AI-generated computer code is rife with references to nonexistent third-party libraries, creating a golden opportunity for supply-chain attacks that poison legitimate programs with malicious packages that can steal data, plant backdoors, and carry out other nefarious actions, newly published research shows.

The study, which used 16 of the most widely used large language models to generate 576,000 code samples, found that 440,000 of the package dependencies they contained were “hallucinated,” meaning they were nonexistent. Open source models hallucinated the most, with 21 percent of the dependencies linking to nonexistent libraries. A dependency is an essential code component that a separate piece of code requires to work properly. Dependencies save developers the hassle of rewriting code and are an essential part of the modern software supply chain.

Package Hallucination Flashbacks

These nonexistent dependencies represent a threat to the software supply chain by exacerbating so-called dependency confusion attacks. These attacks work by causing a software package to access the wrong component dependency, for instance by publishing a malicious package and giving it the same name as the legitimate one but with a later version stamp. Software that depends on the package will, in some cases, choose the malicious version rather than the legitimate one because the former appears to be more recent.

Also known as package confusion, this form of attack was first demonstrated in 2021 in a proof-of-concept exploit that executed counterfeit code on networks belonging to some of the biggest companies on the planet, Apple, Microsoft, and Tesla included. It's one type of technique used in software supply-chain attacks, which aim to poison software at its very source in an attempt to infect all users downstream.

“Once the attacker publishes a package under the hallucinated name, containing some malicious code, they rely on the model suggesting that name to unsuspecting users,” Joseph Spracklen, a University of Texas at San Antonio PhD student and lead researcher, told Ars via email. “If a user trusts the LLM's output and installs the package without carefully verifying it, the attacker’s payload, hidden in the malicious package, would be executed on the user's system.”

In AI, hallucinations occur when an LLM produces outputs that are factually incorrect, nonsensical, or completely unrelated to the task it was assigned. Hallucinations have long dogged LLMs because they degrade their usefulness and trustworthiness and have proven vexingly difficult to predict and remedy. In a paper scheduled to be presented at the 2025 USENIX Security Symposium, they have dubbed the phenomenon “package hallucination.”

For the study, the researchers ran 30 tests, 16 in the Python programming language and 14 in JavaScript, that generated 19,200 code samples per test, for a total of 576,000 code samples. Of the 2.23 million package references contained in those samples, 440,445, or 19.7 percent, pointed to packages that didn’t exist. Among these 440,445 package hallucinations, 205,474 had unique package names.

One of the things that makes package hallucinations potentially useful in supply-chain attacks is that 43 percent of package hallucinations were repeated over 10 queries. “In addition,” the researchers wrote, “58 percent of the time, a hallucinated package is repeated more than once in 10 iterations, which shows that the majority of hallucinations are not simply random errors but a repeatable phenomenon that persists across multiple iterations. This is significant, because a persistent hallucination is more valuable for malicious actors looking to exploit this vulnerability and makes the hallucination attack vector a more viable threat.”

In other words, many package hallucinations aren’t random, one-off errors. Rather, specific names of nonexistent packages are repeated over and over. Attackers could seize on the pattern by identifying nonexistent packages that are repeatedly hallucinated. The attackers would then publish malware using those names and wait for them to be accessed by large numbers of developers.

The study uncovered disparities in the LLMs and programming languages that produced the most package hallucinations. The average percentage of package hallucinations produced by open source LLMs such as CodeLlama and DeepSeek was nearly 22 percent, compared with a little more than 5 percent by commercial models. Code written in Python resulted in fewer hallucinations than JavaScript code, with an average of almost 16 percent compared with a little over 21 percent for JavaScript. Asked what caused the differences, Spracklen wrote:

“This is a difficult question because large language models are extraordinarily complex systems, making it hard to directly trace causality. That said, we observed a significant disparity between commercial models (such as the ChatGPT series) and open-source models, which is almost certainly attributable to the much larger parameter counts of the commercial variants. Most estimates suggest that ChatGPT models have at least 10 times more parameters than the open-source models we tested, though the exact architecture and training details remain proprietary. Interestingly, among open-source models, we did not find a clear link between model size and hallucination rate, likely because they all operate within a relatively smaller parameter range.

“Beyond model size, differences in training data, fine-tuning, instruction training, and safety tuning all likely play a role in package hallucination rate. These processes are intended to improve model usability and reduce certain types of errors, but they may have unforeseen downstream effects on phenomena like package hallucination.

“Similarly, the higher hallucination rate for JavaScript packages compared to Python is also difficult to attribute definitively. We speculate that it stems from the fact that JavaScript has roughly 10 times more packages in its ecosystem than Python, combined with a more complicated namespace. With a much larger and more complex package landscape, it becomes harder for models to accurately recall specific package names, leading to greater uncertainty in their internal predictions and, ultimately, a higher rate of hallucinated packages.”

The findings are the latest to demonstrate the inherent untrustworthiness of LLM output. With Microsoft CTO Kevin Scott predicting that 95 percent of code will be AI-generated within five years, here’s hoping developers heed the message.

This story originally appeared onArs Technica.