Configuration
API credentials and sampling parameters. All data stays in browser memory only.
StreamEnable to measure TTFT
LogprobsStrongest identity evidence
Context ProbeUses more tokens
Save LogsDetails go to table
Trusted Reference Endpoint (Differential Mode · Optional)Optional

Enter an endpoint you trust, such as your own official API key. The audit will run the same prompts against both the suspect endpoint and this reference endpoint, then compare them differentially using the MMD distribution test and capability pass-rate delta. Leave blank to use absolute mode only. The key is used only for this run, never persisted or reported.

Test Suite
Select probes to enable. Each group can be toggled independently.
enabled 22/27 items
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RunnerDirect
Preflight → Usage → Identity three-phase execution
Ready0 / 87
Real-time Log0 entries
No logs yet
MMD BaselineNot Recorded
Model Equality Testing (Gao et al., ICLR 2025) uses a two-sample Maximum Mean Discrepancy test to check whether two sets of responses come from the same distribution. First record a baseline from a trusted endpoint, then run audits to compare. Baseline is stored in localStorage, never uploaded.
MMD testing requires temperature > 0 to capture distribution differences. Current temperature = 0.
Dashboard A · Token Usage Audit
Avg Prompt Ratio
1.000
normal
Avg Completion Ratio
1.000
normal
Fixed Offset Estimate
0.0
tokens / request
High Risk Samples
0
/ 0 samples
Overall Risk Level
Normal
Pattern: No significant deviation detected
Linear Regression Evidence

样本数不足,无法可靠识别模式(至少需要 3 个有效样本)

Dashboard B · Model Identity Audit
Overall Verdict
Inconclusive
Based on 12 weighted signals. Unavailable dimensions' weights are proportionally transferred to other dimensions.
0
Anomaly Confidence
0/100
Higher means more deviation from claimed model
Model Comparison
Claimed:gpt-4o
Suspected:unknown
12-Dimension Signal Lights
切词器家族指纹
0% 35%
能力地板
0% 20%
LLMmap Fingerprint
0% 0%
MMD Distribution Equivalence Test
0% 20%
ITT Rhythm Fingerprint
0% 0%
Response Latency & Speed
0% 0%
Self-Identification Probe
0% 0%
Canary Prompts
0% 0%
Refusal Boundary Probe
0% 0%
Context Window
0% 0%
缓存重放检测
0% 15%
Sparse-Token 压力测试
0% 10%
Stylometric Analysis
0% 0%
Evidence Chain
Expand each dimension to see the full reasoning process and raw evidence

Visualization Charts
Core scatter plot reveals bias patterns: fixed offset produces shift, proportional inflation produces tilt
Detailed Results
NameCategoryLocal PtRemote PtP RatioLocal CtRemote CtC RatioTTFTtok/sLogpRisk
No results yet