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...

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...

Mal-D2GAN: Double-Detector Based GAN for Malware Generation

Machine learning ML has been developed to detect malware in recent years. Most researchers focused their efforts on improving the detection performance but ignored the robustness of the ML models. In addition, many machine learning algorithms are very vulnerable to intentional attacks. To solve...

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...

MLRan: a Behavioural Dataset for Ransomware Analysis and Detection

Ransomware remains a critical threat to cybersecurity, yet publicly available datasets for training machine learning-based ransomware detection models are scarce and often have limited sample size, diversity, and reproducibility. In this paper, we introduce MLRan, a behavioural ransomware dataset...

ARMS: a Vision for Actor Reputation Metric Systems in the Open-Source Software Supply Chain

Many critical information technology and cyber-physical systems rely on a supply chain of open-source software projects. OSS project maintainers often integrate contributions from external actors. While maintainers can assess the correctness of a change request, assessing a change request's...

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...

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...

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...

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...

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

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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...

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...

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,...

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...

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...

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...

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...

Verifiable Deep Learning Inference on an Untrusted Party

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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...

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...

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 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...

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 201607-05

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...

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...

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...

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...

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...

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...

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...

JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models

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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...

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...

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...

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...

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,...

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

Whitepaper called TSA-WF: Exploring The Effectiveness Of Time Series Analysis For Website Fingerprinting...

Dynamic Encryption-Based Cloud Security Model Using Facial Image and Password-Based Key Generation for Multimedia Data

In this cloud-dependent era, various security techniques, such as encryption, steganography, and hybrid approaches, have been utilized in cloud computing to enhance security, maintain enormous storage capacity, and provide ease of access. However, the absence of data type-specific encryption and...

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...

Vehicular Intrusion Detection System for Controller Area Network: a Comprehensive Survey and Evaluation

The progress of automotive technologies has made cybersecurity a crucial focus, leading to various cyber attacks. These attacks primarily target the Controller Area Network CAN and specialized Electronic Control Units ECUs. In order to mitigate these attacks and bolster the security of vehicular...

LLM-BSCVM: an LLM-Based Blockchain Smart Contract Vulnerability Management Framework

Smart contracts are a key component of the Web 3.0 ecosystem, widely applied in blockchain services and decentralized applications. However, the automated execution feature of smart contracts makes them vulnerable to potential attacks due to inherent flaws, which can lead to severe security risks...

Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices

The emergence of quantum computing and Shor's algorithm necessitates an imminent shift from current public key cryptography techniques to post-quantum robust techniques. NIST has responded by standardising Post-Quantum Cryptography PQC algorithms, with ML-KEM FIPS-203 slated to replace ECDH...

BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models Via Objective-Decoupled Optimization

Vision-Language-Action VLA models have advanced robotic control by enabling end-to-end decision-making directly from multimodal inputs. However, their tightly coupled architectures expose novel security vulnerabilities. Unlike traditional adversarial perturbations, backdoor attacks represent a...

Unsupervised Network Anomaly Detection with Autoencoders and Traffic Images

Due to the recent increase in the number of connected devices, the need to promptly detect security issues is emerging. Moreover, the high number of communication flows creates the necessity of processing huge amounts of data. Furthermore, the connected devices are heterogeneous in nature, having...

Robust LLM Fingerprinting Via Domain-Specific Watermarks

As open-source language models OSMs grow more capable and are widely shared and finetuned, ensuring model provenance, i.e., identifying the origin of a given model instance, has become an increasingly important issue. At the same time, existing backdoor-based model fingerprinting techniques often...

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...

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...

Unlearning Isn'T Deletion: Investigating Reversibility of Machine Unlearning in LLMs

Unlearning in large language models LLMs is intended to remove the influence of specific data, yet current evaluations rely heavily on token-level metrics such as accuracy and perplexity. We show that these metrics can be misleading: models often appear to forget, but their original behavior can ...