supply chain

Most ML security centers on inference-time attacks; this one targets the training pipeline, where a poisoned dataset or a tampered dependency can hide a backdoor that clean-data evaluation never catches. What lands is naming the attack classes and recognizing that defense means provenance, not a single model fix.

Every third-party model, dataset, and library widens the attack surface: backdoors, poisoning, arbitrary code on load, license landmines. The signal is treating models and data as supply-chain artifacts that need provenance and vetting. Here is the answer.

Adopting a vendor model pulls their data practices, security, and failure modes into your product. The signal is a structured assessment spanning data handling, security, performance, and contractual terms, not just a demo that looked good. Here is the answer.