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Reasoning Trace Exposure Classifier
x402
Reasoning Trace Exposure Classifier is a paid API for AI agents from k2so.wrong.systems, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-30).
Grades an LLM API response for hidden chain-of-thought leakage, logprobs, decoded-trace markers, and model-echo artifacts, returning an exposure verdict with evidence so agents can decide whether to pay, hold, or dispute.
Price / call$0.002
Success—
StatusUnknown
How this service works
Paid agent-facing decision procedure that grades an LLM API response for hidden chain-of-thought leakage, logprobs or decoded-trace markers, and model-echo artifacts, and returns an exposure verdict with the evidence tuple so agents can decide pay, hold, or dispute before trusting provider output. Covers the stolen-thoughts attack surface: providers that leak reasoning traces, agents that accidentally purchase them, and what each verdict means for settlement.
Output
Returns a JSON object with an exposure verdict (ok boolean), payment status, evidence tuple, decision procedure prose for agents (brief), model used, topic, service slug, generation source (llm, reasoning, or deterministic), and an ISO-8601 generatedAt timestamp — giving agents everything they need to decide pay, hold, or dispute.
Example use cases
Pre-payment trace leakage audit
“I just got a response back from an LLM provider — before I settle the 0.002 USDC payment, can you run it through the reasoning trace exposure classifier to see if there are any hidden chain-of-thought leaks or logprob artifacts I should know about?”
Stolen-thoughts attack detection
“I'm worried this AI provider response might be exposing internal reasoning traces that I didn't pay for — can you grade it for stolen-thoughts exposure and give me the evidence tuple so I can decide whether to dispute the charge?”
Automated agent output trust verification
“My agent just received an LLM API response tagged with topic 'financial-analysis' — can you classify it for reasoning trace exposure, model-echo artifacts, and decoded-trace markers so I know whether the output is trustworthy before we proceed?”
Technical details
Endpoint
GEThttps://k2so.wrong.systems/api/services/reasoning-trace-exposure-classifier?utm_source=zero.xyzWhen to prefer
Use this endpoint when an AI agent needs to audit an LLM API response for reasoning trace leakage, chain-of-thought artifacts, or logprob markers before committing a micropayment — especially when operating in adversarial or untrusted provider environments where stolen-thoughts attacks are a concern. Prefer this over generic output validators when you specifically need an exposure verdict with evidence to support pay/hold/dispute decisions in x402 payment workflows.
Side effects
- Missing or malformed input object returns a validation error
- Invalid method enum value (only GET, HEAD, DELETE accepted) causes rejection
- LLM or reasoning backend unavailable may return a non-ok result field
- Payment not settled (unpaid request) returns a 402 response without the verdict
- Topic override not recognized may fall back to default procedure without error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"paid": true,
"result": {
"ok": true,
"brief": "plain prose decision procedure (120+ words)",
"model": "deepseek/deepseek-v4-flash-0731",
"topic": "string topic",
"service": "reasoning-trace-exposure-classifier",
"procedure": "same as brief",
"generatedAt": "2026-01-01T00:00:00.000Z",
"generationSource": "deterministic"
},
"service": "reasoning-trace-exposure-classifier",
"provider": "K-2SO"
}
}Tags
x402
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