A Survey on Privacy Risks and Protection in Large Language Models

Although Large Language Models LLMs have become increasingly integral to diverse applications, their capabilities raise significant privacy concerns. This survey offers a comprehensive overview of privacy risks associated with LLMs and examines current solutions to mitigate these challenges. Firs...

PQS-BFL: a Post-Quantum Secure Blockchain-Based Federated Learning Framework

Federated Learning FL enables collaborative model training while preserving data privacy, but its classical cryptographic underpinnings are vulnerable to quantum attacks. This vulnerability is particularly critical in sensitive domains like healthcare. This paper introduces PQS-BFL Post-Quantum...

Backdoor Attacks against Patch-Based Mixture of Experts

As Deep Neural Networks DNNs continue to require larger amounts of data and computational power, Mixture of Experts MoE models have become a popular choice to reduce computational complexity. This popularity increases the importance of considering the security of MoE architectures. Unfortunately,...

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning

The widespread adoption of Artificial Intelligence AI has been driven by significant advances in intelligent system research. However, this progress has raised concerns about data privacy, leading to a growing awareness of the need for privacy-preserving AI. In response, there has been a seismic...

Energy-Efficient NTT Sampler for Kyber Benchmarked on FPGA

Kyber is a lattice-based key encapsulation mechanism selected for standardization by the NIST Post-Quantum Cryptography PQC project. A critical component of Kyber's key generation process is the sampling of matrix elements from a uniform distribution over the ring Rq . This step is one of the mos...

M-Ary Precomputation-Based Accelerated Scalar Multiplication Algorithms for Enhanced Elliptic Curve Cryptography

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Rogue Cell: Adversarial Attack and Defense in Untrusted O-RAN Setup Exploiting the Traffic Steering XApp

The Open Radio Access Network O-RAN architecture is revolutionizing cellular networks with its open, multi-vendor design and AI-driven management, aiming to enhance flexibility and reduce costs. Although it has many advantages, O-RAN is not threat-free. While previous studies have mainly examined...

An Approach for Handling Missing Attribute Values in Attribute-Based Access Control Policy Mining

Attribute-Based Access Control ABAC enables highly expressive and flexible access decisions by considering a wide range of contextual attributes. ABAC policies use logical expressions that combine these attributes, allowing for precise and context-aware control. Algorithms that mine ABAC policies...

Unified Steganography Via Implicit Neural Representation

Digital steganography is the practice of concealing for encrypted data transmission. Typically, steganography methods embed secret data into cover data to create stega data that incorporates hidden secret data. However, steganography techniques often require designing specific frameworks for each...

Securing Agentic AI: a Comprehensive Threat Model and Mitigation Framework for Generative AI Agents

As generative AI GenAI agents become more common in enterprise settings, they introduce security challenges that differ significantly from those posed by traditional systems. These agents are not just LLMs; they reason, remember, and act, often with minimal human oversight. This paper introduces ...

PHSafe: Disclosure Avoidance for the 2020 Census Supplemental Demographic and Housing Characteristics File (S-DHC)

This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the 2020 Census Supplemental Demographic and Housing Characteristics File S-DHC. The tabulations contain statistics of counts of U.S. persons living in certain types of households, including...

Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks

Hierarchical Federated Learning HFL has recently emerged as a promising solution for intelligent decision-making in vehicular networks, helping to address challenges such as limited communication resources, high vehicle mobility, and data heterogeneity. However, HFL remains vulnerable to...

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures

As large language models LLMs continue to evolve, it is critical to assess the security threats and vulnerabilities that may arise both during their training phase and after models have been deployed. This survey seeks to define and categorize the various attacks targeting LLMs, distinguishing...

Poster: Machine Learning for Vulnerability Detection As Target Oracle in Automated Fuzz Driver Generation

In vulnerability detection, machine learning has been used as an effective static analysis technique, although it suffers from a significant rate of false positives. Contextually, in vulnerability discovery, fuzzing has been used as an effective dynamic analysis technique, although it requires...

Good News for Script Kiddies? Evaluating Large Language Models for Automated Exploit Generation

Large Language Models LLMs have demonstrated remarkable capabilities in code-related tasks, raising concerns about their potential for automated exploit generation AEG. This paper presents the first systematic study on LLMs' effectiveness in AEG, evaluating both their cooperativeness and technica...

Adaptive Wizard for Removing Cross-Tier Misconfigurations in Active Directory

Security vulnerabilities in Windows Active Directory AD systems are typically modeled using an attack graph and hardening AD systems involves an iterative workflow: security teams propose an edge to remove, and IT operations teams manually review these fixes before implementing the removal. As...

HoneyBee: Efficient Role-Based Access Control for Vector Databases Via Dynamic Partitioning

As vector databases gain traction in enterprise applications, robust access control has become critical to safeguard sensitive data. Access control in these systems is often implemented through hybrid vector queries, which combine nearest neighbor search on vector data with relational predicates...

Disassembly As Weighted Interval Scheduling with Learned Weights

Disassembly is the first step of a variety of binary analysis and transformation techniques, such as reverse engineering, or binary rewriting. Recent disassembly approaches consist of three phases: an exploration phase, that overapproximates the binary's code; an analysis phase, that assigns...

The DCR Delusion: Measuring the Privacy Risk of Synthetic Data

Synthetic data has become an increasingly popular way to share data without revealing sensitive information. Though Membership Inference Attacks MIAs are widely considered the gold standard for empirically assessing the privacy of a synthetic dataset, practitioners and researchers often rely on...

Machine Learning for Cyber-Attack Identification from Traffic Flows

This paper presents our simulation of cyber-attacks and detection strategies on the traffic control system in Daytona Beach, FL. using Raspberry Pi virtual machines and the OPNSense firewall, along with traffic dynamics from SUMO and exploitation via the Metasploit framework. We try to answer the...

SafeTab-P: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File a (Detailed DHC-A)

This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the Detailed Demographic and Housing Characteristics File A Detailed DHC-A of the 2020 Census. The tabulations contain statistics counts of demographic characteristics of the entire population of...

VIDSTAMP: a Temporally-Aware Watermark for Ownership and Integrity in Video Diffusion Models

The rapid rise of video diffusion models has enabled the generation of highly realistic and temporally coherent videos, raising critical concerns about content authenticity, provenance, and misuse. Existing watermarking approaches, whether passive, post-hoc, or adapted from image-based techniques...

Watermark Overwriting Attack on StegaStamp Algorithm

This paper presents an attack method on the StegaStamp watermarking algorithm that completely removes watermarks from an image with minimal quality loss, developed as part of the NeurIPS "Erasing the invisible" competition...

SafeTab-H: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File B (Detailed DHC-B)

This article describes SafeTab-H, a disclosure avoidance algorithm applied to the release of the U.S. Census Bureau's Detailed Demographic and Housing Characteristics File B Detailed DHC-B as part of the 2020 Census. The tabulations contain household statistics about household type and tenure...

Allocation of Heterogeneous Resources in General Lotto Games

The allocation of resources plays an important role in the completion of system objectives and tasks, especially in the presence of strategic adversaries. Optimal allocation strategies are becoming increasingly more complex, given that multiple heterogeneous types of resources are at a system...

Modeling Behavioral Preferences of Cyber Adversaries Using Inverse Reinforcement Learning

This paper presents a holistic approach to attacker preference modeling from system-level audit logs using inverse reinforcement learning IRL. Adversary modeling is an important capability in cybersecurity that lets defenders characterize behaviors of potential attackers, which enables attributio...

Fine-Grained Manipulation Attacks to Local Differential Privacy Protocols for Data Streams

Local Differential Privacy LDP enables massive data collection and analysis while protecting end users' privacy against untrusted aggregators. It has been applied to various data types e.g., categorical, numerical, and graph data and application settings e.g., static and streaming. Recent finding...

Building a Secure Agentic AI Application Leveraging A2A Protocol

As Agentic AI systems evolve from basic workflows to complex multi agent collaboration, robust protocols such as Google's Agent2Agent A2A become essential enablers. To foster secure adoption and ensure the reliability of these complex interactions, understanding the secure implementation of A2A i...

WordPress NewsBlogger Theme 0.2.5.1 Shell Upload

WordPress NewsBlogger Theme versions 0.2.5.1 and below suffer from a remote shell upload vulnerability...

CISA: Dams Sector Crisis Management Handbook

The Dams Sector Crisis Management Handbook 2025 introduces crisis management concepts, explains how crisis management measures are an important component of an overall risk management framework, and highlights guidelines to apply these concepts to dams and related infrastructure...

Explainable Machine Learning for Cyberattack Identification from Traffic Flows

The increasing automation of traffic management systems has made them prime targets for cyberattacks, disrupting urban mobility and public safety. Traditional network-layer defenses are often inaccessible to transportation agencies, necessitating a machine learning-based approach that relies sole...

Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability

While machine learning has significantly advanced Network Intrusion Detection Systems NIDS, particularly within IoT environments where devices generate large volumes of data and are increasingly susceptible to cyber threats, these models remain vulnerable to adversarial attacks. Our research...

A Rusty Link in the AI Supply Chain: Detecting Evil Configurations in Model Repositories

Recent advancements in large language models LLMs have spurred the development of diverse AI applications from code generation and video editing to text generation; however, AI supply chains such as Hugging Face, which host pretrained models and their associated configuration files contributed by...

A False Sense of Privacy: Evaluating Textual Data Sanitization beyond Surface-Level Privacy Leakage

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LLM Watermarking Using Mixtures and Statistical-To-Computational Gaps

Given a text, can we determine whether it was generated by a large language model LLM or by a human? A widely studied approach to this problem is watermarking. We propose an undetectable and elementary watermarking scheme in the closed setting. Also, in the harder open setting, where the adversar...

Capability-Based Multi-Tenant Access Management in Crowdsourced Drone Services

We propose a capability-based access control method that leverages OAuth 2.0 and Verifiable Credentials VCs to share resources in crowdsourced drone services. VCs securely encode claims about entities, offering flexibility. However, standardized protocols for VCs are lacking, limiting their...

Securing the Future of IVR: AI-Driven Innovation with Agile Security, Data Regulation, and Ethical AI Integration

The rapid digitalization of communication systems has elevated Interactive Voice Response IVR technologies to become critical interfaces for customer engagement. With Artificial Intelligence AI now driving these platforms, ensuring secure, compliant, and ethically designed development practices i...

Active Sybil Attack and Efficient Defense Strategy in IPFS DHT

The InterPlanetary File System IPFS is a decentralized peer-to-peer P2P storage that relies on Kademlia, a Distributed Hash Table DHT structure commonly used in P2P systems for its proved scalability. However, DHTs are known to be vulnerable to Sybil attacks, in which a single entity controls...

Confidential Serverless Computing

Although serverless computing offers compelling cost and deployment simplicity advantages, a significant challenge remains in securely managing sensitive data as it flows through the network of ephemeral function executions in serverless computing environments within untrusted clouds. While...

Analysis of the Vulnerability of Machine Learning Regression Models to Adversarial Attacks Using Data from 5G Wireless Networks

This article describes the process of creating a script and conducting an analytical study of a dataset using the DeepMIMO emulator. An advertorial attack was carried out using the FGSM method to maximize the gradient. A comparison is made of the effectiveness of binary classifiers in the task of...

HoneyWin: High-Interaction Windows Honeypot in Enterprise Environment

Windows operating systems OS are ubiquitous in enterprise Information Technology IT and operational technology OT environments. Due to their widespread adoption and known vulnerabilities, they are often the primary targets of malware and ransomware attacks. With 93% of the ransomware targeting...

PatchFuzz: Patch Fuzzing for JavaScript Engines

Patch fuzzing is a technique aimed at identifying vulnerabilities that arise from newly patched code. While researchers have made efforts to apply patch fuzzing to testing JavaScript engines with considerable success, these efforts have been limited to using ordinary test cases or publicly...

Attack and Defense Techniques in Large Language Models: a Survey and New Perspectives

Large Language Models LLMs have become central to numerous natural language processing tasks, but their vulnerabilities present significant security and ethical challenges. This systematic survey explores the evolving landscape of attack and defense techniques in LLMs. We classify attacks into...

Addressing Noise and Stochasticity in Fraud Detection for Service Networks

Fraud detection is crucial in social service networks to maintain user trust and improve service network security. Existing spectral graph-based methods address this challenge by leveraging different graph filters to capture signals with different frequencies in service networks. However, most...

Non-Adaptive Cryptanalytic Time-Space Lower Bounds Via a Shearer-Like Inequality for Permutations

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Protocol-Agnostic and Data-Free Backdoor Attacks on Pre-Trained Models in RF Fingerprinting

While supervised deep neural networks DNNs have proven effective for device authentication via radio frequency RF fingerprinting, they are hindered by domain shift issues and the scarcity of labeled data. The success of large language models has led to increased interest in unsupervised pre-train...

OET: Optimization-Based Prompt Injection Evaluation Toolkit

Large Language Models LLMs have demonstrated remarkable capabilities in natural language understanding and generation, enabling their widespread adoption across various domains. However, their susceptibility to prompt injection attacks poses significant security risks, as adversarial inputs can...

Spill the Beans: Exploiting CPU Cache Side-Channels to Leak Tokens from Large Language Models

Side-channel attacks on shared hardware resources increasingly threaten confidentiality, especially with the rise of Large Language Models LLMs. In this work, we introduce Spill The Beans, a novel application of cache side-channels to leak tokens generated by an LLM. By co-locating an attack...

Auditing without Leaks Despite Curiosity

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A Novel Feature-Aware Chaotic Image Encryption Scheme for Data Security and Privacy in IoT and Edge Networks

The security of image data in the Internet of Things IoT and edge networks is crucial due to the increasing deployment of intelligent systems for real-time decision-making. Traditional encryption algorithms such as AES and RSA are computationally expensive for resource-constrained IoT devices and...