Single-Artifact Scam Detection: A Failure Mode Analysis Threat Model, Failure Taxonomy, and Competitive Positioning for Screenshot-Based Consumer AI

Screenshot-based AI scam detectors represent a growing category of consumer protection tools. This technical note documents the structural limitations of single-artifact scam detection through three complementary lenses: a ten-scenario failure mode taxonomy, a STRIDE-adapted threat model across five attack surfaces, and a competitive comparison against adjacent detection technologies. The analysis demonstrates that screenshot-based detection is architecturally insufficient for the threat class it claims to address—not because of implementation weakness, but because single-artifact analysis cannot govern relationship-level, multi-stage, or multi-channel adversarial behavior. The note also identifies a critical adversarial feedback loop: scammers can use detection tools to test and refine scam templates until they clear, converting the protection tool into a quality assurance service for the adversary.