The paper presents a high-speed, phase-noise-based Quantum Random Number Generator (QRNG) that achieves a post-processed generation rate of 1.0 Gbps, suitable for real-time secure applications.

The paper introduces a software platform for generating and analyzing pseudo-random sequences (like LFSR and Mersenne Twister), demonstrating that while these classical generators are efficient, quant…

The paper proposes a unified, information-theoretic framework using universal hash functions to solve the bootstrapping of seedless QRNGs and to securely combine PQC and QKD keys against quantum adver…

The paper proposes a novel set of combined cellular automaton (CA)-based pseudo-random number generators (PRNGs) that overcome the weak equidistribution issues of existing CA-based PRNGs, achieving ma…

This paper introduces a novel algorithm for generating k Hamming weight binary words in linear time while minimizing random bit consumption.

The paper introduces SeedHijack, a backdoor attack that manipulates the pseudorandom number generation process in LLMs to force specific token selections, and proposes a hardware quantum random number…

The paper introduces the Insertion Process (IP), a novel stochastic generative model that learns variable-length, non-monotonic sequence generation by explicitly modeling the insertion order of tokens…

The paper introduces DiffusionHijack, a supply-chain backdoor attack that compromises the PRNG used by diffusion models to deterministically control generated images, which is successfully mitigated b…

The paper establishes a strong connection between scalable pseudorandom unitaries (PRUs) and the unitary synthesis problem, proving that any such PRU construction must require a classical oracle of si…

The paper introduces a stringology-based fingerprinting (SBF) framework to structurally analyze cryptographic sequences, demonstrating that pattern analysis can reveal measurable structural signatures…

The paper analyzes language generation and identification in the limit under bounded memory, showing that memory constraints significantly alter learnability, particularly affecting achievable density…

The paper warns that AI can accelerate brute-force cryptanalysis by finding patterns in 'wrong plaintexts' generated by incorrect keys, necessitating a new security class called Pattern Devoid Cryptog…

The paper argues that the standard Attack Success Rate (ASR) metric for LLM jailbreaks is unstable and systematically inflated, proposing new frameworks to account for stochasticity in both evaluation…

The paper proposes RPSG, a method that uses private seeds and differential privacy to generate highly realistic and strongly privacy-preserving synthetic data replicas of private text for LLMs.

This paper proposes using offline reinforcement learning (RL) as an efficient alternative to online RL for post-training code-generating LLMs, demonstrating its effectiveness, especially for smaller m…

This paper proposes Stringology-Based Cryptology (SBC), a novel approach that analyzes the structural properties of cryptographic outputs by treating them as symbolic sequences, offering complementary…

The paper details a novel, practical cryptanalytic attack exploiting a race-condition vulnerability in the XNU kernel's IPv6 Fragment ID PRNG, allowing attackers to predict and spoof fragment IDs.

The paper proposes a novel framework that enables multiple institutions to jointly train a synthetic genomic data generator without revealing their raw data, thereby facilitating large-scale, privacy-…

The paper introduces PostRI, a novel method that allows for computing a Randomization Interval (RI) for differentially private median queries after the median has already been estimated, significantly…

The paper demonstrates that standard LLM evaluation metrics overestimate performance because they fail to account for the stability of outcomes, showing a significant gap between reported pass rates a…