Sovereign Local AI Systems
Sampler Mechanics, Security Threat Surfaces, and Runtime Governance
An original technical framework by Cory Miller
Local AI becomes genuinely sovereign only when the system governing inference is as carefully bounded as the model producing it.
Abstract
Local AI execution environments—particularly lightweight inference engines such as llama.cpp—offer autonomy, privacy, resilience, and the ability to operate without continuous dependence on cloud infrastructure. That autonomy, however, transfers responsibility for system integrity from the service provider to the local runtime.
A sovereign runtime therefore has to govern more than model inference. It must account for sampler behavior, dependency integrity, generated-code execution, network exposure, runtime limits, provenance, authority, and the epistemic status of model-generated claims.
This white paper presents a unified architecture for approaching those problems through the Recursive Statefield Framework (RSF): a model in which state, evidence, authority, causality, provenance, and time are treated as explicit dimensions of computational governance.
1. Introduction
The movement toward local and edge-based AI changes the security model of artificial intelligence.
A cloud system can place substantial portions of its infrastructure behind centralized controls. A local system cannot assume those controls exist. The operator becomes responsible for the integrity of the model, runtime, dependencies, interfaces, generated artifacts, and execution environment.
This creates several governance requirements:
- sampling behavior must be understood and bounded;
- dependencies must be identifiable and verifiable;
- generated code must not automatically become executable authority;
- network surfaces must be explicitly controlled;
- runtime resources must have defined limits;
- model output must remain distinguishable from verified external state.
The central architectural principle is therefore:
A model can generate a representation of an event without that representation becoming evidence that the event actually occurred.
2. Recursive Statefield Framework
RSF treats inference as a governed state transition rather than an automatic path from model output to action.
↓
PROPOSED
↓
PREDICATE VALIDATION
↓ ↓
EXECUTED ABSTAINED
The framework uses six principal dimensions:
- State — the current known condition of the system.
- Evidence — the material supporting a proposed interpretation or transition.
- Authority — the permissions governing what the system may change.
- Causality — the relationship between evidence, intervention, and resulting state.
- Provenance — the origin and transformation history of information.
- Time — temporal validity, ordering, and state history.
The important distinction is that these dimensions do not automatically inherit one another.
An inference does not become authority merely because it was generated. A proposal does not become execution merely because it is syntactically valid. A local record does not become external truth merely because it has a cryptographic hash.
3. Sampler Mathematical Mechanics
The behavior of a local language model is substantially influenced by its sampling configuration. Sampling occurs after the model produces a distribution of candidate tokens and therefore directly affects generation characteristics such as repetition, diversity, entropy, and stability.
3.1 Repetition Penalty
Repetition penalties modify token logits according to the implementation's penalty rule, reducing the probability of repeatedly selecting previously generated tokens.
- θi = token logit before the transformation
- s = repetition-penalty parameter
- repeat_last_n = size of the repetition history considered
A value of 1.0 disables repetition penalization. That does not mathematically guarantee infinite repetition, but under sufficiently repetitive probability distributions it can contribute to degeneration.
3.2 Min-P Truncation
Min-P sampling removes candidate tokens whose probability falls below a specified fraction of the highest-probability candidate.
This constrains the sampling distribution by eliminating sufficiently weak candidates relative to the dominant token.
3.3 Mirostat v2 Entropy Control
Mirostat uses feedback to regulate the information content of generated tokens toward a target entropy.
- μ = adaptive control parameter
- η = learning rate
- H(X̂) = observed entropy
- τ = target entropy
Rather than relying exclusively on a fixed truncation threshold, the sampler responds to observed generation behavior.
4. Degeneration and Sampler Failure
A local generation pipeline can exhibit severe repetition when sampling controls are improperly configured.
A representative configuration might contain:
repeat_last_n = 64
frequency_penalty = 0.000
presence_penalty = 0.000
mirostat = 0
Such a configuration removes several mechanisms that can discourage repetitive trajectories. The resulting output may enter a feedback loop in which recently generated material remains disproportionately attractive.
This illustrates a broader RSF principle: an observable output should be treated as a state produced by a particular computational configuration, not as an isolated artifact detached from its generating conditions.
5. Threat Surface Analysis
5.1 Package Hallucination and Slopsquatting
Generated software instructions can contain package names that do not actually exist. If an operator blindly installs such a package, an attacker could potentially register the name and distribute malicious code.
Defensive controls include:
- dependency lockfiles;
- package-name verification;
- cryptographic hashes where supported;
- trusted package indexes or local mirrors;
- review before installation.
5.2 Unsanitized Code Evaluation
Model-generated code is still untrusted input. Direct execution through mechanisms such as exec(), eval(), or shell invocation can cross the boundary between representation and system authority.
Defensive architecture should therefore place generated code behind explicit execution boundaries.
- AST inspection;
- least-privilege execution;
- isolated environments;
- restricted filesystem access;
- explicit command allowlists;
- human or policy approval for sensitive operations.
5.3 Network Exposure
A local inference service bound to a publicly reachable interface can unintentionally expose the runtime to other machines.
Where remote access is unnecessary, binding services to a loopback interface such as 127.0.0.1 reduces the network attack surface. Where remote access is required, authentication, authorization, encryption, and network segmentation should be considered.
6. Execution Runtime Bounds
Sovereignty does not mean unlimited execution. A well-governed local runtime establishes explicit operational boundaries.
request_timeout_ms = 45000
socket_backlog = 512
These values are examples of configurable runtime controls rather than universal safe defaults. Appropriate limits depend on the device, workload, model, concurrency requirements, and threat model.
The architectural principle is more important than any individual number:
7. Sovereign Runtime Telemetry
A local AI system should be capable of describing the conditions under which an inference occurred.
- Engine: llama.cpp / ggml
- Hardware: ARM NEON, FMA, FP16, INT8-capable acceleration where available
- Context: configured according to model and device constraints
- KV cache: configured according to supported precision and memory budget
- Sampler: explicitly recorded
- Runtime: versioned and identifiable
Recording these parameters turns an output from an isolated string into a reproducible computational event with identifiable generating conditions.
8. Governance Enforcement Module
The governance layer is where sampler mechanics, security controls, and epistemic constraints converge.
↓
MODEL INFERENCE
↓
EPISTEMIC CLASSIFICATION
↓
EVIDENCE / PROVENANCE CHECK
↓
AUTHORITY CHECK
↓
POLICY VALIDATION
↓
EXECUTION BOUNDARY
↓
EXTERNAL CONFIRMATION
↓
STATE COMMIT
If a required predicate fails, the runtime does not convert the failure into a successful state transition.
This is a critical distinction. ABSTAINED is not necessarily a system failure. It can represent the correct outcome when the evidence, authority, provenance, or execution conditions required for a transition are absent.
9. The Epistemic Boundary
The central governance problem for AI is not merely whether a model can produce a plausible answer. The deeper problem is what the surrounding system is permitted to do with that answer.
RSF therefore separates:
↓
Inference
↓
Proposal
↓
Validation
↓
Authorization
↓
Execution
No layer automatically inherits the authority of another.
A model output can propose. It cannot authorize itself. A policy can authorize a class of action. It cannot prove that an external event occurred. A cryptographic state root can protect integrity. It cannot manufacture the truth of the underlying data.
10. The Sovereign Local Runtime
“Sovereign” does not mean that a local computer can independently establish every fact about the external world.
It means the runtime can establish and enforce a clearly defined internal verification boundary.
A locally governed system can record:
- what entered the system;
- what the model inferred;
- what was proposed;
- which predicates were evaluated;
- which predicates passed or failed;
- what the system accepted;
- what it rejected;
- what it actually executed;
- what state resulted.
This is narrower—and more defensible—than claiming that a local runtime can independently establish external reality.
11. Conclusion
Local AI changes the relationship between intelligence and infrastructure. Once inference moves onto a device controlled by the operator, responsibility for the boundaries around that intelligence moves with it.
Sampler configuration affects generation behavior. Dependency controls affect supply-chain integrity. Execution boundaries affect system safety. Network configuration affects exposure. Telemetry affects reproducibility. RSF provides an additional layer concerned with something more fundamental: the conditions under which computational representations are allowed to become authoritative state.
The resulting architecture is not simply an AI wrapper, an audit log, or a collection of security controls.
It is a proposal for treating epistemic status as a first-class property of local computation.
The objective of sovereign AI is not unlimited autonomy.
It is bounded autonomy: the ability to compute, propose, verify, abstain, and execute without allowing inference to silently become authority.
Author & Attribution
Cory Miller is the original author of this white paper and the associated architectural concepts presented here. The work is published as original material and may be referenced or quoted with appropriate attribution.
© 2026 Cory Miller. All rights reserved.
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