We ran multi-agent LLM pipelines against historical exploit corpora and live audit engagements. The results reshape where AI fits in a security review — and where it absolutely doesn't.
An agent holding your raw key is one prompt injection from total loss — and 97% of early EIP-7702 delegations went to drainer sweepers. We read the sweeper's source off the chain, dissect a real 35.97-USDC-a-day spend permission on Base, and do the blast-radius math.
Decentralized RL splits the actor from the learner across the internet: the policy a worker acts with runs several steps behind the one that learns. INTELLECT-2 held reward at 4 steps stale; SparrowRL cut the broadcast 79x. The bandwidth and staleness taxes, and what the chain secures.
On-chain AI loves to say a model is 'stored on-chain.' But a chain commits to a 32-byte hash for cents; keeping the 140 GB it points to retrievable is a separate, recurring, surprisingly centralized bill. The storage math, erasure coding, and the retrieval wall.