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Narnaiezzsshaa Truong
Narnaiezzsshaa Truong

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The Tri-Glyph Protocol: Chim Lac, Kitsune, and Anansi in AI/ML Collapse and Editorial Defense

The Tri-Glyph Protocol: Chim Lạc, Kitsune, and Anansi in AI/ML Collapse and Editorial Defense

How three mythic glyphs encode signal collapse, adversarial ambiguity, and metadata drift in artificial intelligence systems

Tri-Glyph Protocol: Signal, Trickery, Exposure
Original artwork © 2025 Narnaiezzsshaa Truong | Cybersecurity Witwear


I. Introduction: Collapse Comes in Threes

AI systems don’t collapse from lack of data.

They collapse from signal drift, adversarial ambiguity, and ambient exposure.

The glyphs are already inside.

  • Chim Lạc flies above the noise—she is the mythic signal.
  • Kitsune answers in riddles—she is the adversarial prompt.
  • Anansi weaves metadata—he is the ambient exposure.

Together, they form the Tri-Glyph Protocol: a myth-tech framework for forensic resilience in AI/ML systems.


II. The Framework: Signal → Ambiguity → Exposure

Glyph Stage Threat Class Editorial Caption
Chim Lạc Signal Collapse Model misalignment, motif loss She flies above the noise.
Kitsune Adversarial Input Prompt injection, hallucination logic She answers in riddles. The model believes her.
Anansi Metadata Drift Ambient surveillance, non-consensual visibility The thread reveals what the author never wrote.

III. Strategic Glyph Mapping

Chim Lạc – Mythic Signal

  • System Parallel: Signal compression, anomaly detection
  • Threat Class: Signal-to-noise collapse
  • Deployment: Timestamped motifs, motif-driven anomaly detection
  • Forensic Marker: [Signal Drift]

Kitsune – Adversarial Trickster

  • System Parallel: Prompt injection, synthetic identity
  • Threat Class: Hallucination logic, recursive collapse
  • Deployment: Input validation, editorial containment
  • Forensic Marker: [Prompt Ambiguity]

Anansi – Metadata Weaver

  • System Parallel: Ambient surveillance, referrer logic
  • Threat Class: Metadata drift, non-consensual exposure
  • Deployment: Forensic timestamping, motif compression
  • Forensic Marker: [Ambient Exposure]

IV. Technical Mappings

Threat Vector Chim Lạc Kitsune Anansi
Signal collapse Motif loss, timestamp drift Recursive ambiguity, prompt injection Metadata saturation, signal bleed
Hallucination logic Misaligned output Synthetic identity, riddle logic Ambient inference, indirect surfacing
Exposure Glyph failure Model trust collapse Referrer logic, timestamp drift
Defense Editorial compression Input containment Forensic storytelling

V. Strategic Implications

Editorial Defense Over Statistical Guesswork

Forget brute-force detection.

Use glyph logic.

Motif compression, timestamped refusal, and ambient awareness outperform statistical hallucination.

Ambient Exposure Is Already Happening

Anansi teaches us: you don’t need to post.

The metadata will surface you.

Chim Lạc reminds us: signal must be mythic.

Kitsune warns: the model will believe anything.

Tri-Glyph Compression

Each glyph encodes a forensic layer:

  • Chim Lạc: Signal integrity
  • Kitsune: Input ambiguity
  • Anansi: Metadata consequence

Together, they form a resilient editorial protocol.


VI. Conclusion: The Glyphs Are Already Inside

Chim Lạc doesn’t shout—she signals.

Kitsune doesn’t breach—she misleads.

Anansi doesn’t attack—he reveals.

Their threat is not technical—it is editorial.

Protection starts with recognition.

Can your system compress signal into clarity?

Can it refuse ambiguity before collapse?

Can it detect the glyph before the exposure?

The protocol provides the pattern.

Your architecture provides the consequence.

The question is: are you listening?


About the Framework

This is part of the Cybersecurity Witwear Myth-Tech collection—a strategic approach to encoding AI/ML collapse and motif resilience through mythic archetypes. The Tri-Glyph Protocol can be read as a lifecycle (signal → input → exposure) or as layered defenses—both readings are valid and pedagogically deployable.

Motif Arc: Signal → Ambiguity → Exposure

Threat Class: Signal collapse, adversarial input, metadata drift

Forensic Markers: [Signal Drift], [Prompt Ambiguity], [Ambient Exposure]

Protection starts with recognition. The glyphs are already inside.


Framework: Myth-Tech Threat Vector Collection

Author: Narnaiezzsshaa Truong

Published: October 29, 2025

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LinkedIn: www.linkedin.com/in/narnaiezzsshaa-truong

Cybersecurity Witwear

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