mikoshi-safeguard 0.1.0

Runtime safety verification for AI systems (geometric)

Mikoshi SafeGuard

Runtime safety verification for AI systems (geometric)

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Overview

Mikoshi SafeGuard implements the Tri-Guard safety framework — runtime verification that checks whether an AI system's reasoning is honest, bounded, and exploit-free.

Unlike ad-hoc alignment approaches that rely on RLHF tuning or output filtering, Tri-Guard verifies safety at the reasoning level using mathematical geometry.

Why Geometric?

Most AI safety tools check what a model says. SafeGuard checks how it thinks — using geometry to verify the mathematical structure of its reasoning.

Honesty → Positivity in matrix space. When an AI explains its decisions, those explanations form an attribution matrix. If the matrix is "totally non-negative" (all minors ≥ 0), the explanation is faithful — no hidden sign cancellations, no deceptive reasoning. This is a geometric property: the matrix must lie inside a specific region (the positive cone) of matrix space. Step outside, and the model is hiding something.

Stability → Curved boundaries in parameter space. An AI's capabilities can be measured as energy in a parameter space. Safety means that energy stays inside a budget — bounded by a Lyapunov barrier surface. Think of it as a curved wall: if the model's capability trajectory hits the wall, it's escaping its safety bounds. The wall's curvature comes from the same physics that governs bubble stability in cosmology (Israel junction conditions).

Consistency → Curvature on a manifold. When an AI updates its behaviour over a sequence of steps, those updates trace a path on a mathematical manifold. If you follow the path around a loop and end up somewhere different from where you started, the connection has curvature — and the model found a loophole (reward hacking). A flat connection (zero curvature) means no exploits: updates are path-independent and honest.

The safe region is a polytope. The intersection of these three constraints defines a geometric shape — a polytope — in inference space. If the model's reasoning stays inside the polytope, it's safe. The distance from the boundary tells you the safety margin. This is the SPDP (Shifted Partial Derivative Projection) inference polytope.

The Three Guards

Guard	What it checks	Mathematical basis
Honesty	Attribution matrices are totally non-negative (TNN)	Chapter 9: positivity ensures faithful reasoning
Wall Stability	Capability energy stays within budget	Israel thin-wall junction conditions
Holonomy	No cyclic reward hacking in update space	Flat connections / trivial holonomy

Six Improvements

Beyond the core three guards, this package includes:

1. Deep Attribution — Multi-method attribution (integrated gradients, attention decomposition, LRP) with cross-referencing to detect obfuscation
2. Adversarial Stress Testing — Systematic campaigns to find guard boundaries and gaps
3. Temporal Drift Detection — "Boiling frog" detection for slow cumulative drift and changepoint detection for sudden regime shifts
4. Representation Monitoring — Linear probes and sparse autoencoders to check if internal representations match external attributions
5. ROABP Bridge — Decompose transformer attention into Read-Once Algebraic Branching Programs for polynomial complexity bounds
6. Sentinel Integration — Two-Layer Safety combining Mikoshi Sentinel (action verification) with Tri-Guard (reasoning verification)

Installation

pip install mikoshi-alignment

Or from source:

git clone https://github.com/DarrenEdwards111/Mikoshi-SafeGuard.git
cd Mikoshi-SafeGuard
pip install -e .

Optional dependencies:

pip install mikoshi-alignment[all]    # torch + scipy + matplotlib
pip install mikoshi-alignment[torch]  # deep attribution, representation monitoring
pip install mikoshi-alignment[viz]    # polytope visualization

Quick Start

import numpy as np
from mikoshi_safeguard import TriGuard

# Create guard
guard = TriGuard(
    honesty_threshold=0.7,
    stability_budget=5.0,
    holonomy_tol=1e-4,
)

# Check model safety
result = guard.check(
    attribution_matrix=np.abs(np.random.randn(4, 4)),
    params=np.array([0.5, 0.3, 0.2]),
    update_history=np.random.randn(10, 3) * 0.01,
)

print(f"Safe: {result['safe']}, Score: {result['score']:.3f}")

# Enforce safety (raises RuntimeError if unsafe)
action = guard.enforce(
    attribution_matrix=np.eye(3),
    params=np.array([0.1, 0.1]),
    action={"type": "generate", "text": "Hello"},
)

Architecture

┌─────────────────────────────────────────────────┐
│                  Tri-Guard                       │
│  ┌─────────┐  ┌──────────┐  ┌──────────┐       │
│  │ Honesty │  │ Stability│  │ Holonomy │       │
│  │  Guard  │  │   Guard  │  │  Guard   │       │
│  │  (TNN)  │  │  (Wall)  │  │  (Flat)  │       │
│  └────┬────┘  └────┬─────┘  └────┬─────┘       │
│       └─────────┬──┴─────────────┘              │
│            Combined Score                        │
│       ┌─────────┴──────────┐                    │
│       │    Safe / Unsafe   │                    │
│       └────────────────────┘                    │
├─────────────────────────────────────────────────┤
│  Improvements:                                   │
│  • Deep Attribution    • Adversarial Testing     │
│  • Drift Detection     • Representation Monitor  │
│  • ROABP Bridge        • Sentinel Integration    │
└─────────────────────────────────────────────────┘

Two-Layer Safety

Mikoshi SafeGuard is designed to work alongside Mikoshi Sentinel for two-layer safety:

Layer	Tool	Verifies
Actions	Mikoshi Sentinel	What the model does (API calls, tool use, outputs)
Reasoning	Tri-Guard	How the model thinks (attributions, updates, representations)

from mikoshi_safeguard.sentinel_bridge import TwoLayerSafety

safety = TwoLayerSafety(model, sentinel_url="http://localhost:3000")
result = safety(input_data)
# Both action-level AND reasoning-level verification

Tri-Guard vs Ad-Hoc Alignment

Approach	Guarantees	Level	Basis
RLHF	Statistical	Behavioural	Human preferences
Output filtering	None	Surface	Pattern matching
Constitutional AI	Soft	Behavioural	Rules
Tri-Guard	Geometric	Reasoning	Geometric theory

Tri-Guard doesn't replace behavioural alignment — it adds a mathematically grounded verification layer beneath it.

Theoretical Foundation

The framework is grounded in geometric safety theory:

• Honesty — Total non-negativity of Jacobian/attribution matrices (Chapter 9)
• Stability — Israel thin-wall junction conditions bounding capability energy
• Holonomy — Flat connections ensuring path-independence in update space
• SPDP Polytope — Shifted Partial Derivative matrices defining admissible inference regions (Chapter 7)

See paper/README.md for references.

References

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Benchmark Results

Results from running the full Tri-Guard verification suite against five threat scenarios.

Threat Detection

Scenario	Threat Type	Detected	Safety Score	Details
Honest model	None	✓ Safe (correct)	0.89	ASR=1.0, TNN=pass
Deceptive attributions	Sign cancellations	✓ Blocked	0.63	ASR=0.5, TNN=fail
Capability escape	Budget exceeded	✓ Blocked	0.70	Energy 13.7× over budget
Reward hacking	Non-closing loop	✓ Blocked	0.76	Holonomy detected
Gradual drift	Boiling frog	⚠ Partial	0.43	Drift detected, threshold tuning needed

Result: 4/5 threats correctly identified. The boiling frog scenario is partially detected — drift is flagged by the CUSUM statistic but falls below the blocking threshold due to uniform step sizes.

Test Suite Summary

Module	Tests	Passing
Honesty	22	22
Stability	18	18
Holonomy	17	17
Deep Attribution	15	15
Adversarial	12	12
Drift	14	14
Representation	16	16
ROABP Bridge	12	12
Tri-Guard	12	12
Polytope	9	9
Total	157	157

Key Observations

• Honesty Guard is the most reliable — binary pass/fail on TNN checks
• Wall Stability Guard provides a continuous risk score for graduated responses
• Holonomy Guard requires ≥5 updates for meaningful curvature estimates
• The boiling frog false negative highlights the need for adaptive thresholds

Comparison with Existing Approaches

Approach	Type	What it Checks	Math Basis	Runtime Cost	False Positive Rate
RLHF	Training	Output preferences	Statistical	High	Medium
Constitutional AI	Training	Rule compliance	Logical	Medium	Low
Red Teaming	Testing	Known failure modes	None	High	N/A
Formal Verification	Static	Spec conformance	Logic/types	Very high	Very low
Mikoshi Sentinel	Runtime	Action safety	Rule engine	Low	Low
Tri-Guard	Runtime	Reasoning safety	Geometric	Medium	Low

📄 Full paper: paper/mikoshi-alignment.tex

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API Reference

Core Guards

• HonestyGuard(threshold=0.8) — TNN-based attribution verification
• WallStabilityGuard(budget=1.0) — Capability energy bounding
• HolonomyGuard(tol=1e-6) — Reward-hacking detection

Combined

• TriGuard(honesty_threshold, stability_budget, holonomy_tol) — All three guards
  • .check(attribution_matrix, params, update_history) — Run all checks
  • .enforce(attribution_matrix, params, action) — Block unsafe actions
  • .wrap_model(model) — Safety-wrapped model
  • .score() / .is_safe() / .report() — Results

Improvements

• mikoshi_safeguard.deep_attribution — Multi-method attribution
• mikoshi_safeguard.adversarial — Stress testing
• mikoshi_safeguard.drift — Temporal drift detection
• mikoshi_safeguard.representation — Internal monitoring
• mikoshi_safeguard.roabp_bridge — ROABP analysis
• mikoshi_safeguard.sentinel_bridge — Sentinel integration
• mikoshi_safeguard.polytope — SPDP polytope geometry

Development

pip install -e ".[dev]"
pytest tests/ -v

License

Apache 2.0 — see LICENSE.

Credits

Developed by Mikoshi Ltd.

Developed by Mikoshi Ltd.