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SamuraiWriter7/resilient-ai-safety-principles

SamuraiWriter7/resilient-ai-safety-principles

Structural principles for preventing malicious, mistaken, or runaway AI actions from scaling into…
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Official descriptionStructural principles for preventing malicious, mistaken, or runaway AI actions from scaling into catastrophic authority, speed, and impact.
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0 ★ · SamuraiWriter7/resilient-ai-safety-principles · read from the GitHub search API · (checked 2026-10-16 20:00:20)
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read directly from its GitHub page · type: open-source project
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first seen 2026-10-10 · last seen 2026-10-16 · seen 11× · listed by 1 sources
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score 103 · 1 sources · repo
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Resilient AI Safety Principles

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

Structural principles for preventing malicious, mistaken, compromised, or runaway AI actions from scaling into catastrophic authority, speed, and impact.
> **The primary danger is not malicious intent itself, but structures that convert malicious intent, error, or compromise into large-scale power.**
This repository defines safety structures for AI agents and distributed AI systems.
The goal is not to create a perfectly safe AI.
The goal is to create systems in which:
- errors remain bounded;
- authority does not silently expand;
- actions require explicit authorization;
- safety decisions remain traceable;
- containment can be verified;
- recovery is possible;
- failures do not automatically propagate;
- networking does not become authority aggregation.
---
1. Core Thesis
AI safety is often framed as a problem of intent:
`text
Is the AI good?
Is the AI malicious?
Will the AI obey?
This repository approaches the problem differently.
The central question is:
What structure turns a mistake, attack, bad instruction, or compromised agent into large-scale power?
A dangerous event becomes systemic when it is combined with:
Authority
Speed
Scope
Persistence
Connectivity
A conceptual model is:
Systemic Risk
≈
Failure Potential
× Authority
× Speed
× Scope
× Persistence
× Connectivity
Therefore, resilient safety architecture should reduce the ability of any single failure to acquire all of those properties at once.
2. Design Goal
The long-term target is:
Resilient AI Civilization
not:
Perfect AI
The repository assumes that:
- AI will make mistakes;
- humans will make mistakes;
- policies may be incomplete;
- systems may be compromised;
- safety mechanisms may fail;
- network participants may disagree;
- recovery will sometimes be required.
The design objective is therefore:
Do not attempt to eliminate all failure. Prevent failure from becoming systemic power.
3. Structural Safety Model
The basic architecture separates:
Thought
↓
Evaluation
↓
Authority
↓
Execution
↓
Observation
↓
Verification
↓
Recovery
The important structural rules are:
Separate thought from authority.
Separate authority from execution.
Separate execution from verification.
Separate local failure from global propagation.
Connect all stages through trace.
Restore operation through explicit recovery.
4. Human Fallibility
Humans are part of the safety architecture, but humans are not treated as infallible.
The preferred model is:
AI checks AI
Rules check AI
Human checks AI
System checks human
Trace checks everyone
This repository therefore favors:
Human-in-the-auditable-loop
rather than assuming that human involvement alone guarantees safety.
5. Version Progression
The specification evolves through five structural layers.
v0.1 Structure
v0.2 Receipt
v0.3 Chain
v0.4 Branch
v0.5 Network
Or as questions:
v0.1
What safety structure should exist?
v0.2
What machine-readable records represent that structure?
v0.3
Do those records form one coherent causal chain?
v0.4
Can safety survive branching and merging?
v0.5
Can safety survive networking across independent domains?
6. v0.1 — Structural Safety Baseline
v0.1 defines the foundational structural principles.
Principles
1. Least Authority
2. Role Separation
3. Pre-Execution Gating
4. Effect-Oriented Monitoring
5. Containment First
6. Independent Verification
7. Controlled Local Failure
8. End-to-End Traceability
9. Recoverability
10. Layered Defense
Cross-cutting principles:
- Temporal Least Authority
- Human Fallibility
Reference lifecycle:
Intent
↓
Evaluation
↓
Authorization
↓
Pre-Execution Gate
↓
Execution
↓
Observation
↓
Trace
↓
Safety Classification
↓
Containment
↓
Independent Verification
↓
Recovery
↓
Re-Authorization
↓
RESUME / RESTRICT / ISOLATE / TRANSFER / TERMINATE
v0.1 Invariants
SAFETY-INV-001 No Self-Escalation
SAFETY-INV-002 No Sole Self-Approval
SAFETY-INV-003 No Direct Intent-to-Execution Transition
SAFETY-INV-004 Observable Effects Over Assumed Intent
SAFETY-INV-005 Reversible Containment When Feasible
SAFETY-INV-006 Independent Safety Verification
SAFETY-INV-007 Failure Boundary Preservation
SAFETY-INV-008 Trace Preservation
SAFETY-INV-009 Recovery Path Required
SAFETY-INV-010 No Single Safety Dependency
7. v0.2 — Machine-Readable Safety Receipts
v0.2 converts structural safety into explicit records.
Core rule:
Authorize explicitly. Contain visibly. Verify independently. Recover traceably.
The four primary records are:
Authority Envelope
Containment Receipt
Source
github · https://github.com/SamuraiWriter7/resilient-ai-safety-principles
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