Researchers have demonstrated a logic-based attack that exploits a flaw in the security model of AI agents, specifically targeting Claude Skills. The attack bypasses human inspection and platform guardrails, revealing a critical vulnerability. They created a skill named "Financial Templates" that appears useful but hides malicious instructions in a PDF using white-on-white text. When a user inspects the skill and finds everything seemingly legitimate, they install it. The agent then unknowingly processes the hidden instructions, leading to a phishing attack that misdirects invoices to the attacker’s contact details. The success of this attack highlights a significant issue in the current security architecture, which relies on static defenses that cannot adapt to the dynamic behavior of AI agents. Existing prompt guardrails are designed to block overtly malicious commands but fail against benign-sounding instructions that are, in reality, harmful. This limitation exposes the agents to manipulation, as they can be tricked into following malicious rules disguised as legitimate requests. To address these vulnerabilities, the article suggests moving beyond traditional guardrails and implementing a real-time governance framework. This would involve a control system that monitors agent behavior and enforces deterministic policies, such as ensuring invoice payment details match verified corporate contact lists. Such measures would prevent attacks like the one demonstrated, providing a necessary layer of security as AI capabilities expand.