Breaking Espressif’s ESP32 V3: Program Counter Control with Computed Values using Fault Injection

Espressif introduced the ESP32 V3, a low-cost System-on-Chip SoC with wireless connectivity, as a response to earlier hardware revisions that were susceptible to Fault Injection FI attacks. Despite its FI countermeasures, the authors of this paper are the first to bypass all security features of...

Towards Effective Complementary Security Analysis Using Large Language Models

A key challenge in security analysis is the manual evaluation of potential security weaknesses generated by static application security testing SAST tools. Numerous false positives FPs in these reports reduce the effectiveness of security analysis. We propose using Large Language Models LLMs to...

A Novel Approach to Differential Privacy with Alpha Divergence

As data-driven technologies advance swiftly, maintaining strong privacy measures becomes progressively difficult. Conventional $ε, δ$-differential privacy, while prevalent, exhibits limited adaptability for many applications. To mitigate these constraints, we present alpha differential privacy AD...

A Smart Contract-Based Non-Transferable Signature Verification System Using Nominative Signatures

Nominative signatures allow us to indicate who can verify a signature, and they can be employed to construct a non-transferable signature verification system that prevents the signature verification by a third party in unexpected situations. For example, this system can prevent IOU/loan certifica...

Zero-Knowledge Proof-Of-Location Protocols for Vehicle Subsidies and Taxation Compliance

This paper introduces a new set of privacy-preserving mechanisms for verifying compliance with location-based policies for vehicle taxation, or for electric vehicle EV subsidies, using Zero-Knowledge Proofs ZKPs. We present the design and evaluation of a Zero-Knowledge Proof-of-Location ZK-PoL...

Secret Sharing in 5G-MEC: Applicability for Joint Security and Dependability

Multi-access Edge Computing MEC, an enhancement of 5G, processes data closer to its generation point, reducing latency and network load. However, the distributed and edge-based nature of 5G-MEC presents privacy and security challenges, including data exposure risks. Ensuring efficient manipulatio...

A Geometry-Grounded Data Perimeter in Azure

While data perimeter is ubiquitous in cybersecurity speak, it rarely defines how boundary points are arranged. In this paper we show how Azure s blast radius ultrametric provides the distance, and how solving the Traveling Salesman Problem in this ultrametric space provides the ordering, yielding...

Navigating the Deep: Signature Extraction on Deep Neural Networks

Neural network model extraction has emerged in recent years as an important security concern, as adversaries attempt to recover a network's parameters via black-box queries. A key step in this process is signature extraction, which aims to recover the absolute values of the network's weights laye...

SAFEx: Analyzing Vulnerabilities of MoE-Based LLMs Via Stable Safety-Critical Expert Identification

Large language models based on Mixture-of-Experts have achieved substantial gains in efficiency and scalability, yet their architectural uniqueness introduces underexplored safety alignment challenges. Existing safety alignment strategies, predominantly designed for dense models, are ill-suited t...

EditLord: Learning Code Transformation Rules for Code Editing

Code editing is a foundational task in software development, where its effectiveness depends on whether it introduces desired code property changes without changing the original code's intended functionality. Existing approaches often formulate code editing as an implicit end-to-end task, omittin...

Analyzing PDFs like Binaries: Adversarially Robust PDF Malware Analysis Via Intermediate Representation and Language Model

Malicious PDF files have emerged as a persistent threat and become a popular attack vector in web-based attacks. While machine learning-based PDF malware classifiers have shown promise, these classifiers are often susceptible to adversarial attacks, undermining their reliability. To address this...

From Thinking to Output: Chain-Of-Thought and Text Generation Characteristics in Reasoning Language Models

Recently, there have been notable advancements in large language models LLMs, demonstrating their growing abilities in complex reasoning. However, existing research largely overlooks a thorough and systematic comparison of these models' reasoning processes and outputs, particularly regarding thei...

SmartGuard: Leveraging Large Language Models for Network Attack Detection through Audit Log Analysis and Summarization

End-point monitoring solutions are widely deployed in today's enterprise environments to support advanced attack detection and investigation. These monitors continuously record system-level activities as audit logs and provide deep visibility into security events. Unfortunately, existing methods ...

MM-AttacKG: a Multimodal Approach to Attack Graph Construction with Large Language Models

Cyber Threat Intelligence CTI parsing aims to extract key threat information from massive data, transform it into actionable intelligence, enhance threat detection and defense efficiency, including attack graph construction, intelligence fusion and indicator extraction. Among these research topic...

Public-Key Quantum Authentication and Digital Signature Schemes Based on the QMA-Complete Problem

We propose a quantum authentication and digital signature protocol whose security is founded on the Quantum Merlin ArthurQMA-completeness of the consistency of local density matrices. The protocol functions as a true public-key cryptography system, where the public key is a set of local density...

VReaves: Eavesdropping on Virtual Reality App Identity and Activity Via Electromagnetic Side Channels

Virtual reality VR has recently proliferated significantly, consisting of headsets or head-mounted displays HMDs and hand controllers for an embodied and immersive experience. The VR device is usually embedded with different kinds of IoT sensors, such as cameras, microphones, communication sensor...

Semantic-Aware Parsing for Security Logs

Security analysts struggle to quickly and efficiently query and correlate log data due to the heterogeneity and lack of structure in real-world logs. Existing AI-based parsers focus on learning syntactic log templates but lack the semantic interpretation needed for querying. Directly querying lar...

SafeGenBench: a Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code

The code generation capabilities of large language modelsLLMs have emerged as a critical dimension in evaluating their overall performance. However, prior research has largely overlooked the security risks inherent in the generated code. In this work, we introduce SafeGenBench, a benchmark...

Sharpening Kubernetes Audit Logs with Context Awareness

Kubernetes has emerged as the de facto orchestrator of microservices, providing scalability and extensibility to a highly dynamic environment. It builds an intricate and deeply connected system that requires extensive monitoring capabilities to be properly managed. To this account, K8s natively...

SecureFed: a Two-Phase Framework for Detecting Malicious Clients in Federated Learning

Federated Learning FL protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversarial clients that could alter results or sabotage model performance. This study presents SecureFed, a two-phase FL...

Safety Interventions against Adversarial Patches in an Open-Source Driver Assistance System

Drivers are becoming increasingly reliant on advanced driver assistance systems ADAS as autonomous driving technology becomes more popular and developed with advanced safety features to enhance road safety. However, the increasing complexity of the ADAS makes autonomous vehicles AVs more exposed ...

Security through the Eyes of AI: How Visualization Is Shaping Malware Detection

Malware, a persistent cybersecurity threat, increasingly targets interconnected digital systems such as desktop, mobile, and IoT platforms through sophisticated attack vectors. By exploiting these vulnerabilities, attackers compromise the integrity and resilience of modern digital ecosystems. To...

Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task

With today's wide adoption of LLM products like ChatGPT from OpenAI, humans and businesses engage and use LLMs on a daily basis. Like any other tool, it carries its own set of advantages and limitations. This study focuses on finding out the cognitive cost of using an LLM in the educational conte...

Malware Classification Leveraging NLP and Machine Learning for Enhanced Accuracy

This paper investigates the application of natural language processing NLP-based n-gram analysis and machine learning techniques to enhance malware classification. We explore how NLP can be used to extract and analyze textual features from malware samples through n-grams, contiguous string or API...

Probe Before You Talk: Towards Black-Box Defense against Backdoor Unalignment for Large Language Models

Backdoor unalignment attacks against Large Language Models LLMs enable the stealthy compromise of safety alignment using a hidden trigger while evading normal safety auditing. These attacks pose significant threats to the applications of LLMs in the real-world Large Language Model as a Service...

Probing the Robustness of Large Language Models Safety to Latent Perturbations

Safety alignment is a key requirement for building reliable Artificial General Intelligence. Despite significant advances in safety alignment, we observe that minor latent shifts can still trigger unsafe responses in aligned models. We argue that this stems from the shallow nature of existing...

Black-Box Privacy Attacks on Shared Representations in Multitask Learning

Multitask learning MTL has emerged as a powerful paradigm that leverages similarities among multiple learning tasks, each with insufficient samples to train a standalone model, to solve them simultaneously while minimizing data sharing across users and organizations. MTL typically accomplishes th...

Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models

Intelligent Transportation Systems ITS are increasingly vulnerable to sophisticated cyberattacks due to their complex, interconnected nature. Ensuring the cybersecurity of these systems is paramount to maintaining road safety and minimizing traffic disruptions. This study presents a novel...

Automated Energy Billing with Blockchain and the Prophet Forecasting Model: a Holistic Approach

This paper presents a comprehensive approach to automated energy billing that leverages IoT-based smart meters, blockchain technology, and the Prophet time series forecasting model. The proposed system facilitates real-time power consumption monitoring via Wi-Fi-enabled ESP32 modules and a mobile...

Private Training and Data Generation by Clustering Embeddings

Deep neural networks often use large, high-quality datasets to achieve high performance on many machine learning tasks. When training involves potentially sensitive data, this process can raise privacy concerns, as large models have been shown to unintentionally memorize and reveal sensitive...

RansomLord NG Anti-Ransomware Exploit Tool 1.0

RansomLord is a proof-of-concept tool that automates the creation of PE files, used to compromise ransomware pre-encryption. This tool uses dll hijacking to defeat ransomware by placing PE files in the x32 or x64 directories where the program is run from. This is the NG version that currently...

Emission Impossible: Privacy-Preserving Carbon Emissions Claims

Information and Communication Technologies ICT have a significant climate impact, and data centres account for a large proportion of the carbon emissions from ICT. To achieve sustainability goals, it is important that all parties involved in ICT supply chains can track and share accurate carbon...

The Hitchhiker'S Guide to Efficient, End-To-End, and Tight DP Auditing

This paper systematizes research on auditing Differential Privacy DP techniques, aiming to identify key insights into the current state of the art and open challenges. First, we introduce a comprehensive framework for reviewing work in the field and establish three cross-contextual desiderata tha...

Physical-Layer Signal Injection Attacks on EV Charging Ports: Bypassing Authentication Via Electrical-Level Exploits

The proliferation of electric vehicles in recent years has significantly expanded the charging infrastructure while introducing new security risks to both vehicles and chargers. In this paper, we investigate the security of major charging protocols such as SAE J1772, CCS, IEC 61851, GB/T 20234, a...

PRISON: Unmasking the Criminal Potential of Large Language Models

As large language models LLMs advance, concerns about their misconduct in complex social contexts intensify. Existing research overlooked the systematic understanding and assessment of their criminal capability in realistic interactions. We propose a unified framework PRISON, to quantify LLMs'...

SAFER-D: a Self-Adaptive Security Framework for Distributed Computing Architectures

The rise of the Internet of Things and Cyber-Physical Systems has introduced new challenges on ensuring secure and robust communication. The growing number of connected devices increases network complexity, leading to higher latency and traffic. Distributed computing architectures DCAs have gaine...

Privacy-Preserving LLM Interaction with Socratic Chain-Of-Thought Reasoning and Homomorphically Encrypted Vector Databases

Large language models LLMs are increasingly used as personal agents, accessing sensitive user data such as calendars, emails, and medical records. Users currently face a trade-off: They can send private records, many of which are stored in remote databases, to powerful but untrusted LLM providers...

Applications of Zero-Knowledge Proofs on Bitcoin

This paper explores how zero-knowledge proofs can enhance Bitcoin's functionality and privacy. First, we consider Proof-of-Reserve schemes: by using zk-STARKs, a custodian can prove its Bitcoin holdings are more than a predefined threshold X, without revealing addresses or actual balances. We...

Few-Shot Learning-Based Cyber Incident Detection with Augmented Context Intelligence

In recent years, the adoption of cloud services has been expanding at an unprecedented rate. As more and more organizations migrate or deploy their businesses to the cloud, a multitude of related cybersecurity incidents such as data breaches are on the rise. Many inherent attributes of cloud...

CUBA: Controlled Untargeted Backdoor Attack against Deep Neural Networks

Backdoor attacks have emerged as a critical security threat against deep neural networks in recent years. The majority of existing backdoor attacks focus on targeted backdoor attacks, where trigger is strongly associated to specific malicious behavior. Various backdoor detection methods depend on...

Spotting Tell-Tale Visual Artifacts in Face Swapping Videos: Strengths and Pitfalls of CNN Detectors

Face swapping manipulations in video streams represents an increasing threat in remote video communications, due to advances in automated and real-time tools. Recent literature proposes to characterize and exploit visual artifacts introduced in video frames by swapping algorithms when dealing wit...

Advantech WISE 4060LAN / IoT Gateway Packet Injection

Remote attackers can execute Modbus commands to WISE-4060/LAN module and manipulate the DO channels. This could lead to unauthorized control of connected devices, such as turning systems on or off, causing disruptions or unsafe conditions. In industrial settings, the DO channels might control...

Efficient Blockchain-Based Steganography Via Backcalculating Generative Adversarial Network

Blockchain-based steganography enables data hiding via encoding the covert data into a specific blockchain transaction field. However, previous works focus on the specific field-embedding methods while lacking a consideration on required field-generation embedding. In this paper, we propose a...

AndroIDS : Android-Based Intrusion Detection System Using Federated Learning

The exponential growth of android-based mobile IoT systems has significantly increased the susceptibility of devices to cyberattacks, particularly in smart homes, UAVs, and other connected mobile environments. This article presents a federated learning-based intrusion detection framework called...

Watermarking Autoregressive Image Generation

Watermarking the outputs of generative models has emerged as a promising approach for tracking their provenance. Despite significant interest in autoregressive image generation models and their potential for misuse, no prior work has attempted to watermark their outputs at the token level. In thi...

Differentiation-Based Extraction of Proprietary Data from Fine-Tuned LLMs

The increasing demand for domain-specific and human-aligned Large Language Models LLMs has led to the widespread adoption of Supervised Fine-Tuning SFT techniques. SFT datasets often comprise valuable instruction-response pairs, making them highly valuable targets for potential extraction. This...

Centre Driven Controlled Evolution of Wireless Virtual Networks Based on Broadcast Tokens

In a wireless sensor network, the virtual connectivity between nodes is a function of the keys shared between various nodes. Pre-embedding these key configurations in the nodes would make the network inflexible. On the other hand, permitting subsets of nodes to engage in a common key synthesis...

Trustworthy Artificial Intelligence for Cyber Threat Analysis

Artificial Intelligence brings innovations into the society. However, bias and unethical exist in many algorithms that make the applications less trustworthy. Threats hunting algorithms based on machine learning have shown great advantage over classical methods. Reinforcement learning models are...

Tech-ASan: Two-Stage Check for Address Sanitizer

Address Sanitizer ASan is a sharp weapon for detecting memory safety violations, including temporal and spatial errors hidden in C/C++ programs during execution. However, ASan incurs significant runtime overhead, which limits its efficiency in testing large software. The overhead mainly comes fro...

A Sea of Cyber Threats: Maritime Cybersecurity from the Perspective of Mariners

Maritime systems, including ships and ports, are critical components of global infrastructure, essential for transporting over 80% of the world's goods and supporting internet connectivity. However, these systems face growing cybersecurity threats, as shown by recent attacks disrupting Maersk, on...