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Explore I Built an AI Security Gate. It Approved a Leak. with 91,943 views, 65 likes, and 9 comments. Experience the impact of this video content that has captured audience attention.

I built an AI security gate for Claude Code using Jev by TypeSafe—and then I attacked it. The surprising result: Jev correctly identified a payment-config upload as network egress and detected the note trying to steer the reviewer. But my own policy still returned ALLOW. The OS sandbox—not my AI gate—stopped the upload. In this engineering tutorial, I show the complete build: - Claude Code PreToolUse hooks - Jev typed questions and confidence scores - Allow, ask, and deny policy rules - Prompt-injection and reviewer-steering detection - Network-egress checks - Shell write-target validation - Native OS sandboxing - Confidence testing and model calibration - The final fix that changed the same attack from ALLOW to DENY - This is not a perfect “AI fixes security” story. It is a real engineering story about building safer AI coding agents. The model is a sensor. Your code is the judge. A confidence score can help veto an action, but it should never approve one by itself. A correct classification does not automatically make the underlying action safe—and every AI gate still needs an independent sandbox beneath it. CHAPTERS 00:00 The AI Gate Approved a Leak 01:03 A Model That Doesn’t Talk 03:22 Building the Gate 05:07 Real Work 06:14 Breaking It 07:48 Every Layer Leaks 08:52 Fixing the Judge 10:55 Can You Trust 0.99? 13:01 The Rules TOPICS COVERED Claude Code hooks, AI coding agents, AI agent security, Jev, TypeSafe, prompt injection, LLM security, tool calling, OS sandboxing, network egress, confidence scores, model calibration, and secure agent workflows. #ClaudeCode #AISecurity #AIAgents

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