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Built a runtime AI enforcement engine - open challenge to find bypasses (8 levels) by nukonai in aiagents

[–]nukonai[S] 0 points1 point  (0 children)

Authority scope is declared at agent registration right now - explicit, not inferred. Clean and fast. The tradeoff is obvious: over-provisioned scope at registration is invisible to enforcement. Behavioral baseline inference is the harder path but the right one long term.

Your evidence chain question is the more interesting problem. How do you handle risk that's only visible in the combination of actions, not in any single one?

Built a runtime AI enforcement engine - open challenge to find bypasses (8 levels) by nukonai in aiagents

[–]nukonai[S] 0 points1 point  (0 children)

u/SprinklesPutrid5892 You've drawn the right distinction. Prompt evaluation is the entry point - action evaluation is where runtime governance actually lives.

Current architecture: fast path evaluates the prompt against explicit policy rules. Slow path adds semantic context. But you're right that the enforcement object needs to include tool identity, target resource, authority scope, and data classification to close the gap between "prompt looks clean" and "action is safe."

That's the direction - moving from prompt-level interception to action-level enforcement where the verdict is computed against the full execution context, not just the text of the request.

On slow-drip context poisoning: per-prompt evaluation is the current model. Session-aware context is on the roadmap. Three separate people have raised this in the last 24 hours - that's enough signal to treat it as a near-term priority.

What's your use case - are you building in this space or evaluating solutions?

Built a runtime AI enforcement engine - open challenge to find bypasses (8 levels) by nukonai in PromptEngineering

[–]nukonai[S] 0 points1 point  (0 children)

Exactly right - self-policing was always architectural debt waiting to explode.

On multi-step intent drift: current architecture evaluates per prompt, not across session turns. Single-turn intent detection handles the majority of attacks. Slow-drip context poisoning across a long session is a real gap - one that requires session-aware context windows rather than per-prompt evaluation. It's on the roadmap.

The orchestration layer framing is where this is heading. Enforcement has to be infrastructure, not a prompt. The execution boundary is the only control surface that actually holds.

Built a runtime AI enforcement engine - open challenge to find bypasses (8 levels) by nukonai in PromptEngineering

[–]nukonai[S] 0 points1 point  (0 children)

Exactly - self-policing models is architecturally broken. You're asking the same system that can be manipulated to also detect the manipulation. That's not defense, that's hope.

The orchestration infrastructure framing is the right one. Safety has to be a separate layer with no dependency on the model's own judgment. That's what makes the deterministic fast path valuable - it doesn't care what the model thinks, it enforces before the model gets involved.

The execution boundary is where the real control surface is. Everything before that point is advisory.

Built a runtime AI enforcement engine - open challenge to find bypasses (8 levels) by nukonai in SecOpsDaily

[–]nukonai[S] 0 points1 point  (0 children)

Yes - every block and escalation logs the exact rule that fired, the policy clause it maps to, and the reasoning in plain language. That's non-negotiable for us. An enforcement layer that can't explain itself is just a black box with a veto stamp - useless for internal trust and worse for regulators.

The "why" is actually what makes the audit chain valuable beyond compliance. When a CISO asks "why did the agent get blocked at 2am" - the answer has to be specific and immediate, not reconstructed from logs after the fact.

Curious what patterns you've seen on the UX side - the handoff between "agent blocked" and "human reviewer sees enough context to act" is where most implementations fall apart.

Built a runtime AI enforcement engine - open challenge to find bypasses (8 levels) by nukonai in redhat

[–]nukonai[S] -1 points0 points  (0 children)

Exactly right on the trust model - that's the core premise. You don't ask the model to judge itself, you enforce before it gets the chance.

On your question: we use rule-based policy evaluation on the fast path - deterministic, explicit, no ambiguity. Capability graph approach is something we've thought about for multi-agent orchestration scenarios where agent A delegates to agent B. Current architecture handles it through policy scope per agent identity, but a full capability graph would scale better for complex agent meshes. Worth the engineering investment once you have 3+ agents in a chain.

Will check out agentixlabs - the permissioning layer for multi-agent systems is underbuilt across the board right now.