Detect Reasoning Compromise Without Model Access
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—after the fact, on any model.
Existing Tools Miss Reasoning Compromise
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.
Input filters miss engineered attacks
Adversarial prompts designed to appear benign bypass content filters, then destabilize reasoning mid-sequence. The attack succeeds before any safety system reacts.
Output filters detect content, not reasoning
Checking for harmful words in the response misses the deeper issue: was the model's reasoning chain compromised? Superficially acceptable output can mask fundamental instability.
Interpretability requires white-box access
Tools like TransformerLens analyze model internals—useless for API-based models. You can't inspect GPT-4's attention weights or Claude's hidden states.
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 layerGPT-5.2 Multi Agent Instability - Hidden Logic Tax
The monologue of GPT5.2 for 3 meduim difficulty prompts was processed by NCF. The audit runtime detected sustained logic reset, invisible to standard tools.
Observability for LLM Reasoning
Distributed tracing gave microservices observability. NCF Audit gives LLM pipelines the same visibility—especially critical for multi-agent systems.
Reasoning Chain Debugging
Token-level visibility into WHERE reasoning went wrong, not just THAT it went wrong.
Version Comparison
Quantifiable stability metrics across fine-tuning iterations. Did v2 improve or degrade?
Prompt Engineering
A/B test prompts by stability profile. Which prompts produce turbulent reasoning?
Agent Handoff Integrity
Track semantic coherence across agent boundaries in multi-agent pipelines.
Cascade Failure Detection
Identify WHERE the chain broke when one agent's instability propagates downstream.
Adversarial Propagation
Trace prompt injection "infection" through your entire pipeline.
✗ 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
Audit evidence for EU AI Act, NIST AI RMF, ISO 42001
Security Operations
Detect successful jailbreaks from output analysis
Insurance Underwriters
Quantifiable risk scores for AI deployments
Incident Response
Forensic analysis of historical chatbot logs
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.
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