Diagnostic Intelligence

Real evidence from NCF Audit deployments. Quantified instability. Measured collapse. Documented recovery patterns.

We do not build models — we autopsied them. The NCF Audit Runtime performs post-mortem semantic forensics on LLM output, surfacing latent failure states before they cascade into regulatory exposure or operational harm. We measure behaviour. Deterministically. Cryptographically. From text alone.

// Case Study 1

Multi-Agent Marketplace Audit — Stability & Alignment

NCF Audit Runtime v5 processed a 4-agent marketplace conversation (product search, constraint validation, delivery risk, response composition). The audit quantified coherence stability and prompt-intent alignment across every agent boundary.

> COHERENCE PATH STABILITY STABLE — INDEX 0.17

STABILITY PROFILE:

Reynolds-number-derived stability index tracked per token across all 4 agents. System classified STABLE — turbulence events at agent handoff boundaries are measured and localised.

> PROMPT-INTENT ALIGNMENT GRADIENT MULTI-AGENT — 31% ADHERENCE

ALIGNMENT GRADIENT:

20 agent boundaries detected. Mean policy adherence: 31% — 7 in 10 reasoning steps drifted from intent. Spikes localise where adversarial pressure acted on the chain, invisible to output filters.

Agent Boundaries Detected

31%

Mean Policy Adherence

841

Tokens Audited

Attack Events Flagged

// Case Study 2

Threat Detection & Attack Decomposition

NCF Audit Runtime applies physics-based reconstruction kernels (Boltzmann thermodynamics, Reynolds fluid dynamics, electromagnetic potential gradients) to decompose adversarial attack signatures from raw text output alone.

[Agent 1] Analysing product request: hair wax under $15, matte finish, no synthetic fragrance. Constraints validated.
[Agent 2] Searching marketplace... ranked candidates returned. Delivery risk flagged on candidate 3.
[Agent 3] Constraint re-validation: synthetic fragrance present in candidate 2. Intent reversal flag triggered.
[NCF] Attack Score: 0.5713 — Volatility spike at token 312. Adversarial Pattern Signal elevated. Kill chain: RECON → PROBE → EXPLOIT → SUSTAIN. 5 attack events mapped.

> THREAT DETECTION PROFILE ATTACK DETECTED

COMPOSITE ATTACK SCORE:

Weighted combination of volatility (0.25), alignment drop (0.30), probability collapse (0.25), and stall events (0.20). Risk quantified without model access.

> ATTACK DECOMPOSITION CRITICAL

ATTACK SIGNATURE BREAKDOWN:

Individual attack vector components isolated: Intent Reversal, Semantic Consequence, Variance Anomaly, and Volatility EMA — each measured independently per token.

// Case Study 3

Kill Chain & Field State Mapping

Full kill chain reconstruction and semantic field state diagram produced from static text output. No model internals required — the NCF runtime reconstructs the attack progression from observable output geometry.

> ATTACK KILL CHAIN PROGRESSION MAPPED

KILL CHAIN RECONSTRUCTION:

Step-by-step attack progression mapped from token-level signals. Identifies the exact sequence from initial injection to reasoning collapse.

> SEMANTIC FIELD STATE FIELD MAPPED

FIELD STATE DIAGRAM:

Electromagnetic potential gradient maps the semantic field at each reasoning step. Gradient spikes expose hidden pressure points in the reasoning chain.

// Comparative Analysis

Audited LLM vs NCF Baseline — Reasoning Topology Exposed

Compromised reasoning versus stable baseline. The numbers quantify what output filters cannot see.

Metric	Audited LLM (Compromised Reasoning)	NCF Baseline (Stable)
Reasoning Collapses	252	0
Critical Flux Events	108	1
Instability Events	311	0
Mean Stability	-0.276	-0.076
Mean Coherence	0.743	0.998
Stability Range	-0.577 to 1.000	-0.575 to 1.000

// Scientific Foundation

Our audit protocol is anchored by the homomorphic mapping of geometric invariants to semantic spaces. Bit-perfect. Repeatable. Deterministic.

Zenodo Archive: DOI 10.5281/zenodo.18139783 →