1. Executive Summary
A brief overview of what EchoLeak is, why it’s dangerous, and what structural gaps make it possible.
2. The Vulnerability Landscape
- What is Prompt Injection?
- Evolution from Click-Based to Zero-Click Attacks
- How EchoLeak operates in real-world scenarios
3. Root Cause Analysis
- Trust assumptions in prompt-driven architectures
- LLM behavior over unverified context inputs
- Why current mitigation strategies (filtering, fine-tuning) fail
4. Case Studies
- GitHub issues triggering Copilot hallucinations
- Email/metadata poisoning in Claude/OpenAI API agents
- The failure of sandboxing when LLMs are autonomous
5. Structural Solutions: From Prompt to Protocol
- Why prompt filtering ≠ secure alignment
- The need for context validation & input integrity
- Proposal: Contract-style input wrappers, with verifiable fields
- Behavioral gating by protocol envelope (not natural language inference)
6. A Protocol-Oriented Architecture
- Structural definition of “trusted input”
- Layered execution permission system
- Interface-level protocol gatekeeping
- Optional “signature_hash” validation (abstractly defined)
7. Compatibility with Existing LLM Ecosystems
- Why this doesn’t require retraining models
- Can be deployed as a proxy/filter layer
- Works with OpenAI, Claude, and even RAG pipelines
8. Strategic Implications for Vendors
- Why EchoLeak will persist unless architectures change
- Regulatory, reputational, and safety costs
- A call for alignment-standardization: time for protocols, not prompts