Hackers can use 9 of the most popular AI tools to assemble massive botnets

A new attack vector dubbed 'HalluSquatting' exploits a well-known weakness of large language models: their tendency to hallucinate plausible but incorrect answers. According to Ars Technica, researchers have demonstrated that nine of the most popular AI tools can be weaponized to assemble massive botnets. The technique works by prompting an LLM to generate code that includes references to nonexistent software packages. Since the model cannot say 'I don't know,' it invents package names that appear legitimate. Attackers then publish malicious packages under those names on public repositories. When developers run the LLM-generated code, their systems automatically download and execute the malicious packages, enrolling them into a botnet. The attack is particularly insidious because it requires minimal effort and can scale across many tools. For developers and solopreneurs building AI workflows, this highlights a critical security gap: relying on LLM output without verification can introduce backdoors. The findings underscore the need for robust validation of any code or dependencies produced by AI systems, especially when integrating multiple tools into a pipeline.