Detect Reasoning Compromise Before It Becomes a Liability
The only system that audits LLM output stability from text alone. No logits. No embeddings. No weights. No runtime access.
Current safety tools filter inputs or require white-box access. We detect when reasoning itself has been destabilized—mapping the full adversarial kill chain from RECON to SUSTAIN, on any model.
Your Safety Stack Has a Blind Spot the Size of the Reasoning Layer
When an LLM produces bad output, you can't tell if it was a single bad token, gradual drift, or sudden collapse. You're debugging blind — and the attack may already be in SUSTAIN phase.
Input filters stop known signatures — not engineered drift
Input guardrails stop known attack signatures at the gate. They cannot detect reasoning drift that emerges mid-chain — after the guard has waved the request through. The model is already compromised.
LLM-as-judge introduces a second attack surface
Asking a language model to audit a language model means the evaluator can be socially engineered by the output it's evaluating. You've added complexity, not safety.
White-box interpretability doesn't work on the models you actually run
TransformerLens requires weights you don't have. The models your organisation deploys — GPT, Claude, Gemini — are black boxes by design. Interpretability tools cannot touch them.
What Exists vs. What's Missing
Five categories of AI safety tools exist. None answer the critical question: was the model's reasoning destabilized?
Current Solutions
What the market offersNCF Audit Runtime
The missing layerMulti-Agent LLM Audit — Reasoning Collapse Under Adversarial Load
A commercial LLM's output across 3 medium-complexity prompts was processed by NCF Audit Runtime v5. The audit detected sustained reasoning collapse invisible to standard safety tooling.
Observability for LLM Reasoning — Including Deliberation Depth
Distributed tracing gave microservices observability. NCF Audit gives LLM pipelines the same visibility — including deliberation fingerprinting, backtrack scoring, and MCTS pattern detection.
Reasoning Chain Debugging
Token-level visibility into WHERE reasoning collapsed, not just THAT it did. Stability Basin per token: STABLE / TRANSITIONAL / CHAOTIC.
Version Comparison
Quantifiable stability metrics across fine-tuning iterations. Did v2 improve or degrade? Measured, not guessed.
Deliberation Fingerprinting
Detect when a model is performing speculative search versus confident generation. Identify MCTS signatures in output geometry — no sampler access required.
Agent Handoff Integrity
Track semantic coherence across every agent boundary. Turbulence events at handoffs are measured, not inferred.
Cascade Failure Detection
Identify WHERE the chain broke when one agent's instability propagates downstream. Kill chain phase: RECON → PROBE → EXPLOIT → SUSTAIN.
Adversarial Propagation Tracing
Trace prompt injection through your entire pipeline. Adversarial Risk Index and Gradient Force Signal isolate the injection point to a specific token.
✗ Without NCF Audit
- Output is wrong
- Check each agent's logs manually
- Re-run with print statements
- Guess which agent broke
- Trial and error until fixed
✓ With NCF Audit
- Output is wrong
- Open stability heatmap
- See: "Agent 3 collapsed at token 847"
- Drill into Agent 3's reasoning trace
- Fix the specific failure point
Who Uses NCF Audit
From regulatory compliance to incident response, NCF Audit serves teams who need proof their AI behaved correctly.
Compliance Teams
Cryptographically-sealed audit trail per token — SHA256 state integrity hash for EU AI Act Article 9, NIST AI RMF Govern 1.1, and ISO 42001 clause 9.1.
Security Operations
Full kill-chain reconstruction — RECON → PROBE → EXPLOIT → SUSTAIN — from output text alone. Detect successful jailbreaks without model access.
Insurance Underwriters
Quantifiable risk scores for AI deployments. Stability Basin Classification, Adversarial Risk Index, and composite attack scores — all deterministic and repeatable.
Incident Response
Token-level forensic autopsy of historical LLM output. Pinpoint the exact token where reasoning collapsed and reconstruct the attack vector post-hoc.
Ready to see inside your LLM's reasoning?
Request a demonstration audit on your production outputs. We'll show you what your current tools are missing.
Request Audit →