent Packages?

To understand the attack, one must understand the flaw in the technology. LLMs do not “know” facts; they predict the next most likely token in a sequence.

• Plausible Naming Conventions: If an AI sees thousands of packages named google-cloud-storage and azure-storage-blob, it infers that a package named aws-storage-connector should exist, even if it doesn’t.
• Outdated Training Data: An AI might recommend a library that was deprecated or removed years ago. Attackers can revive these “zombie” packages with malicious code.
• Confabulation: When pressed for a solution to a niche problem, the model’s “helpfulness” objective overrides its factual accuracy mechanism. It invents a library to satisfy the user’s request for a quick fix.

The “Slopsquatting” Phenomenon

Security researcher Seth Larson coined the term “Slopsquatting” to describe this behavior. Unlike typosquatting, which relies on a victim’s clumsiness, slopsquatting relies on the slop—the low-quality, unverified garbage code churned out by generative AI. As developers copy-paste this “slop” without verification, they bypass traditional security checks.

Real-World Case Studies: It’s Already Happening

This is not theoretical. Security researchers have demonstrated the efficacy of this attack vector in the wild.

1. The huggingface-cli Incident

In a landmark study, Bar Lanyado, a security researcher at Lasso Security, discovered that LLMs frequently hallucinated a package named huggingface-cli for Python. While Hugging Face is a massive AI platform, this specific Python package did not exist on PyPI (the official tool is usually installed differently).

Lanyado registered the empty package huggingface-cli to test the theory.

Result: Within three months, the package was downloaded over 30,000 times.

The Scary Part: It wasn’t just random devs. Major companies, including Alibaba, were found to have copy-pasted instructions into their own public documentation that referenced this hallucinated package, creating a cascading supply chain risk.

2. Vulcan Cyber’s Stack Overflow Experiment

Researchers at Vulcan Cyber automated the process of asking ChatGPT thousands of questions scraped from Stack Overflow.

They found that ChatGPT hallucinated packages in widely used languages like Node.js and Python.

In one instance, the AI suggested a package for solving a specific arithmetic problem. When the researchers checked NPM, the package didn’t exist—yet.

The research highlighted that attackers can use the same APIs to generate thousands of hallucinations and register them before a human developer ever sees the prompt.

The Economics of the Attack: Why It Works

For cybercriminals, AI Hallucination Squatting offers a higher ROI (Return on Investment) than traditional methods.

Feature	Typosquatting	Hallucination Squatting
Trigger	User makes a typing error.	AI confidently recommends it.
Trust Level	Low (User might notice requessts).	High (AI is viewed as an “expert”).
Targeting	Broad / Random.	Targeted (Specific problem solvers).
Persistence	Low (Typos are random).	High (LLMs are deterministic; they repeat the same lie).

Repetitiveness is Key

Research from the USENIX Security Symposium showed that LLMs are creature of habit. If GPT-4 suggests a fake package to one developer, there is a high probability (often >20%) it will suggest the exact same fake package to another developer asking a similar question. Attackers exploit this determinism to camp on the most “popular” lies.

How to Defend Against AI Hallucination Squatting

The defense against this threat requires a shift in behavior for both individual developers and enterprise security teams. The mantra “Trust but Verify” is no longer sufficient; we must move to “Verify, Then Trust.”

🔐 For Developers

Check the “Vibe”: Never pip install or npm install a library recommended by an AI without visiting its repository first.

Verify the Metrics: - Download Count: Does this package have 500 downloads or 5 million? A package the AI claims is “industry standard” should not have near-zero downloads. - Release Date: Was the package published yesterday? If so, it’s a red flag. - Author Reputation: Is the publisher a known entity (e.g., Google, Facebook, verified user) or a random handle?

Read the Readme: Hallucinated packages often have empty or generic README.md files. If the documentation looks sparse or auto-generated, do not install it.

🏢 For Organizations

• Private Registries & Proxies: Use tools like Artifactory or Sonatype Nexus. Configure them to block the installation of packages that are less than 30 days old or have low reputation scores.
• Scope Your Packages: Enforce the use of scoped packages (e.g., @company/library) to prevent confusion with public packages.
• AI Usage Policies: Explicitly train developers on the risks of AI hallucinations. Update security guidelines to treat AI-generated code as “untrusted input” similar to data from an external user.

🤖 For AI Vendors

• RAG (Retrieval-Augmented Generation): AI models must be grounded in reality. Vendors should connect LLMs to live package manager APIs to verify that a package exists before recommending it.
• DPO (Direct Preference Optimization): Models should be penalized during training for hallucinating URLs or citations.

Conclusion: The Era of “Skeptical Coding”

AI Hallucination Squatting represents a fundamental shift in the cybersecurity landscape. We have moved from an era where we feared machines taking over, to an era where we fear machines being incorrect.

The “Vibe-to-Malware” pipeline is effective because it exploits the path of least resistance. Developers want solutions fast, and AI provides them instantly. But speed is the enemy of security.

As we integrate more AI into our SDLC (Software Development Life Cycle), the human developer’s role must evolve from “writer” to “auditor.” The code on your screen might look correct, the package name might sound legitimate, and the AI might be confident. But in 2024 and beyond, if you didn’t verify it, you didn’t write it—and you shouldn’t trust it.