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Understanding “Package Hallucination” in AI Language Models

In the realm of artificial intelligence, particularly with large language models (LLMs), the term “hallucination” refers to instances where these models generate outputs that are not only incorrect but also nonsensical or unrelated to the intended task. These inaccuracies hinder the functionality and reliability of LLMs, presenting significant challenges in both prediction and resolution. A recent study slated for presentation at the 2025 USENIX Security Symposium has introduced the specific concept of “package hallucination.”

The researchers conducted a comprehensive analysis involving 30 distinct tests, diving into 16 using Python and 14 with JavaScript, which collectively produced an impressive 576,000 code samples. The results revealed an alarming figure: of the 2.23 million package references analyzed, approximately 440,445, or 19.7 percent, were linked to non-existent packages. Notably, among these hallucinations, 205,474 featured unique package names.

What adds a layer of concern to the issue of package hallucinations is their potential exploitation in supply-chain attacks. The study noted that 43 percent of these hallucinations appeared multiple times across over 10 queries. Furthermore, the researchers remarked that “58 percent of the time, a hallucinated package is repeated more than once in 10 iterations,” indicating that these hallucinations are not mere random mistakes. Instead, they represent consistent patterns that could be particularly advantageous for malicious entities. This consistent nature enhances the risk profile of such hallucinations, marking them as a more serious threat vector.

In essence, many of these package hallucinations do not occur randomly. Certain names of non-existent packages are consistently generated, presenting a window of opportunity for adversaries. By pinpointing these recurrently hallucinated package names, attackers can create malware under these fictitious labels, subsequently waiting for unsuspecting developers to interact with them.

The research also shed light on how different LLMs and programming languages vary in their rates of package hallucinations. Open-source LLMs, including CodeLlama and DeepSeek, exhibited an average hallucination rate nearing 22 percent, starkly contrasting with a little over 5 percent from commercial alternatives. Additionally, the study found that Python code led to fewer hallucinations compared to JavaScript, averaging around 16 percent versus just above 21 percent for JavaScript.

While these findings underscore the efficacy of certain programming languages and models over others, they also highlight the pressing need for further investigation into the underlying causes behind these disparities. Understanding the roots of package hallucination will be crucial in developing strategies to mitigate their occurrence and enhance the safety of AI applications.

Source
arstechnica.com