Quantum Lifting for Invertible Permutations and Ideal Ciphers

In this work, we derive the first lifting theorems for establishing security in the quantum random permutation and ideal cipher models. These theorems relate the success probability of an arbitrary quantum adversary to that of a classical algorithm making only a small number of classical queries...

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation

Large Language Models LLM are typically trained on vast amounts of data from various sources. Even when designed modularly e.g., Mixture-of-Experts, LLMs can leak privacy on their sources. Conversely, training such models in isolation arguably prohibits generalization. To this end, we propose a...

SoK: Timeline Based Event Reconstruction for Digital Forensics: Terminology, Methodology, and Current Challenges

Event reconstruction is a technique that examiners can use to attempt to infer past activities by analyzing digital artifacts. Despite its significance, the field suffers from fragmented research, with studies often focusing narrowly on aspects like timeline creation or tampering detection. This...

Automating Function-Level TARA for Automotive Full-Lifecycle Security

As modern vehicles evolve into intelligent and connected systems, their growing complexity introduces significant cybersecurity risks. Threat Analysis and Risk Assessment TARA has therefore become essential for managing these risks under mandatory regulations. However, existing TARA automation...

Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications

This work empirically evaluates machine learning models on two imbalanced public datasets KDDCUP99 and Credit Card Fraud 2013. The method includes data preparation, model training, and evaluation, using an 80/20 train/test split. Models tested include eXtreme Gradient Boosting XGB, Multi Layer...

A Gradient-Optimized TSK Fuzzy Framework for Explainable Phishing Detection

Phishing attacks represent an increasingly sophisticated and pervasive threat to individuals and organizations, causing significant financial losses, identity theft, and severe damage to institutional reputations. Existing phishing detection methods often struggle to simultaneously achieve high...

ECG Identity Authentication in Open-Set with Multi-Model Pretraining and Self-Constraint Center and Irrelevant Sample Repulsion Learning

Electrocardiogram ECG signal exhibits inherent uniqueness, making it a promising biometric modality for identity authentication. As a result, ECG authentication has gained increasing attention in recent years. However, most existing methods focus primarily on improving authentication accuracy...

PICO: Secure Transformers Via Robust Prompt Isolation and Cybersecurity Oversight

We propose a robust transformer architecture designed to prevent prompt injection attacks and ensure secure, reliable response generation. Our PICO Prompt Isolation and Cybersecurity Oversight framework structurally separates trusted system instructions from untrusted user inputs through dual...

LightDSA: a Python-Based Hybrid Digital Signature Library and Performance Analysis of RSA, DSA, ECDSA and EdDSA in Variable Configurations, Elliptic Curve Forms and Curves

Digital signature algorithms DSAs are fundamental to cryptographic security, ensuring data integrity and authentication. While RSA, DSA, ECDSA, and EdDSA are widely used, their performance varies significantly depending on key sizes, hash functions, and elliptic curve configurations. In this pape...

Faraday 5.13.0

Faraday is a tool that introduces a new concept called IPE, or Integrated Penetration-Test Environment. It is a multiuser penetration test IDE designed for distribution, indexation and analysis of the generated data during the process of a security audit. The main purpose of Faraday is to re-use...

ThreMoLIA: Threat Modeling of Large Language Model-Integrated Applications

Large Language Models LLMs are currently being integrated into industrial software applications to help users perform more complex tasks in less time. However, these LLM-Integrated Applications LIA expand the attack surface and introduce new kinds of threats. Threat modeling is commonly used to...

TSCL:Multi-Party Loss Balancing Scheme for Deep Learning Image Steganography Based on Curriculum Learning

For deep learning-based image steganography frameworks, in order to ensure the invisibility and recoverability of the information embedding, the loss function usually contains several losses such as embedding loss, recovery loss and steganalysis loss. In previous research works, fixed loss weight...

Gladinet CentreStack 16.1.10296.56315 Remote Code Execution

Proof of concept exploit for Gladinet CentreStack versions up to 16.1.10296.56315. This software has a deserialization vulnerability due to the CentreStack portal's hardcoded machineKey use, as exploited in the wild in March 2025. This issue is fixed in version 16.4.10315.56368...

WordPress Verification SMS with TargetSMS 1.5 Remote Code Execution

WordPress Verification SMS with TargetSMS plugin versions 1.5 and below suffer from a logic flaw that allow for remote code execution...

Commvault Command Center Innovation Release 11.38 Remote Code Execution

Commvault Command Center Innovation Release version 11.38 proof of concept pre-authentication remote code execution exploit...

Bighuge BLS OSINT Tool 2.1.0 Local Privilege Escalation

Bighuge BLS OSINT Tool BBOT version 2.1.0 is susceptible to a local privilege escalation issue when a malicious module is used. Proof of concept module included...

AI-Based Vulnerability Analysis of NFT Smart Contracts

With the rapid growth of the NFT market, the security of smart contracts has become crucial. However, existing AI-based detection models for NFT contract vulnerabilities remain limited due to their complexity, while traditional manual methods are time-consuming and costly. This study proposes an...

Quantum Autoencoder for Multivariate Time Series Anomaly Detection

Anomaly Detection AD defines the task of identifying observations or events that deviate from typical - or normal - patterns, a critical capability in IT security for recognizing incidents such as system misconfigurations, malware infections, or cyberattacks. In enterprise environments like SAP...

A Comment on "E-PoS: Making PoS Decentralized and Fair"

Proof-of-Stake PoS is a prominent Sybil control mechanism for blockchain-based systems. In "e-PoS: Making PoS Decentralized and Fair," Saad et al. TPDS'21 introduced a new Proof-of-Stake protocol, e-PoS, to enhance PoS applications' decentralization and fairness. In this comment paper, we address...

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations

This paper explores the vulnerability of machine learning models to simple single-feature adversarial attacks in the context of Ethereum fraudulent transaction detection. Through comprehensive experimentation, we investigate the impact of various adversarial attack strategies on model performance...

A Systematic Study on the Design of Odd-Sized Highly Nonlinear Boolean Functions Via Evolutionary Algorithms

This paper focuses on the problem of evolving Boolean functions of odd sizes with high nonlinearity, a property of cryptographic relevance. Despite its simple formulation, this problem turns out to be remarkably difficult. We perform a systematic evaluation by considering three solution encodings...

Near-Term Pseudorandom and Pseudoresource Quantum States

Whitepaper called Near-Term Pseudorandom And Pseudoresource Quantum States...

Crypto-NcRNA: Non-Coding RNA (NcRNA) Based Encryption Algorithm

In the looming post-quantum era, traditional cryptographic systems are increasingly vulnerable to quantum computing attacks that can compromise their mathematical foundations. To address this critical challenge, we propose crypto-ncRNA-a bio-convergent cryptographic framework that leverages the...

Silenzio: Secure Non-Interactive Outsourced MLP Training

Outsourcing the ML training to cloud providers presents a compelling opportunity for resource constrained clients, while it simultaneously bears inherent privacy risks, especially for highly sensitive training data. We introduce Silenzio, the first fully non-interactive outsourcing scheme for the...

Identity Control Plane: the Unifying Layer for Zero Trust Infrastructure

This paper introduces the Identity Control Plane ICP, an architectural framework for enforcing identity-aware Zero Trust access across human users, workloads, and automation systems. The ICP model unifies SPIFFE-based workload identity, OIDC/SAML user identity, and scoped automation credentials v...

User Profiles: the Achilles' Heel of Web Browsers

Web browsers provide the security foundation for our online experiences. Significant research has been done into the security of browsers themselves, but relatively little investigation has been done into how they interact with the operating system or the file system. In this work, we provide the...

Biting the CHERI Bullet: Blockers, Enablers and Security Implications of CHERI in Defence

There is growing interest in securing the hardware foundations software stacks build upon. However, before making any investment decision, software and hardware supply chain stakeholders require evidence from realistic, multiple long-term studies of adoption. We present results from a 12 month...

Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction

With the rapid digitalization of healthcare systems, there has been a substantial increase in the generation and sharing of private health data. Safeguarding patient information is essential for maintaining consumer trust and ensuring compliance with legal data protection regulations. Machine...

Cluster-Aware Attacks on Graph Watermarks

Data from domains such as social networks, healthcare, finance, and cybersecurity can be represented as graph-structured information. Given the sensitive nature of this data and their frequent distribution among collaborators, ensuring secure and attributable sharing is essential. Graph...

Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions

Phishing detection on Ethereum has increasingly leveraged advanced machine learning techniques to identify fraudulent transactions. However, limited attention has been given to understanding the effectiveness of feature selection strategies and the role of graph-based models in enhancing detectio...

Proof of Useful Intelligence (PoUI): Blockchain Consensus beyond Energy Waste

Blockchain technology enables secure, transparent data management in decentralized systems, supporting applications from cryptocurrencies like Bitcoin to tokenizing real-world assets like property. Its scalability and sustainability hinge on consensus mechanisms balancing security and efficiency...

Optimizing the Privacy-Utility Balance Using Synthetic Data and Configurable Perturbation Pipelines

This paper explores the strategic use of modern synthetic data generation and advanced data perturbation techniques to enhance security, maintain analytical utility, and improve operational efficiency when managing large datasets, with a particular focus on the Banking, Financial Services, and...

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings

The rapid evolution of malware variants requires robust classification methods to enhance cybersecurity. While Large Language Models LLMs offer potential for generating malware descriptions to aid family classification, their utility is limited by semantic embedding overlaps and misalignment with...

From Randomized Response to Randomized Index: Answering Subset Counting Queries with Local Differential Privacy

Local Differential Privacy LDP is the predominant privacy model for safeguarding individual data privacy. Existing perturbation mechanisms typically require perturbing the original values to ensure acceptable privacy, which inevitably results in value distortion and utility deterioration. In this...

"Shifting Access Control Left" Using Asset and Goal Models

Access control needs have broad design implications, but access control specifications may be elicited before, during, or after these needs are captured. Because access control knowledge is distributed, we need to make knowledge asymmetries more transparent, and use expertise already available to...

Apple Security Advisory 04-16-2025-1

Apple Security Advisory 04-16-2025-1 - iOS 18.4.1 and iPadOS 18.4.1 addresses bypass and code execution vulnerabilities...

Apple Security Advisory 04-16-2025-2

Apple Security Advisory 04-16-2025-2 - macOS Sequoia 15.4.1 addresses bypass and code execution vulnerabilities...

Apple Security Advisory 04-16-2025-3

Apple Security Advisory 04-16-2025-3 - tvOS 18.4.1 addresses bypass and code execution vulnerabilities...

Apple Security Advisory 04-16-2025-4

Apple Security Advisory 04-16-2025-4 - visionOS 2.4.1 addresses bypass and code execution vulnerabilities...

Avoiding Leakage Poisoning: Concept Interventions under Distribution Shifts

In this paper, we investigate how concept-based models CMs respond to out-of-distribution OOD inputs. CMs are interpretable neural architectures that first predict a set of high-level concepts e.g., stripes, black and then predict a task label from those concepts. In particular, we study the impa...

Contrastive Learning for Continuous Touch-Based Authentication

Smart mobile devices have become indispensable in modern daily life, where sensitive information is frequently processed, stored, and transmitted-posing critical demands for robust security controls. Given that touchscreens are the primary medium for human-device interaction, continuous user...

Secured Encryption Scheme Based on the Ree Groups

An improved design of a cryptosystem based on small Ree groups is proposed. We have changed the encryption algorithm and propose to use a logarithmic signature for the entire Ree group. This approach improves security against sequential key recovery attacks. Hence, the complexity of the key...

Optimized Approaches to Malware Detection: a Study of Machine Learning and Deep Learning Techniques

Digital systems find it challenging to keep up with cybersecurity threats. The daily emergence of more than 560,000 new malware strains poses significant hazards to the digital ecosystem. The traditional malware detection methods fail to operate properly and yield high false positive rates with l...

STCL: Curriculum Learning Strategies for Deep Learning Image Steganography Models

Whitepaper called STCL: Curriculum Learning Strategies For Deep Learning Image Steganography Models...

Linux Kernel 6.6.75 Vsock Use-After-Free

Linux kernel version 6.6.75 vsock use-after free proof of concept exploit...

WordPress WP Remote Thumbnail 1.3.2 Shell Upload

WordPress WP Remote Thumbnail plugin versions 1.3.2 and below suffer from a remote shell upload vulnerability...

CheatAgent: Attacking LLM-Empowered Recommender Systems Via LLM Agent

Recently, Large Language Model LLM-empowered recommender systems RecSys have brought significant advances in personalized user experience and have attracted considerable attention. Despite the impressive progress, the research question regarding the safety vulnerability of LLM-empowered RecSys...

AiXamine: Simplified LLM Safety and Security

Evaluating Large Language Models LLMs for safety and security remains a complex task, often requiring users to navigate a fragmented landscape of ad hoc benchmarks, datasets, metrics, and reporting formats. To address this challenge, we present aiXamine, a comprehensive black-box evaluation...

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding

Variational Autoencoders VAEs have played a key role in scaling up diffusion-based generative models, as in Stable Diffusion, yet questions regarding their robustness remain largely underexplored. Although adversarial training has been an established technique for enhancing robustness in predicti...

Breaking the Flow and the Bank: Stealthy Cyberattacks on Water Network Hydraulics

As water distribution networks WDNs become increasingly connected with digital infrastructures, they face greater exposure to cyberattacks that threaten their operational integrity. Stealthy False Data Injection Attacks SFDIAs are particularly concerning, as they manipulate sensor data to...