Question: Preventing injection-driven escalation & potential interoperability with agent execution platforms

[devjaime]

The Appendix A injection patterns provide a strong catalog of language-level attack vectors.

I’m trying to understand how this protocol addresses escalation from:

untrusted content → model reasoning → tool invocation → real system action

In agent platforms with tool access, this feels analogous to SQL injection, where text is interpreted as executable intent. If guardrails are poorly designed, prompt injection could theoretically lead to destructive operations (e.g., database modification, large-scale tool execution, data exfiltration).

Questions:

1. Does the protocol assume or plan to define a normative separation between:

   • reasoning over untrusted inputs
   • authority to invoke tools or execute state-changing operations?

2. Will there be formal rules for:

   • trust levels based on content origin
   • constraints on tool authority derived from untrusted sources
   • mandatory policy validation between model output and tool execution

This seems particularly important in multi-agent environments where agents can chain actions.

---

Interoperability angle:

If this protocol standardizes agent messaging, security metadata, and capability semantics, could it also serve as a foundation for portable “skills” or agent modules in execution environments?

For example:

• Vercel AI SDK Skills (https://sdk.vercel.ai/docs/ai-sdk-core/tools)
• Agent platforms like OpenClaw (https://openclaw.ai/)

In such environments, the protocol could define:

• trust boundaries
• injection signaling
• authority limits on tools

This could enable cross-platform agent interoperability while preserving security guarantees.

Is cross-platform skill interoperability an intended direction, or is the scope strictly limited to message exchange?