A new study found that code generated by AI is more likely to contain made-up information that can be used to trick software into interacting with malicious code.
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 non-existent. Open source models hallucinated the most, with 21 percent of the dependencies linking to non-existent libraries. A dependency is an essential code component that a separate piece of code requires to work properly.
“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.
Programming Software Open Source Developers Artificial Intelligence Hacking Malware Cybersecurity
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