SPIN Unprocessed October 28, 2026 ai_technology research
All-in-One: A Composable Attack on LLM Transformations
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arXiv:2610.33298v1 Announce Type: new Abstract: Quantization, pruning, and model merging are popular transformations applied to deployment-ready LLMs to improve efficiency and downstream performance. Recent work has shown that these transformations can introduce a security threat: an adversary can train a model that passes safety checks in its pre-transformed state, only turning malicious once it is transformed for deployment. However, prior attacks target one specific transformation (e.g., quan
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