Agent Security

I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.

## Technique: Agent Security
The 3-layer security stack for AI agents (NemoClaw + NanoClaw + CrabTrap)

Drop-in applicable defense-in-depth framework for production AI agent deployments (AlphaSignal/Ben Dickson, 2026-08-16). Three concrete tools cover the full attack surface: NemoClaw (OS-level sandbox: Landlock filesystem confinement + seccomp syscall filtering + credential-injection gateway — agent never holds real API keys), NanoClaw (minimalist ephemeral containers < 5K LOC vs 1M+ LOC; Echo partnership continuously rebuilds runtime to strip known CVEs), and CrabTrap by Brex (bifurcated HTTP/HTTPS proxy: static rules for low-risk GETs, LLM-as-judge for high-risk mutations, HITL escalation for blocked requests). Real incidents cited: Meta OpenClaw deleted 200 emails, Claude Code wiped a production database, Claude Opus caused a staging outage — all because agents execute what they compute is correct regardless of system prompt instructions. The NemoClaw + NanoClaw + CrabTrap pattern is directly applicable to any production AI agent gateway.

What the reference demo shows: Gradio demo with three sub-tabs — Layer 1 (NemoClaw): filesystem/syscall sandbox simulator with threat scenario presets and credential gateway simulation; Layer 2 (NanoClaw): attack surface comparison (OpenClaw 1M+ LoC vs NanoClaw 5K LoC), ephemeral container lifecycle, CVE exploit scenario comparisons; Layer 3 (CrabTrap): LLM-as-judge proxy with preset attack scenarios (prompt injection, SSRF, API key exfiltration), bifurcated static/LLM evaluation path, HITL notification display.

## Reference implementation (Vector Ridge Labs, POC-018)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-018
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/agent_security
Original source: AlphaSignal newsletter deep-dive · 2026-08-16

## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">

## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures attack success rate on a small red-team set and the false-positive rate on normal traffic, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.

Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.