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Authorization engine for AI agents: allow, deny, route to a human, or reroute safely — catching prompt injection too.
By: Lelu-ai
Submitted on: Jun 19, 2026
The problem: Giving AI agents real tool access (delete records, move money, send messages, deploy code) means trusting them with a blank check. Traditional authorization — OPA, Casbin, AWS Verified Permissions, Okta — only answers "is this agent allowed to do X?" They can't catch the dangerous case: a legitimately authorized agent that's been manipulated into doing the wrong thing — through prompt injection in the data it's processing, a low-confidence/hallucinated decision, or anomalous behavior. "Authorized but wrong" is exactly the failure mode that makes agent autonomy risky in production. How Lelu solves it: Every agent action flows through one authorization call that returns one of four outcomes instead of just allow/deny: allow — proceed deny — blocked human_review — pause, a human approves, the agent resumes compute — reroute to a safer/sandboxed alternative Behind that call is a layered pipeline: a prompt-injection filter → a confidence gate (using verified LLM token log-probs where available, failing closed when they aren't) → policy evaluation (YAML or OPA/Rego) → a risk score (criticality × (1 − confidence) × reliability × anomaly_factor) → a most-restrictive-wins merge so any single layer can only make a decision stricter, never looser → an audit log of every decision. Around it sits agent identity (RS256 workload JWTs / OIDC), an MCP OAuth 2.1 server, and an encrypted OAuth token vault. The result: every action is checked, every decision is logged, and a human is pulled in exactly when it matters — without changing how you build the agent (one SDK call).
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Ismael Kedir (Ismael KD)
This is very interesting and timely! Starring this.
Abenezer
MakerThank you Ismael for your comment
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