FLIPS identifies LLM instances with 96% closed-set and 90% open-set accuracy by exploiting biases in generated binary random sequences across 237 instances.
Trap: Targeted ran- dom adversarial prompt honeypot for black-box identification.arXiv preprint arXiv:2402.12991
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
LLMPrint generates unique, post-processing-robust fingerprints for base LLMs and their variants via optimized prompt injection with statistical verification for gray-box and black-box settings.
citing papers explorer
-
FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences
FLIPS identifies LLM instances with 96% closed-set and 90% open-set accuracy by exploiting biases in generated binary random sequences across 237 instances.
-
Fingerprinting LLMs via Prompt Injection
LLMPrint generates unique, post-processing-robust fingerprints for base LLMs and their variants via optimized prompt injection with statistical verification for gray-box and black-box settings.