Learning-Based Cost-Aware Defense of Parallel Server Systems against Malicious Attacks

We consider the cyber-physical security of parallel server systems, which is relevant for a variety of engineering applications such as networking, manufacturing, and transportation. These systems rely on feedback control and may thus be vulnerable to malicious attacks such as denial-of-service,...

Several New Classes of Self-Orthogonal Minimal Linear Codes Violating the Ashikhmin-Barg Condition

Whitepaper called Several New Classes Of Self-Orthogonal Minimal Linear Codes Violating The Ashikhmin-Barg Condition...

White-Basilisk: a Hybrid Model for Code Vulnerability Detection

The proliferation of software vulnerabilities presents a significant challenge to cybersecurity, necessitating more effective detection methodologies. We introduce White-Basilisk, a novel approach to vulnerability detection that demonstrates superior performance while challenging prevailing...

LINE: Public-Key Encryption

We propose a public key encryption cryptosystem based on solutions of linear equation systems with predefinition of input parameters through shared secret computation for factorizable substitutions. The existence of multiple equivalent solutions for an underdetermined system of linear equations...

Invariant-Based Robust Weights Watermark for Large Language Models

Watermarking technology has gained significant attention due to the increasing importance of intellectual property IP rights, particularly with the growing deployment of large language models LLMs on billions resource-constrained edge devices. To counter the potential threats of IP theft by...

Approximating Euler Totient Function Using Linear Regression on RSA Moduli

The security of the RSA cryptosystem is based on the intractability of computing Euler's totient function phin for large integers n. Although deriving phin deterministically remains computationally infeasible for cryptographically relevant bit lengths, and machine learning presents a promising...

An Efficient Construction of Raz's Two-Source Randomness Extractor with Improved Parameters

Randomness extractors are algorithms that distill weak random sources into near-perfect random numbers. Two-source extractors enable this distillation process by combining two independent weak random sources. Raz's extractor STOC '05 was the first to achieve this in a setting where one source has...

Linear and Numerical SDoF Bounds of Active RIS-Assisted MIMO Wiretap Interference Channel

The multiple-input multiple-output MIMO wiretap interference channel IC serves as a canonical model for information-theoretic security, where a multiple-antenna eavesdropper attempts to intercept communications in a two-user MIMO IC system. The secure degrees-of-freedom SDoF of an active...

On Key Exchange Protocol Based on Two-Side Multiplication Action

We present a cryptanalysis of a key exchange protocol based on the digital semiring. For this purpose, we find the maximal solution of a linear system over such semiring, and use the properties of circulant matrix to demonstrate that the protocol is vulnerable. Specifically, we provide an efficie...

Astra Linux – Vulnerability found in Linux 6.1, Linux 6.12

In the Linux kernel, the following vulnerabilities have been resolved: ipvlan: Ensure that the network headers are in the skb linear part. syzbot found that ipvlanprocessv6outbound assumes that the IPv6 network header isis present in skb-head 1. Add the necessary pskbnetworkmaypull calls for both...

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning

Machine-learning systems continue to advance at a rapid pace, demonstrating remarkable utility in various fields and disciplines. As these systems continue to grow in size and complexity, a nascent industry is emerging which aims to bring machine-learning-as-a-service MLaaS to market. Outsourcing...

firefox: thunderbird: Out-of-bounds access when optimizing linear sums

A flaw was found in Firefox and Thunderbird. The Mozilla Foundation's Security Advisory describes the following issue: An attacker could perform an out-of-bounds read or write on a JavaScript object by confusing array index sizes...

AlphaSteer: Learning Refusal Steering with Principled Null-Space Constraint

As LLMs are increasingly deployed in real-world applications, ensuring their ability to refuse malicious prompts, especially jailbreak attacks, is essential for safe and reliable use. Recently, activation steering has emerged as an effective approach for enhancing LLM safety by adding a refusal...

Privacy Amplification through Synthetic Data: Insights from Linear Regression

Synthetic data inherits the differential privacy guarantees of the model used to generate it. Additionally, synthetic data may benefit from privacy amplification when the generative model is kept hidden. While empirical studies suggest this phenomenon, a rigorous theoretical understanding is stil...

firefox: thunderbird: Out-of-bounds access when optimizing linear sums

A flaw was found in Firefox and Thunderbird. The Mozilla Foundation's Security Advisory describes the following issue: An attacker could perform an out-of-bounds read or write on a JavaScript object by confusing array index sizes...

Important: Red Hat Security Advisory: firefox security update

An update for firefox is now available for Red Hat Enterprise Linux 9.2 Extended Update Support. Red Hat Product Security has rated this update as having a security impact of Important. A Common Vulnerability Scoring System CVSS base score, which gives a detailed severity rating, is available for...

Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor

Estimating the density of a distribution from its samples is a fundamental problem in statistics. Hypothesis selection addresses the setting where, in addition to a sample set, we are given $n$ candidate distributions -- referred to as hypotheses -- and the goal is to determine which one best...

Robust and Verifiable MPC with Applications to Linear Machine Learning Inference

In this work, we present an efficient secure multi-party computation MPC protocol that provides strong security guarantees in settings with dishonest majority of participants who may behave arbitrarily. Unlike the popular MPC implementation known as SPDZ Crypto '12, which only ensures security wi...

Hush! Protecting Secrets during Model Training: an Indistinguishability Approach

We consider the problem of secret protection, in which a business or organization wishes to train a model on their own data, while attempting to not leak secrets potentially contained in that data via the model. The standard method for training models to avoid memorization of secret information i...

LPASS: Linear Probes As Stepping Stones for Vulnerability Detection Using Compressed LLMs

Large Language Models LLMs are being extensively used for cybersecurity purposes. One of them is the detection of vulnerable codes. For the sake of efficiency and effectiveness, compression and fine-tuning techniques are being developed, respectively. However, they involve spending substantial...