Source URL: https://simonwillison.net/2025/Apr/12/andrew-nesbitt/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Andrew Nesbitt
Feedly Summary: Slopsquatting — when an LLM hallucinates a non-existent package name, and a bad actor registers it maliciously. The AI brother of typosquatting.
Credit to @sethmlarson for the name
— Andrew Nesbitt
Tags: ai-ethics, slop, packaging, generative-ai, supply-chain, ai, llms, seth-michael-larson
AI Summary and Description: Yes
Summary: The concept of “slopsquatting” pertains to a security risk associated with Large Language Models (LLMs), where an LLM generates a fictitious package name that can be exploited by malicious actors. This highlights a critical intersection of generative AI security and supply chain vulnerabilities, urging professionals in AI and software security to remain vigilant against such novel threats.
Detailed Description: The text introduces the term “slopsquatting,” which describes a new form of malicious activity that leverages the limitations of LLMs, particularly in their tendency to “hallucinate” or generate non-existent package names. This represents a significant concern for developers and security professionals involved in AI, software, and supply chain security.
Key points include:
– **Definition of Slopsquatting**: A term coined to describe when an LLM mistakenly generates a name of a non-existent software package. This can lead to vulnerabilities if a malicious actor registers the unverified package name to exploit unsuspecting users or systems.
– **Comparison to Typosquatting**: The concept is likened to typosquatting, where attackers register misspelled versions of popular domain names; slopsquatting extends this idea to AI-generated outputs.
– **Implications for Security**:
– **Supply Chain Security**: Developers and CI/CD pipelines must be cautious of the potential introduction of these fictitious packages into their environments, which can compromise software reliability and integrity.
– **AI Security**: The incident highlights the importance of securing the outputs of LLMs, ensuring that such systems are robust against generating erroneous or misleading information that could be exploited.
– **Call for Vigilance**: This emerging threat underscores the necessity for organizations to implement stringent code reviews and validation processes for dependencies generated via LLMs.
Overall, slopsquatting is another reminder of the evolving threat landscape that AI introduces and emphasizes the need for proactive measures in governance and compliance regarding AI and software development practices.