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...

InverTune: Removing Backdoors from Multimodal Contrastive Learning Models Via Trigger Inversion and Activation Tuning

Multimodal contrastive learning models like CLIP have demonstrated remarkable vision-language alignment capabilities, yet their vulnerability to backdoor attacks poses critical security risks. Attackers can implant latent triggers that persist through downstream tasks, enabling malicious control ...

Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning

Differential privacy DP is obtained by randomizing a data analysis algorithm, which necessarily introduces a tradeoff between its utility and privacy. Many DP mechanisms are built upon one of two underlying tools: Laplace and Gaussian additive noise mechanisms. We expand the search space of...

Differential Privacy in Machine Learning: from Symbolic AI to LLMs

Machine learning models should not reveal particular information that is not otherwise accessible. Differential privacy provides a formal framework to mitigate privacy risks by ensuring that the inclusion or exclusion of any single data point does not significantly alter the output of an algorith...

A Retrospective on DISPEED -- Leveraging Heterogeneity in a Drone Swarm for IDS Execution

Swarms of drones are gaining more and more autonomy and efficiency during their missions. However, security threats can disrupt their missions' progression. To overcome this problem, Network Intrusion Detection Systems NIDS are promising solutions to detect malicious behavior on network traffic...

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,...

Investigating Vulnerabilities and Defenses against Audio-Visual Attacks: a Comprehensive Survey Emphasizing Multimodal Models

Multimodal large language models MLLMs, which bridge the gap between audio-visual and natural language processing, achieve state-of-the-art performance on several audio-visual tasks. Despite the superior performance of MLLMs, the scarcity of high-quality audio-visual training data and computation...

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...

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...

Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generation

In the life-cycle of software development, testing plays a crucial role in quality assurance. Proper testing not only increases code coverage and prevents regressions but it can also ensure that any potential vulnerabilities in the software are identified and effectively fixed. However, creating...

Mechanistic Interpretability in the Presence of Architectural Obfuscation

Architectural obfuscation - e.g., permuting hidden-state tensors, linearly transforming embedding tables, or remapping tokens - has recently gained traction as a lightweight substitute for heavyweight cryptography in privacy-preserving large-language-model LLM inference. While recent work has sho...

Optimizing Resource Allocation and Energy Efficiency in Federated Fog Computing for IoT

Address Resolution Protocol ARP spoofing attacks severely threaten Internet of Things IoT networks by allowing attackers to intercept, modify, or block communications. Traditional detection methods are insufficient due to high false positives and poor adaptability. This research proposes a...

KCLNet: Physics-Informed Power Flow Prediction Via Constraints Projections

In the modern context of power systems, rapid, scalable, and physically plausible power flow predictions are essential for ensuring the grid's safe and efficient operation. While traditional numerical methods have proven robust, they require extensive computation to maintain physical fidelity und...

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...

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...

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...

PermRust: a Token-Based Permission System for Rust

Permission systems which restrict access to system resources are a well-established technology in operating systems, especially for smartphones. However, as such systems are implemented in the operating system they can at most manage access on the process-level. Since moderns software often reuse...

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...

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...

Shelter Soul: Bridging Shelters and Adopters through Technology

Pet adoption processes often face inefficiencies, including limited accessibility, lack of real-time information, and mismatched expectations between shelters and adopters. To address these challenges, this study presents Shelter Soul, a technology-based solution designed to streamline pet adopti...

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...

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...

Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information

LLM-based Conversational AIs CAIs, also known as GenAI chatbots, like ChatGPT, are increasingly used across various domains, but they pose privacy risks, as users may disclose personal information during their conversations with CAIs. Recent research has demonstrated that LLM-based CAIs could be...

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...

SoK: Automated Vulnerability Repair: Methods, Tools, and Assessments

The increasing complexity of software has led to the steady growth of vulnerabilities. Vulnerability repair investigates how to fix software vulnerabilities. Manual vulnerability repair is labor-intensive and time-consuming because it relies on human experts, highlighting the importance of...

The Domination and Secure Domination Numbers of Direct Product of Cliques with Paths and Cycles

In this paper, we obtain the exact values of several domination parameters for the direct product of a complete graph with a path or a cycle. Specifically, we determine the domination number, independent domination number, $1,2$-domination number, secure domination number, and 2-domination number...

OSI Stack Redesign for Quantum Networks: Requirements, Technologies, Challenges, and Future Directions

Quantum communication is poised to become a foundational element of next-generation networking, offering transformative capabilities in security, entanglement-based connectivity, and computational offloading. However, the classical OSI model-designed for deterministic and error-tolerant...

Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?

Inherent communication noises have the potential to preserve privacy for wireless federated learning WFL but have been overlooked in digital communication systems predominantly using floating-point number standards, e.g., IEEE 754, for data storage and transmission. This is due to the potentially...

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...

TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs

Chip manufacturing is a complex process, and to achieve a faster time to market, an increasing number of untrusted third-party tools and designs from around the world are being utilized. The use of these untrusted third party intellectual properties IPs and tools increases the risk of adversaries...

Federated Learning-Based Data Collaboration Method for Enhancing Edge Cloud AI System Security Using Large Language Models

With the widespread application of edge computing and cloud systems in AI-driven applications, how to maintain efficient performance while ensuring data privacy has become an urgent security issue. This paper proposes a federated learning-based data collaboration method to improve the security of...

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...

LLM Embedding-Based Attribution (LEA): Quantifying Source Contributions to Generative Model'S Response for Vulnerability Analysis

Security vulnerabilities are rapidly increasing in frequency and complexity, creating a shifting threat landscape that challenges cybersecurity defenses. Large Language Models LLMs have been widely adopted for cybersecurity threat analysis. When querying LLMs, dealing with new, unseen...

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...

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...

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.,...

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,...

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...

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...

Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly Detection

The decentralized finance DeFi community has grown rapidly in recent years, pushed forward by cryptocurrency enthusiasts interested in the vast untapped potential of new markets. The surge in popularity of cryptocurrency has ushered in a new era of financial crime. Unfortunately, the novelty of t...

New Characterization of Full Weight Spectrum One-Orbit Cyclic Subspace Codes

In this paper, we determine the weight distributions of a family of FWS codes and exhibit some equivalence classes of FWS codes under certain conditions. Furthermore, we provide a complete classification for $r$-FWS codes...

Excessive Reasoning Attack on Reasoning LLMs

Recent reasoning large language models LLMs, such as OpenAI o1 and DeepSeek-R1, exhibit strong performance on complex tasks through test-time inference scaling. However, prior studies have shown that these models often incur significant computational costs due to excessive reasoning, such as...

Doppelgänger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack

Since the advent of large language models, prompt engineering now enables the rapid, low-effort creation of diverse autonomous agents that are already in widespread use. Yet this convenience raises urgent concerns about the safety, robustness, and behavioral consistency of the underlying prompts,...

A Halpha Metric for Identifying Dormant Black Holes in X-Ray Transients

Dormant black holes BHs in X-ray transients can be identified by the presence of broad Ha emission lines from quiescent accretion discs. Unfortunately, short-period cataclysmic variables CVs can also produce broad Ha lines, especially when viewed at high inclinations, and are thus a major source ...

RAS-Eval: a Comprehensive Benchmark for Security Evaluation of LLM Agents in Real-World Environments

The rapid deployment of Large language model LLM agents in critical domains like healthcare and finance necessitates robust security frameworks. To address the absence of standardized evaluation benchmarks for these agents in dynamic environments, we introduce RAS-Eval, a comprehensive security...

An Efficient Construction of Raz's Two-Source Randomness Extractor with Improved Parameters

Randomness extractors are algorithms that distill weak random sources into near-perfect random numbers. Two-source extractors enable this distillation process by combining two independent weak random sources. Raz's extractor STOC '05 was the first to achieve this in a setting where one source has...

Towards Pervasive Distributed Agentic Generative AI -- a State of the Art

The rapid advancement of intelligent agents and Large Language Models LLMs is reshaping the pervasive computing field. Their ability to perceive, reason, and act through natural language understanding enables autonomous problem-solving in complex pervasive environments, including the management o...

SecFwT: Efficient Privacy-Preserving Fine-Tuning of Large Language Models Using Forward-Only Passes

Large language models LLMs have transformed numerous fields, yet their adaptation to specialized tasks in privacy-sensitive domains, such as healthcare and finance, is constrained by the scarcity of accessible training data due to stringent privacy requirements. Secure multi-party computation...

Tady: a Neural Disassembler without Structural Constraint Violations

Disassembly is a crucial yet challenging step in binary analysis. While emerging neural disassemblers show promise for efficiency and accuracy, they frequently generate outputs violating fundamental structural constraints, which significantly compromise their practical usability. To address this...