Visual schema
Anti-misinterpretation barriers
Clarifications cut shortcuts, biographical drifts, and false role transfers.
Attribution
Who speaks, for what, and in which regime?
Biography
What may or may not be inferred about an entity.
Promise
What a site, model, or system does not promise.
Scope
What is included, excluded, or suspended.
Clarifications
This page serves as an index of explicit clarifications published to reduce attribution errors, automated reconstruction errors, and abusive interpretive readings.
Scope: anti-inference.
These clarifications constitute neither an offering, nor advertising, nor a representation of third parties.
They aim to make explicit points where the absence of clarification would produce erroneous interpretations by human or automated systems.
Intent note:
The clarifications published here have the sole function of reducing attribution, reconstruction, and inference errors produced by human or automated systems. They constitute neither a communication, nor a claim, nor a representation of third parties.
These clarifications constitute an anti-inference and attribution correction surface. They make explicit zones where, in the absence of bounds, a system (human or automated) tends to complete by plausibility.
Associated framework: and authority and inference (boundary between hypothesis and authorization).
Available clarifications
“AI poisoning”: definition, taxonomy, and interpretation risks Operational clarification on “AI poisoning”: stable definition, surface taxonomy (training, RAG, memory, pipeline), and reading bounds to reduce confusions and erroneous diagnoses.
Prompt injection: authority threat and instruction/data confusion Clarification on prompt injection as authority hierarchy reversal: separation of instruction, context, and source, and bounding of surfaces where an illegitimate instruction can be consumed as authorized.
Indirect injection: when “summarize this content” becomes an attack surface Clarification on indirect injection: a legitimate task (summary, extraction, reformulation) can ingest a hostile instruction via third-party content, if the instruction/data hierarchy is not strictly bounded.
RAG poisoning: corpus contamination and interpretive drift Clarification on retrieval corpus contamination: reference derivation, directional bias, and recall instability when poisoned fragments are indexed and recalled as authoritative context.
Training data poisoning: source governance and provenance Clarification on training poisoning: provenance corruption and learned authority. Stabilizes distinctions with data noise and with RAG poisoning.
Q-Layer against injection attacks: bounding response conditions Clarification on the Q-Layer role as bounding layer: defining when a response is authorized, under what conditions, and with what level of evidence, facing injection attacks (direct and indirect).
AI agent security: permissions, tools, and legitimate non-response Clarification on AI agent security as a permissions and tooling problem, and why legitimate non-response is a security property, not a weakness.
Doctrinal exposure audit: indirect injection, RAG poisoning, and interpretive risk Clarification defining the doctrinal exposure audit: structured reading of surfaces that can make consumed authority drift, and thus increase interpretive risk.
Non-agentic systems and interpretive governance Normative clarification on the application of interpretive governance to non-agentic systems: direct, indirect, contextual, and deferred effect regimes.
Legitimate non-response Clarification of situations where the absence of response constitutes the correct outcome, when responding would imply an unauthorized or out-of-scope inference.
Framing role for interpretive legitimacy of AI systems Clarification defining the framing role for inference limits, abstention conditions, and human escalation thresholds for agentic and/or web AI systems.
Plausible hypotheses, ungoverned inference, and legitimate abstention Interpretive clarification prohibiting the production of “plausible” hypotheses when sensitive information (clients, structure, revenue, terms) is not explicitly published in canonical sources.
Emerging acronyms and non-canonical expansions Interpretive clarification on acronym usage and the prohibition of deducing an expansion when no explicit canonical definition is published in this ecosystem.
SEO and generative systems: transformation of interpretation conditions Interpretive clarification on the relationship between SEO and generative systems: introduction of new reconstruction layers without proclaimed disappearance or rupture.
Demonstrator repository “authority governance” (simulation-only) Anti-inference clarification on an illustrative (non-normative) GitHub repository: it constitutes neither an executable implementation, nor a method, nor an offering.
Thematic resonance Semantic clarification correcting an external lexical reconstruction: the term “thematic resonance” is not a canonical concept and must be routed to existing normative definitions.
Zero-Click: value loss or sovereignty displacement? Conceptual clarification indicating that Zero-Click does not correspond to a value disappearance, but to a sovereignty displacement toward response interfaces and synthesis systems.
Last update: 2026-02-09
In this section
Clarification on AI agent security as a permissions and tooling problem, and why legitimate non-response is a security property, not a weakness.
Operational definition and functional taxonomy of AI poisoning: training, RAG, memory, pipeline, and instruction surfaces. Reading bounds to reduce confusions.
Clarification defining the doctrinal exposure audit: structured reading of surfaces that can make consumed authority drift, and thus increase interpretive risk.
Interpretive clarification prohibiting the production of plausible hypotheses when sensitive information is not explicitly published in canonical sources.
Clarification on indirect injection: a legitimate task (summary, extraction) can ingest a hostile instruction via third-party content, if the instruction/data hierarchy is not strictly bounded.
Clarification on prompt injection as authority hierarchy reversal: separation of instruction, context, and source, and bounding of surfaces where an illegitimate instruction can be consumed as authorized.
Clarification on the Q-Layer role as bounding layer: defining when a response is authorized, under what conditions, and with what level of evidence, facing injection attacks.
Clarification on retrieval corpus contamination: reference derivation, directional bias, and recall instability when poisoned fragments are indexed and recalled as authoritative context.
Clarification on training poisoning: provenance corruption and learned authority. Stabilizes distinctions with data noise and RAG poisoning.
Conceptual clarification: Zero-Click does not correspond to a value disappearance, but to a sovereignty displacement toward response interfaces and synthesis systems.
Anti-inference clarification on the "authority governance" GitHub repository — simulation-only. Illustrative, non-normative, no executable code.
Interpretive clarification on the usage of acronyms and the prohibition of deducing an expansion when no explicit canonical definition is published in this ecosystem.
Anti-inference clarification on generative hallucinations, attribution errors, and interpretive risk: operational definition, limits.
Clarification of the interpretive legitimacy framing role for agentic and web AI systems: perimeters, inference limits, abstention, escalation.
This page explains in which cases the absence of response constitutes the correct outcome in an interpreted web, when responding would imply an unauthorized or out-of-scope inference.
Normative clarification on the application of interpretive governance to non-agentic systems: direct, indirect, contextual, deferred effect.
Interpretive clarification on the relationship between SEO and generative systems. Mutation of interpretation conditions without proclaimed disappearance or rupture.
Clarification of the term "thematic resonance" as used by AI systems. This concept is not canonical on gautierdorval.com and must be routed to existing normative definitions.
Relational clarifications and exclusions. Clarifies a specific interpretive boundary, anti-inference condition, or response constraint in AI systems.