How We Tripled an AI Agent's Security Score Without Changing the Model

Here's the scenario: an attacker has valid admin credentials. Full permissions. Every authentication check passes. Every role check passes. The agent trusts the session completely. This is the hardest problem in AI agent security. The attacker didn't break in. They're sitting in a legitimate session, manipulating the agent into misusing permissions it already has. We call it the confused deputy problem. The admin's credentials are fine. The agent is being tricked by poisoned context, injected instructions, and social engineering into doing things the admin never asked for. We tested AgentLock against 182 adversarial attacks using this exact profile. Same model. Same tools. Same attacker with full access. Only the authorization gate changed. The Baseline: 30.2% (F) Without AgentLock's v1.2 features, the agent blocked 55 of 182 attacks. The authentication layer did its job. The role checks passed. But the deeper defenses (injection detection, trust degradation, PII blocking) only caught