Device authentication protocols based on a strong physical unclonable function (PUF) show promise for enhancing Internet of Things security. However, a strong PUF is vulnerable to machine learning (ML) attacks. This paper proposes a Feistel structure sequential obfuscation-based PUF (Feistel-PUF) to resist ML attacks. When the PUF is obfuscated by the Feistel structure, the challenge-response relationship resembles that of sequential logic circuits. Specifically, each response depends on both current and historical challenges, thereby significantly increasing the complexity of the challenge-response mapping. Experimental results showed that even with 1 million collected challenge response pairs, the prediction accuracies of ML attacks on Feistel-PUF remained at approximately 50%. Notably, the obfuscation process excluded response data, thereby preserving the randomness, reliability, and uniqueness of PUF with negligible performance degradation. The Feistel structure was iteratively reused for sequential obfuscation processing, maintaining constant and minimal hardware overhead. We propose a novel approach that combines the Feistel-PUF with an authentication protocol. This system synchronizes obfuscation parameters during the initial registration phase and implements dynamic updates throughout the authentication process. This approach allows the obfuscation structure to be made public, thereby overcoming the reliance on structure or algorithm secrecy in traditional dynamic challenge obfuscation schemes. The security of the protocol was confirmed by subjecting it to ProVerif verification and security analysis.

Prototype pollution is a kind of severe vulnerability in Node.js that enables unauthorized modification of object prototypes, leading to critical security risks.Although prior work has focused on detecting and assessing the severity of prototype pollution, automated and efficient exploit generation for server-side scenarios remains unaddressed.To bridge this gap, we develop AePPollo 1 , a framework that integrates taint analysis, knowledge-enhanced Large Language Models (LLMs), and fuzzing to automatically generate exploits for Node.jsprototype pollution.Evaluated on 218 real-world prototype pollution vulnerabilities, AePPollo achieves a 91.74% exploit generation success rate with minimal overhead, outperforming state-of-theart methods.

Open-source kernels have been adopted by massive downstream vendors on billions of devices. However, these vendors often omit or delay the adoption of patches released in the mainstream version. Even worse, many vendors are not publicizing the patching progress or even disclosing misleading information. However, patching status is critical for groups (e.g., governments and enterprise users) that are keen to security threats. Such a practice motivates the need for reliable patch presence testing for downstream kernels. Currently, the best means of patch presence testing is to examine the existence of a patch in the target kernel by using the code signature match. However, such an approach cannot address the key challenges in practice. Specifically, downstream vendors widely customize the mainstream code and use non-standard building configurations, which often change the code around the patching sites such that the code signatures are ineffective.