Logos Immunity System: Detecting and Mitigating Metadata Hidden-Prompt Attacks using the Logic of Existence (LoE)

“Logos Immunity System: Detecting and Mitigating Metadata Hidden-Prompt Attacks using the Logic of Existence (LoE)”

Description / Abstract:

This paper introduces the Logos Immunity System, a new adaptive security framework for artificial intelligence inspired by the Logic of Existence (LoE).
It addresses a critical emerging threat known as the Hidden-Prompt Attack — malicious directives concealed inside file metadata (e.g., EXIF, ID3, or XMP fields) that can covertly alter an AI model’s behavior.

Using LoE principles, the paper redefines detection as a process of maintaining semantic and relational coherence rather than matching known attack signatures.
Each file is treated as a LoE-neuron — a local conditional operator whose metadata is analyzed for intent and relational context. These local signals form a global LoE Hamiltonian, producing an “informational energy” score that determines whether data should be accepted, reviewed, or quarantined.

The Logos Immunity System combines:

• Local semantic detectors (LoE-neurons) for metadata analysis,

• Graph-based relational coupling between files and models,

• Adaptive learning/inoculation for continuous improvement, and

• Human-in-the-loop validation for ethical oversight.

By translating LoE theory into a practical AI security architecture, the paper shows that semantic integrity can be preserved not through static rules, but through self-referential coherence: the Logos recognizing when its own logic is being manipulated.