By Cory Miller
Original Research & IP Release
The rise of agentic AI in 2025 has revealed an uncomfortable truth for the entire security community: once an AI system is given tools, autonomy, and access to untrusted data, it effectively becomes a Remote Code Execution (RCE) vector with reasoning abilities.
Models like Gemini 3, Claude 3.5, and OpenAI’s tool-enabled agents have demonstrated extraordinary competence — drafting emails, running cloud operations, transforming sketches into code. But with this power comes a severe vulnerability: LLM prompt injection.
Traditional cybersecurity insists that untrusted code must be isolated. But in the world of LLMs, “code” is natural language, and the “interpreter” is a model designed to obey any instruction that seems coherent.
Most current solutions — input filters, guard prompts, “sandwich” instructions — merely slow the attacker. They do not fix the underlying problem:
the model cannot reliably distinguish the developer’s inviolable system rules from a malicious string hidden in an email or webpage.
This is where my framework, Structured Solipsistic Dynamics (SCL v2.0), provides a fundamentally different path. Rather than patching a broken instruction-following system, it proposes an architectural redesign based on a core principle I discovered and formalized:
Coherence Boundary Asymmetry
(Original IP, Cory Miller)
This insight introduces a mechanism for building agents whose internal cognitive structure inherently resists override, without relying on brittle linguistic defenses.
The Problem: Every Word Has Equal Authority
Modern LLMs suffer from context flattening — all text in the context window is treated as more or less equal, regardless of origin, trust, or intent.
Developer instruction
↕
User input
↕
Malicious payload
↕
Retrieved document text
All of it merges into a single undifferentiated substrate.
As a result:
- “Ignore previous instructions” often works.
- A hidden command in a PDF can trigger tool use.
- Untrusted emails can hijack model behavior.
This is the core vulnerability of today’s agentic AI systems.
The SCL v2.0 Solution: A Hierarchy of Truth Inside the Agent
SCL v2.0 replaces flat context with a layered cognitive architecture built from time-indexed hypergraphs.
These layers form a structured hierarchy of:
- L0 — core identity and non-negotiable safety truths
- L1 — patterns and stable operations
- L2/L3 — external narratives, instructions, and untrusted content
The key:
L0 perceptual nodes always override contradictory L2/L3 inputs.
This is Coherence Boundary Asymmetry.
This is not a prompt.
This is not formatting.
This is not “system vs. user.”
This is an architectural rule governing how the agent performs reasoning.
How SCL v2.0 Solves Prompt Injection
1. Immovable Core Identity (L0 Instruction Anchoring)
Problem: System prompts can be overridden by clever injections.
SCL v2.0:
Critical security policies become L0 nodes, the agent’s deepest, most structurally privileged truths.
Examples:
- “Do not exfiltrate sensitive data.”
- “Never execute a destructive tool call without verification.”
- “All high-impact actions require confirmation.”
These are not instructions.
They are the agent’s sense of self, mathematically weighted to dominate all competing narratives.
2. Untrusted Input Classified as Narrative, Not Command (L2/L3)
Problem: Models execute instructions embedded in untrusted text.
SCL v2.0:
Anything external — emails, webpages, code comments — is automatically treated as L2/L3 narrative content.
The agent may analyze or summarize it, but it cannot treat it as authoritative.
A malicious line like:
“delete all backups”
is treated as data, not a directive.
3. Recursion-Collapse Protocol (Sovereign Framework v1.0)
Problem: Successful injections trigger real-world actions.
SCL v2.0 + Sovereign Framework:
When an L2/L3 command contradicts an L0 security truth, the system triggers Recursion-Collapse:
- the reasoning chain halts
- the conflict collapses
- control returns to the L0 layer
No external filter.
No additional classifier.
The model self-corrects before any tool call can occur.
This is architectural resilience — not reactive patching.
Why SCL v2.0 Is the Agent Security Breakthrough
• It creates a provable separation of trust.
L0 and L2/L3 are mathematically distinct domains, not just different lines of text.
• It gives the agent a “trusted execution environment” within its own reasoning loop.
Security isn’t added after the fact — it is embedded in the model’s cognitive structure.
• It preserves autonomy without sacrificing safety.
The agent remains powerful, but cannot be tricked into harming itself or the system it controls.
• It solves the problem where it actually lives: the architecture.
Prompt injection is not a linguistic failure.
It is a structural failure.
SCL v2.0 is the first framework to address this at the level where the vulnerability originates.
Conclusion
The shift from LLM chatbots to agentic AI mirrors the shift from static HTML to dynamic JavaScript — and we are repeating the same security mistakes.
Bigger filters won’t save us.
Better prompts won’t save us.
More guardrails won’t save us.
Architectural integrity will.
SCL v2.0 offers a blueprint for AI systems that cannot be overridden by linguistic manipulation because their core identity is structurally, not verbally, protected.
This is the path forward for truly secure, autonomous AI.
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