By skyforbes I’ve been working on a defensive architecture that sits above prompt-level techniques — more of a “behavioral hardening layer” for AI systems.
I’m calling it SPIP v3.0 (Silent Prompt Injection Protocol). It’s designed to reduce surface area for jailbreaks, stabilize outputs across LLMs, and enforce consistent reasoning even when the underlying model drifts.
The core principles:
• Behavioral gating instead of keyword filtering
• Reasoning-path constraints
• Context-preservation frames
• Cross-LLM consistency scaffolding
• Built-in rollback and correction patterns
• Anti-mirroring and anti-reversal defenses
• Topology-based injection suppression (not regex)
It’s essentially a system-layer wrapper: the model never sees the whole structure at once, and violations trigger controlled resets.
I’m not here to promote anything — just trying to validate whether this kind of architecture is useful to builders who work with multi-agent systems, GPTs, or custom pipelines.
If there’s interest, I can share a diagram of the layer topology or walk through one of the guardrail mechanisms.
Curious what this community thinks:
• Does a behavioral-layer approach make sense?
• Where would you improve or stress-test it?
• Any blind spots I should be aware of?
Happy to contribute back anything useful.