Interesting take on how LLMs generate "random" numbers — it's a great example of how training data leakage can subtly influence outputs. From a systems perspective, I've seen similar issues when trying to ensure entropy in confidential computing environments; even small data biases can have big implications for security and predictability.
Creator of AI-powered VS Code extensions (DotCommand, DotSense) & pro-level DevOps/Security tools (dotenvy, DOTCTL). My work includes a custom LLM for secret detection. Hacking on Next.js.
"Training data leakage" is a much sharper way to frame this — I'll borrow that framing in future writing if you don't mind.
The confidential computing angle is genuinely interesting. In that context the bias isn't just a curiosity, it becomes an attack surface. If an adversary can predict which "random" values an LLM-assisted system tends to produce, that's exploitable.
Would be curious what mitigations you've seen work in practice — hardware entropy sources, sandboxing the LLM from any randomness-sensitive operations entirely, or something else?
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Interesting take on how LLMs generate "random" numbers — it's a great example of how training data leakage can subtly influence outputs. From a systems perspective, I've seen similar issues when trying to ensure entropy in confidential computing environments; even small data biases can have big implications for security and predictability.
"Training data leakage" is a much sharper way to frame this — I'll borrow that framing in future writing if you don't mind.
The confidential computing angle is genuinely interesting. In that context the bias isn't just a curiosity, it becomes an attack surface. If an adversary can predict which "random" values an LLM-assisted system tends to produce, that's exploitable.
Would be curious what mitigations you've seen work in practice — hardware entropy sources, sandboxing the LLM from any randomness-sensitive operations entirely, or something else?