The adversary targets terrestrial space-domain awareness pipelines, sensor networks, tracking centers, catalogs, and their data flows, to blind or confuse broad-area monitoring. Paths include compromising or spoofing observational feeds (radar/optical returns, TLE updates, ephemeris exchanges), injecting falsified or time-shifted tracks, tampering with fusion/association parameters, and saturating ingestion and alerting with noisy or adversarial inputs. Where SDA employs AI/ML for detection and correlation, the attacker can degrade models by flooding them with ambiguous scenes or crafted features that increase false positives/negatives and consume analyst cycles. Unlike onboard deception, this approach skews the external decision-support picture across many assets at once, delaying detection of real maneuvers and providing cover for concurrent operations.