Abstract: Physically unclonable function (PUF) is a promising security primitive for authentication security.Proposing attack models tailored to different PUFs can drive their design improvement and enhance security.Adversarial-arbiter PUF (A-APUF) is a secure PUF proposed in 2021,claiming to effectively resist modeling attacks.This paper focuses on the A-APUF and proposes a bidirectional- challenge-based attack model.Firstly,bidirectional challenge sequences are applied to the A-APUF to obtain bidirectional response sequence,and then the control sequence is calculated.Secondly,the XOR tap coefficients are calculated using the control sequence.Finally,the XOR tap coefficients are used to crack the A-APUF into an arbiter PUF (APUF).The experiments show that this model effectively reduces attack difficulty for both standard and upgraded defense mechanisms of A-APUF,bringing it to the level of an ordinary APUF.Using a conventional feedforward neural network achieves over 90% accuracy with just 1 000 challenge-response  pairs for A-APUF.

Key words: physically unclonable function (PUF), obfuscation, bidirectional challenge sequence, modeling attack