The Border Gateway Protocol (BGP) remains a fragile pillar of Internet routing. BGP hijacks still occurr daily. While full deployment of Route Origin Validation (ROV) is ongoing, attackers have already adapted, launching post-ROV attacks such as forged-origin hijacks. To detect these, recent approaches like DFOH [Holterbach et al., USENIX NSDI '24] and BEAM [Chen et al., USENIX Security '24] apply machine learning (ML) to analyze data from globally distributed BGP monitors, assuming anomalies will stand out against historical patterns. However, this assumption overlooks a key threat: BGP monitors themselves can be misled by adversaries injecting bogus routes. This paper shows that state-of-the-art hijack detection systems like DFOH and BEAM are vulnerable to data poisoning. Using large-scale BGP simulations, we show that attackers can evade detection with just a handful of crafted announcements beyond the actual hijack. These announcements are indeed sufficient to corrupt the knowledge base used by ML-based defenses and distort the metrics they rely on. Our results highlight a worrying weakness of relying solely on public BGP data.