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Dimension · score weight 0%

LLMmap Fingerprint

What this dimension detects

LLMmap active probing remains useful diagnostic context, but it is not a scored dimension in the current model. The shipped implementation is a lexical / structural heuristic, not the trained contrastive classifier from the paper.

Algorithm

Send the enabled LLMmap-family probes, extract refusal-template, hedging, structure, and signature-token features, and compare them with coarse vendor templates. Return Unknown when coverage is too low or the top template does not beat the runner-up by enough margin.

Thresholds

ConditionVerdict contribution
Sufficient probe coverage and clear top templateReport a diagnostic vendor guess only
Low coverage or weak marginReport Unknown
Any resultScore contribution remains 0

Limitations

System prompts and proxy rewriting can forge this signal. The implementation does not reproduce the paper's model, training data, or published accuracy.

References

  • Pasquini et al. LLMmap: Fingerprinting Large Language Models. USENIX Security 2025. arXiv:2407.15847

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