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...

Privacy-Preserving and Reward-Based Mechanisms of Proof of Engagement

Proof-of-Attendance PoA mechanisms are typically employed to demonstrate a specific user's participation in an event, whether virtual or in-person. The goal of this study is to extend such mechanisms to broader contexts where the user wishes to digitally demonstrate her involvement in a specific...

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...

SecurityLingua: Efficient Defense of LLM Jailbreak Attacks Via Security-Aware Prompt Compression

Large language models LLMs have achieved widespread adoption across numerous applications. However, many LLMs are vulnerable to malicious attacks even after safety alignment. These attacks typically bypass LLMs' safety guardrails by wrapping the original malicious instructions inside adversarial...

Leakage-Resilient Extractors against Number-On-Forehead Protocols

Given a sequence of $N$ independent sources $\mathbfX1,\mathbfX2,\dots,\mathbfXN\sim\0,1^n$, how many of them must be good i.e., contain some min-entropy in order to extract a uniformly random string? This question was first raised by Chattopadhyay, Goodman, Goyal and Li STOC '20, motivated by...

PuDHammer: Experimental Analysis of Read Disturbance Effects of Processing-Using-DRAM in Real DRAM Chips

Processing-using-DRAM PuD is a promising paradigm for alleviating the data movement bottleneck using DRAM's massive internal parallelism and bandwidth to execute very wide operations. Performing a PuD operation involves activating multiple DRAM rows in quick succession or simultaneously, i.e.,...

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...

CnC-PRAC: Coalesce, Not Cache, Per Row Activation Counts for an Efficient In-DRAM Rowhammer Mitigation

JEDEC has introduced the Per Row Activation Counting PRAC framework for DDR5 and future DRAMs to enable precise counting of DRAM row activations using per-row activation counts. While recent PRAC implementations enable holistic mitigation of Rowhammer attacks, they impose slowdowns of up to 10% d...

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...

Embedding Trust at Scale: Physics-Aware Neural Watermarking for Secure and Verifiable Data Pipelines

We present a robust neural watermarking framework for scientific data integrity, targeting high-dimensional fields common in climate modeling and fluid simulations. Using a convolutional autoencoder, binary messages are invisibly embedded into structured data such as temperature, vorticity, and...

An Efficient Hardware Implementation of Elliptic Curve Point Multiplication over $GF(2^M)$ on FPGA

Elliptic Curve Cryptography ECC is widely accepted for ensuring secure data exchange between resource-limited IoT devices. The National Institute of Standards and Technology NIST recommended implementation, such as B-163, is particularly well-suited for Internet of Things IoT applications. Here,...

Watermarking Quantum Neural Networks Based on Sample Grouped and Paired Training

Quantum neural networks QNNs leverage quantum computing to create powerful and efficient artificial intelligence models capable of solving complex problems significantly faster than traditional computers. With the fast development of quantum hardware technology, such as superconducting qubits,...

Multi-Domain Anomaly Detection in a 5G Network

With the advent of 5G, mobile networks are becoming more dynamic and will therefore present a wider attack surface. To secure these new systems, we propose a multi-domain anomaly detection method that is distinguished by the study of traffic correlation on three dimensions: temporal by analyzing...

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption

Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext...

An Attack Method for Medical Insurance Claim Fraud Detection Based on Generative Adversarial Network

Insurance fraud detection represents a pivotal advancement in modern insurance service, providing intelligent and digitalized monitoring to enhance management and prevent fraud. It is crucial for ensuring the security and efficiency of insurance systems. Although AI and machine learning algorithm...

SEC-Bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks

Rigorous security-focused evaluation of large language model LLM agents is imperative for establishing trust in their safe deployment throughout the software development lifecycle. However, existing benchmarks largely rely on synthetic challenges or simplified vulnerability datasets that fail to...

Bidirectional Biometric Authentication Using Transciphering and (T)FHE

Biometric authentication systems pose privacy risks, as leaked templates such as iris or fingerprints can lead to security breaches. Fully Homomorphic Encryption FHE enables secure encrypted evaluation, but its deployment is hindered by large ciphertexts, high key overhead, and limited trust...

KCES: Training-Free Defense for Robust Graph Neural Networks Via Kernel Complexity

Graph Neural Networks GNNs have achieved impressive success across a wide range of graph-based tasks, yet they remain highly vulnerable to small, imperceptible perturbations and adversarial attacks. Although numerous defense methods have been proposed to address these vulnerabilities, many rely o...

Smart Buildings Energy Consumption Forecasting Using Adaptive Evolutionary Ensemble Learning Models

Smart buildings are gaining popularity because they can enhance energy efficiency, lower costs, improve security, and provide a more comfortable and convenient environment for building occupants. A considerable portion of the global energy supply is consumed in the building sector and plays a...

UCD: Unlearning in LLMs Via Contrastive Decoding

Machine unlearning aims to remove specific information, e.g. sensitive or undesirable content, from large language models LLMs while preserving overall performance. We propose an inference-time unlearning algorithm that uses contrastive decoding, leveraging two auxiliary smaller models, one train...

GNSS Spoofing Detection Based on Opportunistic Position Information

The limited or no protection for civilian Global Navigation Satellite System GNSS signals makes spoofing attacks relatively easy. With modern mobile devices often featuring network interfaces, state-of-the-art signals of opportunity SOP schemes can provide accurate network positions in replacemen...

DinoCompanion: an Attachment-Theory Informed Multimodal Robot for Emotionally Responsive Child-AI Interaction

Children's emotional development fundamentally relies on secure attachment relationships, yet current AI companions lack the theoretical foundation to provide developmentally appropriate emotional support. We introduce DinoCompanion, the first attachment-theory-grounded multimodal robot for...

SoK: the Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation

Large language models LLMs are sophisticated artificial intelligence systems that enable machines to generate human-like text with remarkable precision. While LLMs offer significant technological progress, their development using vast amounts of user data scraped from the web and collected from...

Towards Safety and Security Testing of Cyberphysical Power Systems by Shape Validation

The increasing complexity of cyberphysical power systems leads to larger attack surfaces to be exploited by malicious actors and a higher risk of faults through misconfiguration. We propose to meet those risks with a declarative approach to describe cyberphysical power systems and to automaticall...

When Forgetting Triggers Backdoors: a Clean Unlearning Attack

Machine unlearning has emerged as a key component in ensuring Right to be Forgotten, enabling the removal of specific data points from trained models. However, even when the unlearning is performed without poisoning the forget-set clean unlearning, it can be exploited for stealthy attacks that...

VulStamp: Vulnerability Assessment Using Large Language Model

Although modern vulnerability detection tools enable developers to efficiently identify numerous security flaws, indiscriminate remediation efforts often lead to superfluous development expenses. This is particularly true given that a substantial portion of detected vulnerabilities either possess...

LLMs on Support of Privacy and Security of Mobile Apps: State of the Art and Research Directions

Modern life has witnessed the explosion of mobile devices. However, besides the valuable features that bring convenience to end users, security and privacy risks still threaten users of mobile apps. The increasing sophistication of these threats in recent years has underscored the need for more...

Secure User-Friendly Blockchain Modular Wallet Design Using Android and OP-TEE

Emerging crypto economies still hemorrhage digital assets because legacy wallets leak private keys at almost every layer of the software stack, from user-space libraries to kernel memory dumps. This paper solves that twin crisis of security and interoperability by re-imagining key management as a...

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...

Deep Spatial Neural Net Models with Functional Predictors: Application in Large-Scale Crop Yield Prediction

Accurate prediction of crop yield is critical for supporting food security, agricultural planning, and economic decision-making. However, yield forecasting remains a significant challenge due to the complex and nonlinear relationships between weather variables and crop production, as well as...

O2Former:Direction-Aware and Multi-Scale Query Enhancement for SAR Ship Instance Segmentation

Instance segmentation of ships in synthetic aperture radar SAR imagery is critical for applications such as maritime monitoring, environmental analysis, and national security. SAR ship images present challenges including scale variation, object density, and fuzzy target boundary, which are often...

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...

Rectifying Privacy and Efficacy Measurements in Machine Unlearning: a New Inference Attack Perspective

Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing...

Expert Insight-Based Modeling of Non-Kinetic Strategic Deterrence of Rare Earth Supply Disruption: a Simulation-Driven Systematic Framework

This study constructs a quantifiable modelling framework to simulate non-kinetic strategic deterrence pathways in rare earth supply disruption scenarios, based on structured responses from expert interviews led by Dr. Daniel O'Connor, CEO of the Rare Earth Exchange REE. Focusing on disruption...

IDOL: Improved Different Optimization Levels Testing for Solidity Compilers

As blockchain technology continues to evolve and mature, smart contracts have become a key driving force behind the digitization and automation of transactions. Smart contracts greatly simplify and refine the traditional business transaction processes, and thus have had a profound impact on vario...

Robot Context Protocol (RCP): a Runtime-Agnostic Interface for Agent-Aware Robot Control

The Robot Context Protocol RCP is a lightweight, middleware-agnostic communication protocol designed to simplify the complexity of robotic systems and enable seamless interaction between robots, users, and autonomous agents. RCP provides a unified and semantically meaningful interface that...

Reversing the Paradigm: Building AI-First Systems with Human Guidance

The relationship between humans and artificial intelligence is no longer science fiction -- it's a growing reality reshaping how we live and work. AI has moved beyond research labs into everyday life, powering customer service chats, personalizing travel, aiding doctors in diagnosis, and supporti...

Fuzzy Location and Allocation Hub Network Design for Air Cargo Transportation Considering Sustainability and Time Window

Hub location Problems seek to find hub facilities and assign non-hub nodes to them in such a way that the flow between origin and destination should be effectively established according to the desired goal. In general, in the literature of location, it is assumed that the time horizon of hub...

FAME: a Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes

The widespread emergence of face-swap Deepfake videos poses growing risks to digital security, privacy, and media integrity, necessitating effective forensic tools for identifying the source of such manipulations. Although most prior research has focused primarily on binary Deepfake detection, th...

Secure API-Driven Research Automation to Accelerate Scientific Discovery

The Secure Scientific Service Mesh S3M provides API-driven infrastructure to accelerate scientific discovery through automated research workflows. By integrating near real-time streaming capabilities, intelligent workflow orchestration, and fine-grained authorization within a service mesh...

Protecting Your Voice: Temporal-Aware Robust Watermarking

Whitepaper called Protecting Your Voice: Temporal-Aware Robust Watermarking...

Quantum-Hybrid Support Vector Machines for Anomaly Detection in Industrial Control Systems

Sensitive data captured by Industrial Control Systems ICS play a large role in the safety and integrity of many critical infrastructures. Detection of anomalous or malicious data, or Anomaly Detection AD, with machine learning is one of many vital components of cyberphysical security. Quantum...

AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator

Whitepaper called AdRo-FL: Informed And Secure Client Selection For Federated Learning In The Presence Of Adversarial Aggregator...

AI Safety Vs. AI Security: Demystifying the Distinction and Boundaries

Artificial Intelligence AI is rapidly being integrated into critical systems across various domains, from healthcare to autonomous vehicles. While its integration brings immense benefits, it also introduces significant risks, including those arising from AI misuse. Within the discourse on managin...

PhishDebate: an LLM-Based Multi-Agent Framework for Phishing Website Detection

Phishing websites continue to pose a significant cybersecurity threat, often leveraging deceptive structures, brand impersonation, and social engineering tactics to evade detection. While recent advances in large language models LLMs have enabled improved phishing detection through contextual...

Time-Bin Encoded Quantum Key Distribution over 120 Km with a Telecom Quantum Dot Source

Quantum key distribution QKD with deterministic single photon sources has been demonstrated over intercity fiber and free-space channels. The previous implementations relied mainly on polarization encoding schemes, which are susceptible to birefringence, polarization-mode dispersion and...

Evaluation Pipeline for Systematically Searching for Anomaly Detection Systems

Digitalization in the medical world provides major benefits while making it a target for attackers and thus hard to secure. To deal with network intruders we propose an anomaly detection system on hardware to detect malicious clients in real-time. We meet real-time and power restrictions using...

Offensive Robot Cybersecurity

Offensive Robot Cybersecurity introduces a groundbreaking approach by advocating for offensive security methods empowered by means of automation. It emphasizes the necessity of understanding attackers' tactics and identifying vulnerabilities in advance to develop effective defenses, thereby...

Facility Location Problem under Local Differential Privacy without Super-set Assumption

In this paper, we introduce an adaptation of the facility location problem and analyze it within the framework of local differential privacy LDP. Under this model, we ensure the privacy of client presence at specific locations...

From LLMs to MLLMs to Agents: a Survey of Emerging Paradigms in Jailbreak Attacks and Defenses within LLM Ecosystem

Large language models LLMs are rapidly evolving from single-modal systems to multimodal LLMs and intelligent agents, significantly expanding their capabilities while introducing increasingly severe security risks. This paper presents a systematic survey of the growing complexity of jailbreak...