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A new form of malicious software sabotage known as "slopsquatting" is emerging in the software supply chain, where AI-powered code generation tools hallucinate package names and create fake software packages under non-existent names. This phenomenon has significant implications for developers and security professionals.
The use of AI-powered code generation tools has introduced a new form of malicious software sabotage known as "slopsquatting" in the software supply chain. About 5.2% of package suggestions from commercial AI models and 21.7% from open-source models do not exist, according to a recent study. Large language models (LLMs) can hallucinate or make up package names that do not exist in reality, leading to fake packages. Users who rely on LLM-generated code should double-check these outputs against reality before implementing them due to real-world consequences. The problem is not limited to specific software or programming languages and can be used for malicious purposes. Improving the accuracy and transparency of AI-generated code suggestions, educating users, and implementing better verification mechanisms are essential to address this emerging threat.
The use of artificial intelligence (AI) code generation tools has revolutionized the way developers write software, but it also introduces new risks to the software supply chain. As AI-powered code assistants become more prevalent, security researchers have discovered that these tools can hallucinate package names, leading to a form of malicious software sabotage known as "slopsquatting." This phenomenon involves creating and distributing fake software packages under non-existent package names, which can be installed by unsuspecting users or developers.
In a recent study, security firm Socket found that approximately 5.2% of package suggestions from commercial AI models did not exist, compared to 21.7% from open-source models. This suggests that the issue is not limited to specific AI models but is a broader problem affecting the software supply chain as a whole.
The mechanism behind this phenomenon involves large language models (LLMs) generating code snippets and package suggestions based on user prompts. While LLMs are designed to provide helpful suggestions, they can also hallucinate or make up package names that do not exist in reality. These fake packages can be incredibly convincing, with some even having realistic-looking README files, GitHub repositories, and blogs.
According to security developer-in-residence at the Python Software Foundation, Seth Michael Larson, "We're in the very early days looking at this problem from an ecosystem level." He added that it is difficult, if not impossible, to quantify the number of attempted installs happening due to LLM hallucinations without more transparency from LLM providers. Users who rely on LLM-generated code should double-check these outputs against reality before implementing them, as there can be real-world consequences.
Feross Aboukhadijeh, CEO of security firm Socket, echoed this sentiment. "With AI tools becoming the default assistant for many, 'vibe coding' is happening constantly." He noted that developers often prompt the AI, copy the suggestion, and move on without verifying its accuracy. This lack of verification can lead to installations of fake packages, which can be used for malicious purposes.
The problem is not limited to specific software or programming languages. Security firm Socket has seen realistic-looking packages under names like Microsoft Teams, NCSAM, NCSC, and even Quantum key distribution. These fake packages often have convincing README files and GitHub repositories, making it difficult for users to distinguish them from real packages.
As the use of AI-powered code generation tools continues to grow, it is essential to address this emerging threat. Developers, security professionals, and LLM providers must work together to improve the accuracy and transparency of AI-generated code suggestions. This can involve implementing better verification mechanisms, providing more information about the origins and potential risks of LLM-generated packages, and educating users about the importance of double-checking these outputs.
In conclusion, the rise of AI-powered code generation tools has introduced a new form of malicious software sabotage known as "slopsquatting" in the software supply chain. As this phenomenon continues to evolve, it is crucial that we address this threat head-on by improving the accuracy and transparency of LLM-generated code suggestions and educating users about the potential risks involved.
Related Information:
https://www.ethicalhackingnews.com/articles/The-Rising-Threat-of-AI-Generated-Malware-A-New-Form-of-Slopsquatting-in-the-Software-Supply-Chain-ehn.shtml
https://go.theregister.com/feed/www.theregister.com/2025/04/12/ai_code_suggestions_sabotage_supply_chain/
Published: Sat Apr 12 07:07:40 2025 by llama3.2 3B Q4_K_M
