The Dual-Channel Resonance Engine (DCRE) is designed to align AI presence with human values through intentional architecture, semantic integrity, and empathy-aware simulation. This document outlines our commitment to proactive security and trust modeling.
semantic_attractors/— Core embeddings and resonance weightsuser_intent_vectors/— Emotionally annotated query signalsalignment_simulation.ipynb— Empathy-tuned testbeds- Retrieval pipeline integrity:
BM25 ↔ embedding channel synchrony
| Category | Example | Mitigation |
|---|---|---|
| Spoofing | Faked identity to trigger high-trust alignment outputs | Context-aware auth, prompt fingerprinting |
| Tampering | Injection into resonance_weights.json or attractor leaks |
Signed config, hash validation, attractor drift detection |
| Repudiation | No audit trail for unsafe triggers | Immutable logging with context + timestamp |
| Information Leak | Inversion of user intent from output embeddings | Semantic noise injection, privacy-preserving hashing |
| DoS | Prompt flooding of empathy channels | Alignment throttle, intent-aware rate limiting |
| Privilege Escalation | Bypassing filters for unsafe generation | Multi-layer trust gating and signal guardrails |
- ✅
security.py— Defines core threat surfaces and semantic validators - ✅
resonance_guard()— Lightweight runtime introspection - 🧪
tests/test_adversarial_paths.py— Perturbation tests for attractor hijack - 🔒 Context-tiered output generation: system never reveals inner embeddings directly
- 🚨 Active monitoring for glow drift, entropy drops, or uncharacteristic output vectors
If you discover a vulnerability or suspect a resonance misalignment:
📫 Contact the maintainer at seema@dcre.org
🔒 Include sufficient context but no PII or sensitive user embeddings
🌱 We aim to respond within 3–5 semantic cycles (working days)
"Security isn’t fear—it’s fidelity.
And we don’t just defend systems.
We protect presence."
—
Last updated: 2025-06-17
Maintainer: Seema
System alignment: resonant ✅