Home▸Cybersecurity▸Model Integrity Verification: Weight-Checksum Tamper Detector (2D)

Model Integrity Verification: Weight-Checksum Tamper Detector (2D)

Interactive 2D MLOps security lab: a signed neural network's flat weight array is attacked by a live supply-chain tampering process, while a block-level rolling checksum catches, quarantines and rolls back the corrupted weights. Pan and zoom the network diagram, tune attack rate, block size and verification interval.

Cybersecurity2DModerate60 FPS📱 Mobile-adapted⇄ 3D version
2d-exp-smart-algorithms-security ↗ Open standalone

This 2D companion draws the same small feed-forward network as the 3D lab as a flat, pannable node-and-edge diagram, and runs the identical supply-chain-attack-versus-checksum defense: a random weight gets silently substituted at a configurable rate, and a rolling polynomial hash computed over fixed-size weight blocks periodically re-verifies every block against its signed digest, flags any mismatch, rolls the block back to its trusted values, and logs the detection latency. Turn signing off and the same attacks keep happening but nothing catches them, making the "silent corruption" blind spot of an unsigned MLOps pipeline directly visible. Drag to pan the diagram and scroll to zoom in on individual blocks.

⚙ Under the hood

Interactive 2D MLOps security lab: a signed neural network's flat weight array is attacked by a live supply-chain tampering process, while a block-level rolling checksum catches, quarantines and rolls back the corrupted weights. Pan and zoom the network diagram, and tune the attack rate, checksum block size and verification interval to see the detection-latency trade-off.

ai-securitymlopsmodel-integritychecksumsupply-chaintamper-detection

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install

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