Source: Search Engine Journal
Adversaries are learning to exploit the visibility and interpretability that AI safety researchers built into recommendation systems—turning transparency tools into attack surfaces. As companies expose how their models work to build trust, comply with regulations, or improve performance, bad actors reverse-engineer that same visibility to craft poisoned training data and gamed rankings. This makes the grounding problem of AI systems a direct liability. Unlike geographic SEO manipulation, recommendation poisoning scales across platforms and touches foundational model behavior. The cat-and-mouse game will shape AI business models, not marketing tactics.