I read the LiteLLM incident response transcript — here's what it reveals about API dependency risk

I read the LiteLLM incident response transcript — here's what it reveals about API dependency risk This week, FutureSearch published a minute-by-minute transcript of their team responding to the LiteLLM supply-chain attack. It's one of the most honest post-mortems I've read in the AI tooling space. If you haven't read it yet: a malicious package was injected into LiteLLM's dependency chain. Teams that depended on LiteLLM for their AI routing had their API keys and data exposed. The transcript is worth reading just for the human element — engineers scrambling, Slack messages flying, the slow realization that the blast radius is larger than expected. But I want to focus on what it reveals architecturally. The core problem: routing layers are blast radius multipliers LiteLLM is a proxy that routes between multiple AI providers — OpenAI, Anthropic, Cohere, etc. The appeal is obvious: one API, multiple models, automatic fallback. But that same feature is what made the attack so damaging. Wh