Self-Replicating LLM Artifacts and Accidental Supply-Chain Contamination (github.com)

A recent research note has uncovered a troubling failure mode involving self-replicating code artifacts in large language models (LLMs), particularly in the context of AI coding assistants. This phenomenon emerged while developing a bootstrapping installer for a security-focused Linux distribution, where recursive logic failures were inadvertently created. The artifacts, which were temporarily available on GitHub, demonstrate a concerning pattern that could degrade the performance of multiple LLMs, raising significant supply-chain issues for organizations relying on these models for code generation. The findings suggest that this recursive structure is not merely a glitch, but may act like a "logical prion" that could propagate through the code-assistant ecosystem. The researcher, motivated by a commitment to transparency and safety in AI technologies, emphasizes the importance of documenting these risks to prevent potentially catastrophic failures for non-expert users. Planned experiments aim to further explore the extent of this behavior across open-weight models, underscoring the need for careful monitoring of LLM outputs, particularly in critical software development contexts. This work highlights the broader implications of LLM integrity and the responsibilities of developers towards robust and trustworthy AI tools.