DejaVuzz: Disclosing Transient Execution Bugs with Dynamic Swappable Memory and Differential Information Flow Tracking Assisted Processor Fuzzing

Transient execution vulnerabilities have emerged as a critical threat to modern processors. Hardware fuzzing testing techniques have recently shown promising results in discovering transient execution bugs in large-scale out-of-order processor designs. However, their poor microarchitectural...

GiBy: a Giant-Step Baby-Step Classifier for Anomaly Detection in Industrial Control Systems

The continuous monitoring of the interactions between cyber-physical components of any industrial control system ICS is required to secure automation of the system controls, and to guarantee plant processes are fail-safe and remain in an acceptably safe state. Safety is achieved by managing...

New Capacity Bounds for PIR on Graph and Multigraph-Based Replicated Storage

In this paper, we study the problem of private information retrieval PIR in both graph-based and multigraph-based replication systems, where each file is stored on exactly two servers, and any pair of servers shares at most $r$ files. We derive upper bounds on the PIR capacity for such systems an...

Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks

Graph neural networks have been widely utilized to solve graph-related tasks because of their strong learning power in utilizing the local information of neighbors. However, recent studies on graph adversarial attacks have proven that current graph neural networks are not robust against malicious...

Mitigating the Structural Bias in Graph Adversarial Defenses

In recent years, graph neural networks GNNs have shown great potential in addressing various graph structure-related downstream tasks. However, recent studies have found that current GNNs are susceptible to malicious adversarial attacks. Given the inevitable presence of adversarial attacks in the...

DP-SMOTE: Integrating Differential Privacy and Oversampling Technique to Preserve Privacy in Smart Homes

Smart homes represent intelligent environments where interconnected devices gather information, enhancing users living experiences by ensuring comfort, safety, and efficient energy management. To enhance the quality of life, companies in the smart device industry collect user data, including...

Secure Coding with AI, from Creation to Inspection

While prior studies have explored security in code generated by ChatGPT and other Large Language Models, they were conducted in controlled experimental settings and did not use code generated or provided from actual developer interactions. This paper not only examines the security of code generat...

DICOM Compatible, 3D Multimodality Image Encryption Using Hyperchaotic Signal

Medical image encryption plays an important role in protecting sensitive health information from cyberattacks and unauthorized access. In this paper, we introduce a secure and robust encryption scheme that is multi-modality compatible and works with MRI, CT, X-Ray and Ultrasound images for...

Data Encryption Battlefield: a Deep Dive into the Dynamic Confrontations in Ransomware Attacks

In the rapidly evolving landscape of cybersecurity threats, ransomware represents a significant challenge. Attackers increasingly employ sophisticated encryption methods, such as entropy reduction through Base64 encoding, and partial or intermittent encryption to evade traditional detection...

A Novel Cipher for Enhancing MAVLink Security: Design, Security Analysis, and Performance Evaluation Using a Drone Testbed

We present MAVShield, a novel lightweight cipher designed to secure communications in Unmanned Aerial Vehicles UAVs using the MAVLink protocol, which by default transmits unencrypted messages between UAVs and Ground Control Stations GCS. While existing studies propose encryption for MAVLink, most...

The Hidden Risks of LLM-Generated Web Application Code: a Security-Centric Evaluation of Code Generation Capabilities in Large Language Models

The rapid advancement of Large Language Models LLMs has enhanced software development processes, minimizing the time and effort required for coding and enhancing developer productivity. However, despite their potential benefits, code generated by LLMs has been shown to generate insecure code in...

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models

Parameter-efficient fine-tuning PEFT has emerged as a practical solution for adapting large language models LLMs to custom datasets with significantly reduced computational cost. When carrying out PEFT under collaborative learning scenarios e.g., federated learning, it is often required to exchan...

Efficient Patient-Centric EMR Sharing Block Tree

Flexible sharing of electronic medical records EMRs is an urgent need in healthcare, as fragmented storage creates EMR management complexity for both practitioners and patients. Blockchain has emerged as a promising solution to address the limitations of centralized EMR systems regarding...

Starfish: Rebalancing Multi-Party Off-Chain Payment Channels

Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network PCN, as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channe...

Token-Efficient Prompt Injection Attack: Provoking Cessation in LLM Reasoning Via Adaptive Token Compression

While reasoning large language models LLMs demonstrate remarkable performance across various tasks, they also contain notable security vulnerabilities. Recent research has uncovered a "thinking-stopped" vulnerability in DeepSeek-R1, where model-generated reasoning tokens can forcibly interrupt th...

Sleeping Giants -- Activating Dormant Java Deserialization Gadget Chains through Stealthy Code Changes

Java deserialization gadget chains are a well-researched critical software weakness. The vast majority of known gadget chains rely on gadgets from software dependencies. Furthermore, it has been shown that small code changes in dependencies have enabled these gadget chains. This makes gadget chai...

VIMU: Effective Physics-Based Realtime Detection and Recovery against Stealthy Attacks on UAVs

Sensor attacks on robotic vehicles have become pervasive and manipulative. Their latest advancements exploit sensor and detector characteristics to bypass detection. Recent security efforts have leveraged the physics-based model to detect or mitigate sensor attacks. However, these approaches are...

Robustness Via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction

Large language models LLMs have demonstrated impressive performance and have come to dominate the field of natural language processing NLP across various tasks. However, due to their strong instruction-following capabilities and inability to distinguish between instructions and data content, LLMs...

Network Attack Traffic Detection with Hybrid Quantum-Enhanced Convolution Neural Network

The emerging paradigm of Quantum Machine Learning QML combines features of quantum computing and machine learning ML. QML enables the generation and recognition of statistical data patterns that classical computers and classical ML methods struggle to effectively execute. QML utilizes quantum...

An Algebraic Approach to Asymmetric Delegation and Polymorphic Label Inference (Technical Report)

Language-based information flow control IFC enables reasoning about and enforcing security policies in decentralized applications. While information flow properties are relatively extensional and compositional, designing expressive systems that enforce such properties remains challenging. In...

Enhancing Leakage Attacks on Searchable Symmetric Encryption Using LLM-Based Synthetic Data Generation

Searchable Symmetric Encryption SSE enables efficient search capabilities over encrypted data, allowing users to maintain privacy while utilizing cloud storage. However, SSE schemes are vulnerable to leakage attacks that exploit access patterns, search frequency, and volume information. Existing...

Mutual Information Minimization for Side-Channel Attack Resistance Via Optimal Noise Injection

Side-channel attacks SCAs pose a serious threat to system security by extracting secret keys through physical leakages such as power consumption, timing variations, and electromagnetic emissions. Among existing countermeasures, artificial noise injection is recognized as one of the most effective...

CachePrune: Neural-Based Attribution Defense against Indirect Prompt Injection Attacks

Large Language Models LLMs are identified as being susceptible to indirect prompt injection attack, where the model undesirably deviates from user-provided instructions by executing tasks injected in the prompt context. This vulnerability stems from LLMs' inability to distinguish between data and...

A Summation-Based Algorithm for Integer Factorization

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Erased but Not Forgotten: How Backdoors Compromise Concept Erasure

The expansion of large-scale text-to-image diffusion models has raised growing concerns about their potential to generate undesirable or harmful content, ranging from fabricated depictions of public figures to sexually explicit images. To mitigate these risks, prior work has devised machine...

SFIBA: Spatial-Based Full-Target Invisible Backdoor Attacks

Multi-target backdoor attacks pose significant security threats to deep neural networks, as they can preset multiple target classes through a single backdoor injection. This allows attackers to control the model to misclassify poisoned samples with triggers into any desired target class during...

SoK: Enhancing Privacy-Preserving Software Development from a Developers' Perspective

In software development, privacy preservation has become essential with the rise of privacy concerns and regulations such as GDPR and CCPA. While several tools, guidelines, methods, methodologies, and frameworks have been proposed to support developers embedding privacy into software applications...

Microsoft .library-ms File / NTLM Information Disclosure - Resurrected 2025

It took 7 years, but Microsoft finally realized a vulnerability was severe enough to be addressed and it was not until other researchers also reported it, that the original researcher finally got credited after pointing it out...

Bipartite Randomized Response Mechanism for Local Differential Privacy

With the increasing importance of data privacy, Local Differential Privacy LDP has recently become a strong measure of privacy for protecting each user's privacy from data analysts without relying on a trusted third party. In many cases, both data providers and data analysts hope to maximize the...

SecRepoBench: Benchmarking LLMs for Secure Code Generation in Real-World Repositories

This paper introduces SecRepoBench, a benchmark to evaluate LLMs on secure code generation in real-world repositories. SecRepoBench has 318 code generation tasks in 27 C/C++ repositories, covering 15 CWEs. We evaluate 19 state-of-the-art LLMs using our benchmark and find that the models struggle...

TriniMark: a Robust Generative Speech Watermarking Method for Trinity-Level Attribution

Whitepaper called TriniMark: A Robust Generative Speech Watermarking Method For Trinity-Level Attribution...

Enhancing Vulnerability Reports with Automated and Augmented Description Summarization

Public vulnerability databases, such as the National Vulnerability Database NVD, document vulnerabilities and facilitate threat information sharing. However, they often suffer from short descriptions and outdated or insufficient information. In this paper, we introduce Zad, a system designed to...

Dual Explanations Via Subgraph Matching for Malware Detection

Interpretable malware detection is crucial for understanding harmful behaviors and building trust in automated security systems. Traditional explainable methods for Graph Neural Networks GNNs often highlight important regions within a graph but fail to associate them with known benign or maliciou...

Federated One-Shot Learning with Data Privacy and Objective-Hiding

Privacy in federated learning is crucial, encompassing two key aspects: safeguarding the privacy of clients' data and maintaining the privacy of the federator's objective from the clients. While the first aspect has been extensively studied, the second has received much less attention. We present...

BoxBilling 4.22.1.5 Remote Code Execution

BoxBilling versions 4.22.1.5 and below remote code execution exploit that spawns a php reverse shell...

Exploring the Role of Large Language Models in Cybersecurity: a Systematic Survey

With the rapid development of technology and the acceleration of digitalisation, the frequency and complexity of cyber security threats are increasing. Traditional cybersecurity approaches, often based on static rules and predefined scenarios, are struggling to adapt to the rapidly evolving natur...

The Cost of Performance: Breaking ThreadX with Kernel Object Masquerading Attacks

Microcontroller-based IoT devices often use embedded real-time operating systems RTOSs. Vulnerabilities in these embedded RTOSs can lead to compromises of those IoT devices. Despite the significance of security protections, the absence of standardized security guidelines results in various levels...

Cybersecurity for Autonomous Vehicles

The increasing adoption of autonomous vehicles is bringing a major shift in the automotive industry. However, as these vehicles become more connected, cybersecurity threats have emerged as a serious concern. Protecting the security and integrity of autonomous systems is essential to prevent...

Simplified and Secure MCP Gateways for Enterprise AI Integration

The increased adoption of the Model Context Protocol MCP for AI Agents necessitates robust security for Enterprise integrations. This paper introduces the MCP Gateway to simplify self-hosted MCP server integration. The proposed architecture integrates security principles, authentication, intrusio...

Securing GenAI Multi-Agent Systems against Tool Squatting: a Zero Trust Registry-Based Approach

The rise of generative AI GenAI multi-agent systems MAS necessitates standardized protocols enabling agents to discover and interact with external tools. However, these protocols introduce new security challenges, particularly; tool squatting; the deceptive registration or representation of tools...

The Automation Advantage in AI Red Teaming

This paper analyzes Large Language Model LLM security vulnerabilities based on data from Crucible, encompassing 214,271 attack attempts by 1,674 users across 30 LLM challenges. Our findings reveal automated approaches significantly outperform manual techniques 69.5% vs 47.6% success rate, despite...

Security of a Secret Sharing Protocol on the Qline

Secret sharing is a fundamental primitive in cryptography, and it can be achieved even with perfect security. However, the distribution of shares requires computational assumptions, which can compromise the overall security of the protocol. While traditional Quantum Key Distribution QKD can...

SAGE: a Generic Framework for LLM Safety Evaluation

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GenPTW: In-Generation Image Watermarking for Provenance Tracing and Tamper Localization

The rapid development of generative image models has brought tremendous opportunities to AI-generated content AIGC creation, while also introducing critical challenges in ensuring content authenticity and copyright ownership. Existing image watermarking methods, though partially effective, often...

Metadata-Private Messaging without Coordination

For those seeking end-to-end private communication free from pervasive metadata tracking and censorship, the Tor network has been the de-facto choice in practice, despite its susceptibility to traffic analysis attacks. Recently, numerous metadata-private messaging proposals have emerged with the...

Inception: Jailbreak the Memory Mechanism of Text-To-Image Generation Systems

Currently, the memory mechanism has been widely and successfully exploited in online text-to-image T2I generation systems e.g., DALL E 3 for alleviating the growing tokenization burden and capturing key information in multi-turn interactions. Despite its practicality, its security analyses have...

SoK: a Survey of Mixing Techniques and Mixers for Cryptocurrencies

Blockchain technologies have overturned the digital finance industry by introducing a decentralized pseudonymous means of monetary transfer. The pseudonymous nature introduced privacy concerns, enabling various deanonymization techniques, which in turn spurred development of stronger...

The Dark Side of Digital Twins: Adversarial Attacks on AI-Driven Water Forecasting

Digital twins DTs are improving water distribution systems by using real-time data, analytics, and prediction models to optimize operations. This paper presents a DT platform designed for a Spanish water supply network, utilizing Long Short-Term Memory LSTM networks to predict water consumption...

Smart Water Security with AI and Blockchain-Enhanced Digital Twins

Water distribution systems in rural areas face serious challenges such as a lack of real-time monitoring, vulnerability to cyberattacks, and unreliable data handling. This paper presents an integrated framework that combines LoRaWAN-based data acquisition, a machine learning-driven Intrusion...

Security Bug Report Prediction within and across Projects: a Comparative Study of BERT and Random Forest

Early detection of security bug reports SBRs is crucial for preventing vulnerabilities and ensuring system reliability. While machine learning models have been developed for SBR prediction, their predictive performance still has room for improvement. In this study, we conduct a comprehensive...