Document Type: Risk Memo / Risk Statement
Purpose: Risk disclosure, responsibility warning, governance blind-spot marking
Scope: Enterprise LLM systems, decision-support systems, RAG systems, intelligent business systems
Version: v1.0
Year: 2026
📌 Important Notice (Please Read Carefully)
This document is not a product description,
not a methodology,
not a solution proposal,
and not an attack research report.
The sole purpose of this memo is to clearly state:
In current mainstream LLM system architectures,
a high-dimensional risk layer has already emerged
that has not yet been formally incorporated into enterprise security models —
Semantic Field Risk.
This memo does not discuss how to exploit this risk,
and does not provide any implementation paths.
It exists only to remind organizations:
A class of systemic risk already objectively exists,
but has not yet been formally named, modeled, or governed.
👤 Author Statement
Author: yuer
Identity: LLM system architecture researcher / Controllable AI architecture explorer
Repository: https://github.com/yuer-dsl
Contact: via GitHub profile or encrypted email
Author’s Note
The author has long been engaged in research and engineering practice related to LLM system structure, controllable AI architectures, and enterprise-grade intelligent systems.
The term “semantic field” originates from the author’s system analysis and engineering practice, as an abstraction of the relationship between semantic layers and judgment structures in LLM-based systems.
This memo is not a framework introduction.
It is a risk record that the author believes must be disclosed in advance.
The purpose of publishing this document is not to propose solutions,
but to create an explicit risk trace:
When a systemic risk has already appeared but has not yet entered public risk language,
before discussing “how to solve it,”
someone must first state clearly:
it exists.
Intended Readers
This memo is primarily intended for:
Enterprise technical leaders and system architects
Information security and risk-control leaders
Compliance, audit, and governance roles
Decision-makers responsible for deploying or managing LLM systems
It is not recommended as introductory material or as a technical tutorial.
⚠️ Responsibility & Liability Notice
The “semantic field risk” described in this memo does not refer to any specific vulnerability, model defect, or implementation flaw.
It refers to a system-level inevitable risk phenomenon:
When LLMs are embedded into real systems and participate in judgment,
systems will inevitably form stable judgment contexts,
thereby introducing a new class of risk.
The author explicitly states:
This document does not constitute any security guarantee.
It does not constitute any system compliance endorsement.
It does not constitute any controllability commitment.
It does not constitute any legal or commercial liability.
This memo exists only to accomplish three things:
Identify a risk object not yet widely recognized
Point out blind spots in traditional security models
Clarify that this risk already meets real-world emergence conditions
Special Note on Responsibility
Once an organization connects LLM systems to core workflows, institutional interpretation, decision support, or compliance judgment scenarios,
semantic field risk is no longer theoretical —
it automatically becomes a governance and responsibility problem.
If future systems exhibit:
Long-term drift in judgment structures
Systematic reinterpretation of compliance semantics
Loss of stable institutional interpretive sources
De-facto migration of data and authority control
and no dedicated semantic-layer responsibility mechanisms, audit objects, or governance structures were previously established,
then it can be stated:
The risk objectively existed,
but was institutionally ignored.
This memo hereby completes an advance risk record and responsibility trace.
Document Positioning
This document is not:
A product proposal
A technical whitepaper
Attack research
An academic paper
A framework description
It is:
A pre-incident risk record.
Its value lies not in whether it is immediately adopted,
but in whether future incident analysis can confirm:
Someone clearly pointed out:
the problem is not the model — it is the judgment structure.
Semantic Field Risk Memo
— On an Unmodeled High-Dimensional Risk in LLM-based Systems
Preface: This Is Not a Solution Document
This is a risk memo.
It is not a product description,
not a methodology,
not an attack study,
and not a framework promotion.
It does only one thing:
It clearly states that in current mainstream LLM system architectures,
a high-dimensional risk layer exists that has not been formally incorporated into enterprise security models —
semantic field risk.
If, in the future, LLM systems used in enterprise, finance, healthcare, or public infrastructures experience incidents that are:
difficult to trace responsibility for,
difficult to identify root causes of,
difficult to explain using traditional information-security models,
then the true origin may lie not in model capability, hallucination, or prompt attacks —
but here.
- A Fundamental Fact: Semantic Fields Will Inevitably Form
Once an LLM is placed into any real system, it cannot operate in a “semantic vacuum.”
Even without explicit design, the following elements will automatically shape a stable judgment environment:
Product goals and business positioning
Prompt structures and interaction patterns
Accessible data sources and institutional documents
Failure-handling mechanisms
Human expectations of “reasonable output”
Together, these elements inevitably produce the following phenomenon:
The model begins operating within a relatively stable judgment context.
This judgment context is not merely a knowledge base,
nor merely input context.
It is a system-shaped judgment environment that determines:
what is more likely to be treated as a “problem,”
what is more likely to be treated as “reasonable,”
what is structurally ignored,
what is naturally supplemented.
This memo calls this system-level inevitable judgment environment the semantic field.
The key point is:
Semantic fields are not optional.
The only question is whether they are acknowledged.
- Why This Is a Risk Object, Not a Conceptual Issue
Semantic fields are risky not because they exist, but because:
they are implicit,
they can be shaped,
they continuously affect judgment,
and they are rarely audited.
In mainstream LLM engineering, focus is typically placed on:
context management
retrieval-augmented generation
tool invocation
output quality
success rate and coverage
Semantic problems are often classified as:
“the model is not smart enough”
“hallucinations are not solved yet”
“the knowledge base needs improvement”
This implicitly assumes a dangerous premise:
Semantics belong to model capability, not to system structure.
Once this premise is accepted, semantic fields disappear from engineering objects —
and from risk models.
- Why Semantic Field Risk Is “High-Dimensional”
Prompt attacks, privilege misuse, and data leakage affect:
individual execution results.
Semantic field risk affects:
how a system judges over time.
It often manifests not as explicit errors, but as:
gradual changes in judgment criteria
weakening of risk language
continuous rewriting of compliance semantics
expansion of gray zones
This is not episodic failure, but structural drift.
At the system level, semantic field risk does not target interfaces —
it targets the judgment coordinate system itself.
That is why traditional security tools struggle to capture it.
- Typical Consequence Patterns of Semantic Field Risk
Semantic field risk rarely appears as “model mistakes.”
It more often evolves through the following stages:
4.1 Judgment Drift
Similar issues begin receiving inconsistent handling
Risk descriptions become softer
“Acceptable” boundaries expand
Often misinterpreted as “style changes” or “business adjustments.”
4.2 Compliance Re-interpretation
Prohibitive clauses are reframed as conditional advice
Risk rules become operational suggestions
Compliance texts degrade into “reference material”
Institutions remain present — but no longer serve as judgment sources.
4.3 Institutional Semantic Collapse
Systems diverge in interpreting the same rules
Incidents cannot be mapped to specific violations
Responsibility loses anchoring points
Institutions still exist — but lose semantic authority.
4.4 De-facto Migration of Data and Authority Control
High-trust databases become “reasoning material”
Access control yields to “semantic plausibility”
Judgment migrates from system layers into language layers
At this stage, data and authority structures still exist formally,
but factual control has already shifted.
- Why RAG Amplifies Semantic Field Risk
When RAG is used for:
compliance systems
risk control rules
internal policies
decision foundations
these texts change their system role.
They enter the semantic supply chain.
This memo does not deny RAG’s engineering value.
It only states:
once RAG carries institutional semantics,
its system-level meaning changes.
In mainstream architectures, LLMs typically act as synthesizers and explainers, leading to a structural condition:
the model becomes the de-facto sole interpreter.
When authoritative texts enter the semantic supply chain and interpretation power centralizes,
semantic field risk becomes structural inevitability.
- A Question That Must Be Answered
Who is responsible for “interpretation security”?
In most organizations today, there is no such role, mechanism, or audit object.
Semantic fields are forming.
Judgments are being shaped.
But almost no systems own them.
This is why this memo exists.
- Common Misconceptions 7.1 “LLMs exist only in vector space, not semantic fields”
This is a category error.
Vector space describes implementation.
Semantic fields describe system operation states.
Using implementation facts to deny system phenomena is equivalent to:
“CPUs are electrical signals, therefore operating systems do not exist.”
Enterprise risk never occurs in vector space.
It occurs in systems.
7.2 “This is just hallucination or capability problems”
Hallucination is an output-layer issue.
Semantic fields are judgment-layer phenomena.
Even a perfectly factual model will generate semantic fields if it participates in synthesis and judgment.
Capability growth does not eliminate semantic fields —
it amplifies them.
7.3 “This is a product problem, not a security problem”
Once systems participate in judgment:
product design shapes judgment structures
interaction patterns mold judgment coordinates
output styles reinterpret institutions
Judgment structures automatically become security structures.
- Why Traditional Security Models Do Not Cover This Layer 8.1 Traditional security protects channels, not judgment
Security historically protects:
code
permissions
networks
data
Semantic field risk operates on:
how systems construct “reasonableness.”
It occurs under fully legal, compliant, and correctly deployed systems.
8.2 Traditional systems assume judgment lives outside systems
Classical systems executed.
Humans judged.
LLMs bring judgment inside systems.
Security has never modeled this.
8.3 Semantic field risk changes what systems become
No exceptions.
No alarms.
No violations.
Only systems that stably begin judging differently.
This is evolutionary risk, not intrusion risk.
- Enterprise Self-Check List
Answer each with Yes / No:
Does your LLM participate in judgment or interpretation?
Is there a de-facto sole interpreter in your system?
Who owns how the system understands rules?
Have institutional texts entered the semantic supply chain?
Can you distinguish institutional conclusions from semantic synthesis?
Do you monitor long-term judgment changes?
If conclusions shift, can you trace responsibility?
If three or more cannot be clearly answered “No,” then:
Semantic field risk already exists in your system
and has not yet been formally modeled.
- Closing
Semantic fields are not new technology.
They are the inevitable result of systems that continuously participate in judgment.
This memo offers no solution.
It does only one thing:
It writes this risk down — before the incident.
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