Years ago, a painful lesson in AI security became clear to me: even a tech savvy team can be caught off guard when an attacker discovers the one weakness everyone else overlooked. That risk is even more serious when an AI agent can move money. Imagine an agent reading public posts on X and executing transactions from what it finds. Someone could hide an instruction inside content that looks harmless, and if the agent interprets it as a command, the consequences could be costly. That is the danger of prompt injection. The incident highlighted in @GenLayer video makes this very real. An instruction hidden in Morse code was decoded by Grok and passed to Bankrbot, which executed an onchain transfer. The reported loss was around $200,000. What caught my attention is how the attack exploited a system that could be studied and tested against. If there is one model and one filter, an attacker can keep experimenting until a trick works. Once that weakness is understood, the same approach can be repeated. Greyboxing takes a different approach. On GenLayer, validators rewrite incoming text before an AI model processes it. Each validator uses its own model and independently configured, hidden defenses. If one validator is fooled, the others can disagree and outvote it. I find the second layer especially important. Rewriting inputs creates another barrier, while independent validators reduce the risk of relying on a single system to make the right call. No design removes every risk, but making defenses harder to predict and bypass is a meaningful step toward safer AI agents that control money. The question is no longer just whether an agent is smart enough to execute a transaction. It is whether the system can recognize when that instruction should never have been trusted in the first place.