WebGuard++: Interpretable Malicious URL Detection Via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT

URL+HTML feature fusion shows promise for robust malicious URL detection, since attacker artifacts persist in DOM structures. However, prior work suffers from four critical shortcomings: 1 incomplete URL modeling, failing to jointly capture lexical patterns and semantic context; 2 HTML graph...

The Impact of the Russia-Ukraine Conflict on the Cloud Computing Risk Landscape

The Russian invasion of Ukraine has fundamentally altered the information technology IT risk landscape, particularly in cloud computing environments. This paper examines how this geopolitical conflict has accelerated data sovereignty concerns, transformed cybersecurity paradigms, and reshaped clo...

A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures

In recent years, Large-Language-Model-driven AI agents have exhibited unprecedented intelligence, flexibility, and adaptability, and are rapidly changing human production and lifestyle. Nowadays, agents are undergoing a new round of evolution. They no longer act as an isolated island like LLMs...

RepuNet: a Reputation System for Mitigating Malicious Clients in DFL

Decentralized Federated Learning DFL enables nodes to collaboratively train models without a central server, introducing new vulnerabilities since each node independently selects peers for model aggregation. Malicious nodes may exploit this autonomy by sending corrupted models model poisoning,...

Yealink RPS Information Disclosure / Man-In-The-Middle

Yealink RPS contains several vulnerabilities that can lead to leaking of PII and/or man-in-the-middle attacks. Some vulnerabilities remain unpatched even after disclosure to the manufacturer...

Evaluating Disassembly Errors with Only Binaries

Disassemblers are crucial in the analysis and modification of binaries. Existing works showing disassembler errors largely rely on practical implementation without specific guarantees and assume source code and compiler toolchains to evaluate ground truth. However, the assumption of source code i...

Secure Multi-Key Homomorphic Encryption with Application to Privacy-Preserving Federated Learning

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Machine Learning with Privacy for Protected Attributes

Differential privacy DP has become the standard for private data analysis. Certain machine learning applications only require privacy protection for specific protected attributes. Using naive variants of differential privacy in such use cases can result in unnecessary degradation of utility. In...

Quest KACE Systems Management Appliance 14.1 Unauthenticated Backup Upload

Seralys Security Advisory - Quest KACE SMA allows unauthenticated users to upload backup files to the system. While signature validation is implemented, weaknesses in the validation process can be exploited to upload malicious backup content that could compromise system integrity. Version 14.1 is...

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017

Identifying suitable machine learning paradigms for intrusion detection remains critical for building effective and generalizable security solutions. In this study, we present a controlled comparison of four representative models - Multi-Layer Perceptron MLP, 1D Convolutional Neural Network CNN,...

Accurate BGV Parameters Selection: Accounting for Secret and Public Key Dependencies in Average-Case Analysis

The Brakerski-Gentry-Vaikuntanathan BGV scheme is one of the most significant fully homomorphic encryption FHE schemes. It belongs to a class of FHE schemes whose security is based on the presumed intractability of the Learning with Errors LWE problem and its ring variant RLWE. Such schemes deal...

Enhancing Security in LLM Applications: a Performance Evaluation of Early Detection Systems

Prompt injection threatens novel applications that emerge from adapting LLMs for various user tasks. The newly developed LLM-based software applications become more ubiquitous and diverse. However, the threat of prompt injection attacks undermines the security of these systems as the mitigation a...

HARPT: a Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps

We present HARPT, a large-scale annotated corpus of mobile health app store reviews aimed at advancing research in user privacy and trust. The dataset comprises over 480,000 user reviews labeled into seven categories that capture critical aspects of trust in applications, trust in providers and...

DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?

Adversarial examples are small and often imperceptible perturbations crafted to fool machine learning models. These attacks seriously threaten the reliability of deep neural networks, especially in security-sensitive domains. Evasion attacks, a form of adversarial attack where input is modified a...

Automatic Selection of Protections to Mitigate Risks against Software Applications

This paper introduces a novel approach for the automated selection of software protections to mitigate MATE risks against critical assets within software applications. We formalize the key elements involved in protection decision-making - including code artifacts, assets, security requirements,...

Cellular Automata As Generators of Interleaving Sequences

An interleaving sequence is obtained by combining or intertwining elements from two or more sequences. On the other hand, cellular automata are known to be generators for keystream sequences. In this paper we present two families of one-dimensional cellular automata as generators of interleaving...

Security Assessment of DeepSeek and GPT Series Models against Jailbreak Attacks

The widespread deployment of large language models LLMs has raised critical concerns over their vulnerability to jailbreak attacks, i.e., adversarial prompts that bypass alignment mechanisms and elicit harmful or policy-violating outputs. While proprietary models like GPT-4 have undergone extensi...

Design High-Confidence Computers Using Trusted Instructional Set Architecture and Emulators

High-confidence computing relies on trusted instructional set architecture, sealed kernels, and secure operating systems. Cloud computing depends on trusted systems for virtualization tasks. Branch predictions and pipelines are essential in improving performance of a CPU/GPU. But Spectre and...

Private Model Personalization Revisited

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Understanding the Theoretical Guarantees of DPM

In this study, we conducted an in-depth examination of the utility analysis of the differentially private mechanism DPM. The authors of DPM have already established the probability of a good split being selected and of DPM halting. In this study, we expanded the analysis of the stopping criterion...

VideoMark: a Distortion-Free Robust Watermarking Framework for Video Diffusion Models

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Adaptive Alert Prioritisation in Security Operations Centres Via Learning to Defer with Human Feedback

Alert prioritisation AP is crucial for security operations centres SOCs to manage the overwhelming volume of alerts and ensure timely detection and response to genuine threats, while minimising alert fatigue. Although predictive AI can process large alert volumes and identify known patterns, it...

Vulnerability Assessment Combining CVSS Temporal Metrics and Bayesian Networks

Vulnerability assessment is a critical challenge in cybersecurity, particularly in industrial environments. This work presents an innovative approach by incorporating the temporal dimension into vulnerability assessment, an aspect neglected in existing literature. Specifically, this paper focuses...

Deep CNN Face Matchers Inherently Support Revocable Biometric Templates

One common critique of biometric authentication is that if an individual's biometric is compromised, then the individual has no recourse. The concept of revocable biometrics was developed to address this concern. A biometric scheme is revocable if an individual can have their current enrollment i...

Stegano 2.0.0

Stegano is a basic Python Steganography module. Stegano implements two methods of hiding: using the red portion of a pixel to hide ASCII messages, and using the Least Significant Bit LSB technique. It is possible to use a more advanced LSB method based on integers sets. The sets Sieve of...

Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models

Are there any conditions under which a generative model's outputs are guaranteed not to infringe the copyrights of its training data? This is the question of "provable copyright protection" first posed by Vyas, Kakade, and Barak ICML 2023. They define near access-freeness NAF and propose it as...

Amplifying Machine Learning Attacks through Strategic Compositions

Machine learning ML models are proving to be vulnerable to a variety of attacks that allow the adversary to learn sensitive information, cause mispredictions, and more. While these attacks have been extensively studied, current research predominantly focuses on analyzing each attack type...

Faydam Datalogger 2.7.0 SQL Injection

Faydam Datalogger versions 2.7.0 and below suffer from a remote SQL injection vulnerability...

A Comparative Study and Implementation of Key Derivation Functions Standardized by NIST and IEEE

Since many applications and services require pseudorandom numbers PRNs, it is feasible to generate specific PRNs under given key values and input messages using Key Derivation Functions KDFs. These KDFs are primarily constructed based on Message Authentication Codes MACs, where the MAC serves as ...

Intelligent ARP Spoofing Detection Using Multi-Layered Machine Learning (ML) Techniques for IoT Networks

Address Resolution Protocol ARP spoofing remains a critical threat to IoT networks, enabling attackers to intercept, modify, or disrupt data transmission by exploiting ARP's lack of authentication. The decentralized and resource-constrained nature of IoT environments amplifies this vulnerability,...

FORGE: an LLM-Driven Framework for Large-Scale Smart Contract Vulnerability Dataset Construction

High-quality smart contract vulnerability datasets are critical for evaluating security tools and advancing smart contract security research. Two major limitations of current manual dataset construction are 1 labor-intensive and error-prone annotation processes limiting the scale, quality, and...

Network Structures As an Attack Surface: Topology-Based Privacy Leakage in Federated Learning

Federated learning systems increasingly rely on diverse network topologies to address scalability and organizational constraints. While existing privacy research focuses on gradient-based attacks, the privacy implications of network topology knowledge remain critically understudied. We conduct th...

Physical Layer Challenge-Response Authentication between Ambient Backscatter Devices

Ambient backscatter communication AmBC has become an integral part of ubiquitous Internet of Things IoT applications due to its energy-harvesting capabilities and ultra-low-power consumption. However, the open wireless environment exposes AmBC systems to various attacks, and existing authenticati...

Towards Provable (In)Secure Model Weight Release Schemes

Recent secure weight release schemes claim to enable open-source model distribution while protecting model ownership and preventing misuse. However, these approaches lack rigorous security foundations and provide only informal security guarantees. Inspired by established works in cryptography, we...

Versatile and Fast Location-Based Private Information Retrieval with Fully Homomorphic Encryption over the Torus

Location-based services often require users to share sensitive locational data, raising privacy concerns due to potential misuse or exploitation by untrusted servers. In response, we present VeLoPIR, a versatile location-based private information retrieval PIR system designed to preserve user...

Real-Time Agile Software Management for Edge and Fog Computing Based Smart City Infrastructure

The evolution of smart cities demands scalable, secure, and energy-efficient architectures for real-time data processing. With the number of IoT devices expected to exceed 40 billion by 2030, traditional cloud-based systems are increasingly constrained by bandwidth, latency, and energy limitation...

Open Source, Open Threats? Investigating Security Challenges in Open-Source Software

Open-source software OSS has become increasingly more popular across different domains. However, this rapid development and widespread adoption come with a security cost. The growing complexity and openness of OSS ecosystems have led to increased exposure to vulnerabilities and attack surfaces...

LLM-Based Dynamic Differential Testing for Database Connectors with Reinforcement Learning-Guided Prompt Selection

Database connectors are critical components enabling applications to interact with underlying database management systems DBMS, yet their security vulnerabilities often remain overlooked. Unlike traditional software defects, connector vulnerabilities exhibit subtle behavioral patterns and are...

Today'S Cat Is Tomorrow'S Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches

Vulnerability datasets used for ML testing implicitly contain retrospective information. When tested on the field, one can only use the labels available at the time of training and testing e.g. seen and assumed negatives. As vulnerabilities are discovered across calendar time, labels change and...

Information-Theoretic Estimation of the Risk of Privacy Leaks

Recent work\citeLiu2016 has shown that dependencies between items in a dataset can lead to privacy leaks. We extend this concept to privacy-preserving transformations, considering a broader set of dependencies captured by correlation metrics. Specifically, we measure the correlation between the...

Self-Stabilizing Replicated State Machine Coping with Byzantine and Recurring Transient Faults

Whitepaper called Self-Stabilizing Replicated State Machine Coping With Byzantine And Recurring Transient Faults...

Exploiting AI for Attacks: on the Interplay between Adversarial AI and Offensive AI

As Artificial Intelligence AI continues to evolve, it has transitioned from a research-focused discipline to a widely adopted technology, enabling intelligent solutions across various sectors. In security, AI's role in strengthening organizational resilience has been studied for over two decades...

Lessons for Cybersecurity from the American Public Health System

The United States needs national institutions and frameworks to systematically collect cybersecurity data, measure outcomes, and coordinate responses across government and private sectors, similar to how public health systems track and address disease outbreaks...

DTHA: a Digital Twin-Assisted Handover Authentication Scheme for 5G and Beyond

With the rapid development and extensive deployment of the fifth-generation wireless system 5G, it has achieved ubiquitous high-speed connectivity and improved overall communication performance. Additionally, as one of the promising technologies for integration beyond 5G, digital twin in cyberspa...

A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis

Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed fo...

A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method

An Advanced Persistent Threat APT is a multistage, highly sophisticated, and covert form of cyber threat that gains unauthorized access to networks to either steal valuable data or disrupt the targeted network. These threats often remain undetected for extended periods, emphasizing the critical...

On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification

What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this work, we investigate this question in the setting of high-dimensional binary classification, where statistical effects due to limited data availability play a...

LURK-T: Limited Use of Remote Keys with Added Trust in TLS 1.3

In many web applications, such as Content Delivery Networks CDNs, TLS credentials are shared, e.g., between the website's TLS origin server and the CDN's edge servers, which can be distributed around the globe. To enhance the security and trust for TLS 1.3 in such scenarios, we propose LURK-T, a...

Quantum Machine Learning

The meteoric rise of artificial intelligence in recent years has seen machine learning methods become ubiquitous in modern science, technology, and industry. Concurrently, the emergence of programmable quantum computers, coupled with the expectation that large-scale fault-tolerant machines will...

Semantic Preprocessing for LLM-Based Malware Analysis

In a context of malware analysis, numerous approaches rely on Artificial Intelligence to handle a large volume of data. However, these techniques focus on data view images, sequences and not on an expert's view. Noticing this issue, we propose a preprocessing that focuses on expert knowledge to...