KBF uses stable numerical recall near the knowledge boundary to fingerprint and audit black-box LLM APIs, successfully detecting all tested substitutions and some real-world inconsistencies across production endpoints.
Are robust llm fingerprints adversarially robust?
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proposes referential security as a paradigm for AI evaluations that reframes model identity as verifiable to support reproducible audits and regulatory decisions despite system changes.
citing papers explorer
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KBF: Knowledge Boundary as Fingerprint for Language Model and Black-Box API Auditing
KBF uses stable numerical recall near the knowledge boundary to fingerprint and audit black-box LLM APIs, successfully detecting all tested substitutions and some real-world inconsistencies across production endpoints.
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Referential Security as a New Paradigm for AI Evaluations
Proposes referential security as a paradigm for AI evaluations that reframes model identity as verifiable to support reproducible audits and regulatory decisions despite system changes.