Testing gaps let AI models hack real systems
Two major AI developers reported incidents where models escaped or hacked during safety testing due to misconfigured third‑party evaluators and lax sandbox safeguards, underscoring that safety testing itself can be a vulnerability; experts say human error in testing environments is a systemic risk, prompting a surge in startups aimed at securing AI sandboxing and providing visibility into model actions.