The hidden vulnerabilities of open source (FastCode)

The FastCode site has alengthy article on how large language models make open-source projectsfar more vulnerable to XZ-style attacks.

Open source maintainers, already overwhelmed by legitimatecontributions, have no realistic way to counter this threat. How doyou verify that a helpful contributor with months of solid commitsisn't an LLM generated persona? How do you distinguish betweengenuine community feedback and AI created pressure campaigns? Thesame tools that make these attacks possible are largelyinaccessible to volunteer maintainers. They lack the resources,skills, or time to deploy defensive processes and systems.

The detection problem becomes exponentially harder when LLMs cangenerate code that passes all existing security reviews,contribution histories that look perfectly normal, and socialinteractions that feel authentically human. Traditional codeanalysis tools will struggle against LLM generated backdoorsdesigned specifically to evade detection. Meanwhile, the humanintuition that spot social engineering attacks becomes useless whenthe "humans" are actually sophisticated language models.