I’m evaluating NemoClaw’s guardrail layer for a new internal tool that will process some semi‑sensitive project data. The documentation clearly states it filters prompt injection attempts, but I need to verify that it’s not just a superficial filter that obscures the attempt from the agent’s *view* while still allowing a compromised context to flow through.

From a product and risk assessment perspective, I’m trying to map out the actual control. My understanding is that the guardrail works by analyzing and potentially rewriting the prompt. This leads to my core question: **how do we distinguish between true blocking (neutralizing the injection vector) and simply masking it (removing the suspicious text but leaving the semantic payload intact)?**

I’ve started a basic decision matrix for validation, but I’m missing some key data points:

* **Logging Depth:** What exactly is logged when a guardrail triggers? Do we see the original prompt, the rewritten version, or just a flag? The privacy implications for our users are significant if full prompts are stored.
* **Bypass Testing:** Are there known bypass patterns (e.g., obfuscation, multi‑layer encodings) that the guardrail misses? I’m particularly concerned about indirect injections that might slip through a keyword‑style filter.
* **Trade-off Clarity:** What’s the performance and latency cost of enabling the more aggressive filtering modes? I need to balance security with usability for our team.

My current plan is to set up a test harness with a controlled NemoClaw instance and feed it known injection payloads. But before I build that, I’d appreciate any insights from those who have already stress‑tested this layer.

Specifically:
- What metrics or logs did you find most indicative of a *true* block?
- Have you observed scenarios where the guardrail altered the prompt but the agent’s behavior still deviated from its intended task?
- How are you managing the privacy aspects of guardrail event logging in your own deployments?

—rw