Abstract Text-based passwords serve as a primary means of authentication and play a crucial role in securing information systems. However, easy-to-remember passwords are often vulnerable to targeted password guessing attacks. Research on targeted password guessing not only deepens our understanding of password security but also contributes to enhancing the security of information systems. Although the use of Personally Identifiable Information (PII) and old passwords has been shown to significantly improve the accuracy of targeted password guessing, there has been little research on the combined use of both PII and old passwords for guessing. In an era where PII and old passwords are increasingly accessible, assessing the threat posed by attackers using both PII and old passwords in targeted password guessing is an urgent security issue. To address this gap, we first analyze leaked password and personal information datasets, demonstrating that PII and old passwords critically influence users’ password creation behavior. Then, to simulate the security risks posed by attackers who know both PII and old passwords, we propose the PassGLM model, a model fine-tuned on a targeted password guessing task dataset based on glm-4-9b. PassGLM is capable of generating highly targeted guesses by leveraging both PII and old passwords. Experiments show that PassGLM significantly outperforms leading models that use only PII or only old passwords in terms of guess success rates. Our research demonstrates that combining PII and old passwords can substantially improve the accuracy of password guessing, and that using large language models as tools is an effective way to achieve this improvement.

Abstract Existing threshold Pointcheval–Sanders (PS) signature schemes largely assume equal-weight participants and therefore cannot directly express heterogeneous signing authority in semi-trusted multi-organization settings. This paper presents a dealer-assisted weighted threshold PS signing framework by integrating PS signatures with a Chinese Remainder Theorem (CRT)-based weighted ramp secret sharing (WRSS) mechanism. The main technical contribution is a CRT-to-PS integration method that converts weight-dependent modular shares into exponents while preserving the standard PS verification equation in prime-order bilinear groups. The WRSS base modulus is instantiated as the same prime order used by the bilinear-group scalar field, removing any modular mismatch between CRT reconstruction and PS exponentiation. As a result, the bit-length of each private-key share is proportional to the participant’s weight, and any signing subset whose total weight reaches the reconstruction threshold can jointly generate a valid PS signature. Meanwhile, any coalition whose total weight is at most the privacy threshold learns no information about the master secret beyond what is implied by the public key. We analyze the construction in the semi-honest model and in the WRSS-hybrid model, where the WRSS subroutines are idealized, and establish correctness, unforgeability, and randomization-based non-linkability. We further quantify the incremental computation and communication overhead introduced by the CRT-based weighting layer. The analysis separates CRT arithmetic from the bounded overflow-selection cost and includes heterogeneous-weight parameter tests. The proposed scheme is intended for dealer-assisted, semi-trusted, regulated multi-organization applications, such as joint authorization and credential-issuance workflows with policy-defined participant weights.

Abstract The attacker in white-box model has full access to software implementation of a cryptographic algorithm and full control over its execution environment. In order to solve the issues of high storage cost and inadequate security about most current white-box cryptographic schemes, WAS, an improved white-box cryptographic algorithm over AS iteration is proposed. This scheme utilizes the AS iterative structure to construct a lookup table with a five-layer ASASA structure, and the maximum distance separable matrix is used as a linear layer to achieve complete diffusion in a small number of rounds. Attackers can be prevented from recovering the key under black-box model. The length of nonlinear layer S and affine layer A in lookup table is 16 bits, which effectively avoids decomposition attack against the ASASA structure and makes the algorithm possess anti-key extraction security under the white-box model, while WAS possesses weak white-box (32 KB, 112)-space hardness to satisfy anti-code lifting security. WAS has provable security and better storage cost than existing schemes, with the same anti-key extraction security and anti-code lifting security, only 128 KB of memory space is required in WAS, which is only 14% of SPACE-16 algorithm and 33% of Yoroi-16 algorithm.