Adapting Novelty Towards Generating Antigens for Antivirus Systems

It is well known that anti-malware scanners depend on malware signatures to identify malware. However, even minor modifications to malware code structure results in a change in the malware signature thus enabling the variant to evade detection by scanners. Therefore, there exists the need for a...

LAMDA: a Longitudinal Android Malware Benchmark for Concept Drift Analysis

Machine learning ML-based malware detection systems often fail to account for the dynamic nature of real-world training and test data distributions. In practice, these distributions evolve due to frequent changes in the Android ecosystem, adversarial development of new malware families, and the...

A Study of Semi-Fungible Token Based Wi-Fi Access Control

Current Wi-Fi authentication methods face issues such as insufficient security, user privacy leakage, high management costs, and difficulty in billing. To address these challenges, a Wi-Fi access control solution based on blockchain smart contracts is proposed. Firstly, semi-fungible Wi-Fi tokens...

Benchmarking Poisoning Attacks against Retrieval-Augmented Generation

Retrieval-Augmented Generation RAG has proven effective in mitigating hallucinations in large language models by incorporating external knowledge during inference. However, this integration introduces new security vulnerabilities, particularly to poisoning attacks. Although prior work has explore...

LLM-Driven APT Detection for 6G Wireless Networks: a Systematic Review and Taxonomy

Sixth Generation 6G wireless networks, which are expected to be deployed in the 2030s, have already created great excitement in academia and the private sector with their extremely high communication speed and low latency rates. However, despite the ultra-low latency, high throughput, and...

Anonymity-Washing

Anonymization is a foundational principle of data privacy regulation, yet its practical application remains riddled with ambiguity and inconsistency. This paper introduces the concept of anonymity-washing -- the misrepresentation of the anonymity level of sanitized'' personal data -- as a critica...

Understanding the Relationship between Personal Data Privacy Literacy and Data Privacy Information Sharing by University Students

With constant threats to the safety of personal data in the United States, privacy literacy has become an increasingly important competency among university students, one that ties intimately to the information sharing behavior of these students. This survey based study examines how university...

Securing Credit Inquiries: the Role of Real-Time User Approval in Preventing SSN Identity Theft

Unauthorized credit inquiries are also a central entry point for identity theft, with Social Security Numbers SSNs being widely utilized in fraudulent cases. Traditional credit inquiry systems do not usually possess strict user authentication, making them vulnerable to unauthorized access. This...

Co-PatcheR: Collaborative Software Patching with Component(S)-Specific Small Reasoning Models

Motivated by the success of general-purpose large language models LLMs in software patching, recent works started to train specialized patching models. Most works trained one model to handle the end-to-end patching pipeline including issue localization, patch generation, and patch validation...

Toward Malicious Clients Detection in Federated Learning

Federated learning FL enables multiple clients to collaboratively train a global machine learning model without sharing their raw data. However, the decentralized nature of FL introduces vulnerabilities, particularly to poisoning attacks, where malicious clients manipulate their local models to...

MADCAT: Combating Malware Detection under Concept Drift with Test-Time Adaptation

We present MADCAT, a self-supervised approach designed to address the concept drift problem in malware detection. MADCAT employs an encoder-decoder architecture and works by test-time training of the encoder on a small, balanced subset of the test-time data using a self-supervised objective. Duri...

Gaming Tool Preferences in Agentic LLMs

Large language models LLMs can now access a wide range of external tools, thanks to the Model Context Protocol MCP. This greatly expands their abilities as various agents. However, LLMs rely entirely on the text descriptions of tools to decide which ones to use--a process that is surprisingly...

Verifiable Deep Learning Inference on an Untrusted Party

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Gentoo Linux Security Advisory 201607-07

Gentoo Linux Security Advisory 201607-07 - Multiple vulnerabilities have been found in the Chromium web browser, the worst of which allows remote attackers to execute arbitrary code. Versions less than 51.0.2704.103 are affected...

Towards Anonymous Neural Network Inference

We introduce funion, a system providing end-to-end sender-receiver unlinkability for neural network inference. By leveraging the Pigeonhole storage protocol and BACAP blinding-and-capability scheme from the Echomix anonymity system, funion inherits the provable security guarantees of modern...

A Critical Evaluation of Defenses against Prompt Injection Attacks

Large Language Models LLMs are vulnerable to prompt injection attacks, and several defenses have recently been proposed, often claiming to mitigate these attacks successfully. However, we argue that existing studies lack a principled approach to evaluating these defenses. In this paper, we argue...

EtherBee: a Global Dataset of Ethereum Node Performance Measurements Coupled with Honeypot Interactions and Full Network Sessions

We introduce EtherBee, a global dataset integrating detailed Ethereum node metrics, network traffic metadata, and honeypot interaction logs collected from ten geographically diverse vantage points over three months. By correlating node data with granular network sessions and security events,...

Modeling Interdependent Privacy Threats

The rise of online social networks, user-gene-rated content, and third-party apps made data sharing an inevitable trend, driven by both user behavior and the commercial value of personal information. As service providers amass vast amounts of data, safeguarding individual privacy has become...

Adaptively Secure Distributed Broadcast Encryption with Linear-Size Public Parameters

Distributed broadcast encryption DBE is a variant of broadcast encryption BE that can efficiently transmit a message to a subset of users, in which users independently generate user private keys and user public keys instead of a central trusted authority generating user keys. In this paper, we...

Gentoo Linux Security Advisory 200506-20

Gentoo Linux Security Advisory 200506-20 - Cacti is vulnerable to several SQL injection, authentication bypass and file inclusion vulnerabilities. Versions less than 0.8.6f are affected...

Towards a Quantum-Classical Augmented Network

In the past decade, several small-scale quantum key distribution networks have been established. However, the deployment of large-scale quantum networks depends on the development of quantum repeaters, quantum channels, quantum memories, and quantum network protocols. To improve the security of...

One Model Transfer to All: on Robust Jailbreak Prompts Generation against LLMs

Safety alignment in large language models LLMs is increasingly compromised by jailbreak attacks, which can manipulate these models to generate harmful or unintended content. Investigating these attacks is crucial for uncovering model vulnerabilities. However, many existing jailbreak strategies fa...

SecurePay: Enabling Secure and Fast Payment Processing for Platform Economy

Recent years have witnessed a rapid development of platform economy, as it effectively addresses the trust dilemma between untrusted online buyers and merchants. However, malicious platforms can misuse users' funds and information, causing severe security concerns. Previous research efforts aimed...

TSA-WF: Exploring the Effectiveness of Time Series Analysis for Website Fingerprinting

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Gentoo Linux Security Advisory 201908-26

Gentoo Linux Security Advisory 201908-26 - Multiple vulnerabilities have been found in libofx, the worst of which could result in the arbitrary execution of code. Versions less than 0.9.14 are affected...

Privacy-Preserving Bathroom Monitoring for Elderly Emergencies Using PIR and LiDAR Sensors

In-home elderly monitoring requires systems that can detect emergency events - such as falls or prolonged inactivity - while preserving privacy and requiring no user input. These systems must be embedded into the surrounding environment, capable of capturing activity, and responding promptly. Thi...

Sec5GLoc: Securing 5G Indoor Localization Via Adversary-Resilient Deep Learning Architecture

Emerging 5G millimeter-wave and sub-6 GHz networks enable high-accuracy indoor localization, but security and privacy vulnerabilities pose serious challenges. In this paper, we identify and address threats including location spoofing and adversarial signal manipulation against 5G-based indoor...

Invisible Tokens, Visible Bills: the Urgent Need to Audit Hidden Operations in Opaque LLM Services

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A Linear Approach to Data Poisoning

We investigate the theoretical foundations of data poisoning attacks in machine learning models. Our analysis reveals that the Hessian with respect to the input serves as a diagnostic tool for detecting poisoning, exhibiting spectral signatures that characterize compromised datasets. We use rando...

Gentoo Linux Security Advisory 201908-27

Gentoo Linux Security Advisory 201908-27 - A vulnerability in Nautilus may allow attackers to escape the sandbox. Versions less than 3.30.5-r1 are affected...

Gentoo Linux Security Advisory 201607-05

Gentoo Linux Security Advisory 201607-05 - Multiple vulnerabilities have been found in Cacti, the worst of which could lead to the remote execution of arbitrary code. Versions less than 0.8.8h are affected...

Finetuning-Activated Backdoors in LLMs

Finetuning openly accessible Large Language Models LLMs has become standard practice for achieving task-specific performance improvements. Until now, finetuning has been regarded as a controlled and secure process in which training on benign datasets led to predictable behaviors. In this paper, w...

JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models

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An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs

Recent advances in Large Language Models LLMs have led to the widespread adoption of third-party inference services, raising critical privacy concerns. Existing methods of performing private third-party inference, such as Secure Multiparty Computation SMPC, often rely on cryptographic methods...

Chain-Of-Lure: a Synthetic Narrative-Driven Approach to Compromise Large Language Models

In the era of rapid generative AI development, interactions between humans and large language models face significant misusing risks. Previous research has primarily focused on black-box scenarios using human-guided prompts and white-box scenarios leveraging gradient-based LLM generation methods,...

Gentoo Linux Security Advisory 201607-06

Gentoo Linux Security Advisory 201607-06 - A buffer overflow in CUPS might allow remote attackers to execute arbitrary code. Versions less than 2.0.2-r1 are affected...

Gentoo Linux Security Advisory 201607-04

Gentoo Linux Security Advisory 201607-04 - Multiple vulnerabilities have been found in GD, the worst of which allows remote attackers to execute arbitrary code. Versions less than 2.2.2 are affected...

Large Language Models in the IoT Ecosystem -- a Survey on Security Challenges and Applications

The Internet of Things IoT and Large Language Models LLMs have been two major emerging players in the information technology era. Although there has been significant coverage of their individual capabilities, our literature survey sheds some light on the integration and interaction of LLMs and Io...

Architectural Backdoors for Within-Batch Data Stealing and Model Inference Manipulation

For nearly a decade the academic community has investigated backdoors in neural networks, primarily focusing on classification tasks where adversaries manipulate the model prediction. While demonstrably malicious, the immediate real-world impact of such prediction-altering attacks has remained...

AI/ML for 5G and beyond Cybersecurity

The advancements in communication technology 5G and beyond and global connectivity Internet of Things IoT also come with new security problems that will need to be addressed in the next few years. The threats and vulnerabilities introduced by AI/ML based 5G and beyond IoT systems need to be...

Gentoo Linux Security Advisory 201707-12

Gentoo Linux Security Advisory 201707-12 - A vulnerability in MAN DB allows local users to gain root privileges. Versions less than 2.7.6.1-r2 are affected...

ACSE-Eval: Can LLMs Threat Model Real-World Cloud Infrastructure?

While Large Language Models have shown promise in cybersecurity applications, their effectiveness in identifying security threats within cloud deployments remains unexplored. This paper introduces AWS Cloud Security Engineering Eval, a novel dataset for evaluating LLMs cloud security threat...

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems

Autonomous driving systems ADS increasingly rely on deep learning-based perception models, which remain vulnerable to adversarial attacks. In this paper, we revisit adversarial attacks and defense methods, focusing on road sign recognition and lead object detection and prediction e.g., relative...

Wazuh 4.10.2

Wazuh is a free and open source security platform that unifies XDR and SIEM capabilities. It protects workloads across on-premises, virtualized, containerized, and cloud-based environments. This is the source code release...

Understanding the Security Landscape of Embedded Non-Volatile Memories: a Comprehensive Survey

The modern semiconductor industry requires memory solutions that can keep pace with the high-speed demands of high-performance computing. Embedded non-volatile memories eNVMs address these requirements by offering faster access to stored data at an improved computational throughput and efficiency...

When Safety Detectors Aren'T Enough: a Stealthy and Effective Jailbreak Attack on LLMs Via Steganographic Techniques

Jailbreak attacks pose a serious threat to large language models LLMs by bypassing built-in safety mechanisms and leading to harmful outputs. Studying these attacks is crucial for identifying vulnerabilities and improving model security. This paper presents a systematic survey of jailbreak method...

Can ChatGPT Perform Image Splicing Detection? A Preliminary Study

Multimodal Large Language Models MLLMs like GPT-4V are capable of reasoning across text and image modalities, showing promise in a variety of complex vision-language tasks. In this preliminary study, we investigate the out-of-the-box capabilities of GPT-4V in the domain of image forensics,...

Mitigating Fine-Tuning Risks in LLMs Via Safety-Aware Probing Optimization

The significant progress of large language models LLMs has led to remarkable achievements across numerous applications. However, their ability to generate harmful content has sparked substantial safety concerns. Despite the implementation of safety alignment techniques during the pre-training...

Poster: Towards an Automated Security Testing Framework for Industrial UEs

With the ongoing adoption of 5G for communication in industrial systems and critical infrastructure, the security of industrial UEs such as 5G-enabled industrial robots becomes an increasingly important topic. Most notably, to meet the stringent security requirements of industrial deployments,...

Advancing Security with Digital Twins: a Comprehensive Survey

The proliferation of electronic devices has greatly transformed every aspect of human life, such as communication, healthcare, transportation, and energy. Unfortunately, the global electronics supply chain is vulnerable to various attacks, including piracy of intellectual properties, tampering,...