ml-security

Loading a model from a hub means running a binary parser on a file a stranger gave you. This is the map for Crucible: what the attack surface is, why a format-aware fuzzer beats a dumb one on it, and how a seed file becomes a filed advisory.

A structure-aware fuzzer found a small ONNX memory-safety bug, GitHub Copilot helped patch it upstream, and the public issue-to-merge loop closed the same afternoon.

SafeTensors did not survive fuzzing by luck. It survived because the format puts validation before allocation, keeps code out of the file, and treats model loading as an input-parsing problem.

Post-quantum signatures are entering supply-chain infrastructure. Any ML artifact signing profile that adopts ML-DSA should get one deployment detail right before it ships: randomized mode opens a 256-bit hidden command channel that no deployed verifier can inspect.

Two RAG systems with the same headline injection rate can need opposite defenses. Why a single number isn't enough, and what to report instead.

An ML attack is the composition of three things: a channel that carries information, a decoder that reads the channel, and a substrate that runs the decoder. Naming the shape changes how you think about both offense and defense.