PhishIntentionLLM: Uncovering Phishing Website Intentions through Multi-Agent Retrieval-Augmented Generation

Phishing websites remain a major cybersecurity threat, yet existing methods primarily focus on detection, while the recognition of underlying malicious intentions remains largely unexplored. To address this gap, we propose PhishIntentionLLM, a multi-agent retrieval-augmented generation RAG...

Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems

Large Language Models LLMs deployed in enterprise settings e.g., as Microsoft 365 Copilot face novel security challenges. One critical threat is prompt inference attacks: adversaries chain together seemingly benign prompts to gradually extract confidential data. In this paper, we present a...

BACFuzz: Exposing the Silence on Broken Access Control Vulnerabilities in Web Applications

Broken Access Control BAC remains one of the most critical and widespread vulnerabilities in web applications, allowing attackers to access unauthorized resources or perform privileged actions. Despite its severity, BAC is underexplored in automated testing due to key challenges: the lack of...

ChatGPTUtil Cross Site Scripting

ChatGPTUtil is an AI-powered chatbot assistant, providing access to both ChatGPT and an AI image generator. A self cross site scripting vulnerability exists in the chat component. This can lead to cookie theft leading to remote account hijacking...

Attacking Interpretable NLP Systems

Studies have shown that machine learning systems are vulnerable to adversarial examples in theory and practice. Where previous attacks have focused mainly on visual models that exploit the difference between human and machine perception, text-based models have also fallen victim to these attacks...

Liner Insecure Direct Object Reference / Brute Force

Liner is a reliable AI search engine with over 10 million users worldwide. It is vulnerable to an insecure direct object reference vulnerability. Conversation histories for all users are stored on the server. However, Liner's server does not distinguish the ownership or sharing status of individu...

Ai2 Insecure Direct Object Reference

Ai2 is a Seattle based non-profit AI research institute. Ai2 provides a playground web application to chat that is susceptible to an insecure direct object reference vulnerability. An attacker can exploit this IDOR to tamper other users' conversation...

DP2Guard: a Lightweight and Byzantine-Robust Privacy-Preserving Federated Learning Scheme for Industrial IoT

Privacy-Preserving Federated Learning PPFL has emerged as a secure distributed Machine Learning ML paradigm that aggregates locally trained gradients without exposing raw data. To defend against model poisoning threats, several robustness-enhanced PPFL schemes have been proposed by integrating...

SVAgent: AI Agent for Hardware Security Verification Assertion

Verification using SystemVerilog assertions SVA is one of the most popular methods for detecting circuit design vulnerabilities. However, with the globalization of integrated circuit design and the continuous upgrading of security requirements, the SVA development model has exposed major...

MFAz: Historical Access Based Multi-Factor Authorization

Unauthorized access remains one of the critical security challenges in the realm of cybersecurity. With the increasing sophistication of attack techniques, the threat of unauthorized access is no longer confined to the conventional ones, such as exploiting weak access control policies. Instead,...

Scaling Decentralized Learning with FLock

Fine-tuning the large language models LLMs are prevented by the deficiency of centralized control and the massive computing and communication overhead on the decentralized schemes. While the typical standard federated learning FL supports data privacy, the central server requirement creates a...

Dippyis Insecure Direct Object Reference / Brute Force

Dippyis a popular website to chat with millions of proactive AI characters. The Dippy chat suffers from an insecure direct object reference vulnerability. Conversation histories for all users are stored on the server. However, Dippy's server does not distinguish the ownership or sharing status of...

In-Context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems

Recent advances in biometric systems have significantly improved the detection and prevention of fraudulent activities. However, as detection methods improve, attack techniques become increasingly sophisticated. Attacks on face recognition systems can be broadly divided into physical and digital...

Exploiting Context-Dependent Duration Features for Voice Anonymization Attack Systems

The temporal dynamics of speech, encompassing variations in rhythm, intonation, and speaking rate, contain important and unique information about speaker identity. This paper proposes a new method for representing speaker characteristics by extracting context-dependent duration embeddings from...

Quantum Skyshield: Quantum Key Distribution and Post-Quantum Authentication for Low-Altitude Wireless Networks in Adverse Skies

Recently, low-altitude wireless networks LAWNs have emerged as a critical backbone for supporting the low-altitude economy, particularly with the densification of unmanned aerial vehicles UAVs and high-altitude platforms HAPs. To meet growing data demands, some LAWN deployments incorporate...

Metaverse Security and Privacy Research: a Systematic Review

The rapid growth of metaverse technologies, including virtual worlds, augmented reality, and lifelogging, has accelerated their adoption across diverse domains. This rise exposes users to significant new security and privacy challenges due to sociotechnical complexity, pervasive connectivity, and...

Time Entangled Quantum Blockchain with Phase Encoding for Classical Data

With rapid advancements in quantum computing, it is widely believed that there will be quantum hardware capable of compromising classical cryptography and hence, the internet and the current information security infrastructure in the coming decade. This is mainly due to the operational realizatio...

Frame-Level Temporal Difference Learning for Partial Deepfake Speech Detection

Detecting partial deepfake speech is essential due to its potential for subtle misinformation. However, existing methods depend on costly frame-level annotations during training, limiting real-world scalability. Also, they focus on detecting transition artifacts between bonafide and deepfake...

Data-Plane Telemetry to Mitigate Long-Distance BGP Hijacks

Poor security of Internet routing enables adversaries to divert user data through unintended infrastructures hijack. Of particular concern -- and the focus of this paper -- are cases where attackers reroute domestic traffic through foreign countries, exposing it to surveillance, bypassing legal...

PromptArmor: Simple yet Effective Prompt Injection Defenses

Despite their potential, recent research has demonstrated that LLM agents are vulnerable to prompt injection attacks, where malicious prompts are injected into the agent's input, causing it to perform an attacker-specified task rather than the intended task provided by the user. In this paper, we...

Adaptive Network Security Policies Via Belief Aggregation and Rollout

Evolving security vulnerabilities and shifting operational conditions require frequent updates to network security policies. These updates include adjustments to incident response procedures and modifications to access controls, among others. Reinforcement learning methods have been proposed for...

A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption

Federated Learning FL enables collaborative model training without sharing raw data, making it a promising approach for privacy-sensitive domains. Despite its potential, FL faces significant challenges, particularly in terms of communication overhead and data privacy. Privacy-preserving Technique...

Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data

False Data Injection Attacks FDIAs pose severe security risks to smart grids by manipulating measurement data collected from spatially distributed devices such as SCADA systems and PMUs. These measurements typically exhibit Non-Independent and Identically Distributed Non-IID characteristics acros...

Jamming-Resistant AAV Communications: a Multichannel-Aided Approach

Jamming cancellation is essential to reliable unmanned autonomous vehicle AAV communications in the presence of malicious jammers. In this paper, we develop a practical multichannel-aided jamming cancellation method to realize secure AAV communications. The proposed method is capable of...

Manipulating LLM Web Agents with Indirect Prompt Injection Attack Via HTML Accessibility Tree

This work demonstrates that LLM-based web navigation agents offer powerful automation capabilities but are vulnerable to Indirect Prompt Injection IPI attacks. We show that adversaries can embed universal adversarial triggers in webpage HTML to hijack agent behavior that utilizes the accessibilit...

Hybrid Classical-Quantum Rainbow Table Attack on Human Passwords

Passwords that are long and human-generated pose a challenge for both classical and quantum attacks due to their irregular structure and large search space. In this work, we present an enhanced classical-quantum hybrid attack tailored to this scenario. We build rainbow tables using dictionary-bas...

Enhancing Resilience against Jamming Attacks: a Cooperative Anti-Jamming Method Using Direction Estimation

The inherent vulnerability of wireless communication necessitates strategies to enhance its security, particularly in the face of jamming attacks. This paper uses the collaborations of multiple sensing nodes SNs in the wireless network to present a cooperative anti-jamming approach CAJ designed t...

CANDoSA: a Hardware Performance Counter-Based Intrusion Detection System for DoS Attacks on Automotive CAN Bus

The Controller Area Network CAN protocol, essential for automotive embedded systems, lacks inherent security features, making it vulnerable to cyber threats, especially with the rise of autonomous vehicles. Traditional security measures offer limited protection, such as payload encryption and...

Collusion-Resilient Hierarchical Secure Aggregation with Heterogeneous Security Constraints

Motivated by federated learning FL, secure aggregation SA aims to securely compute, as efficiently as possible, the sum of a set of inputs distributed across many users. To understand the impact of network topology, hierarchical secure aggregation HSA investigated the communication and secret key...

Measuring CEX-DEX Extracted Value and Searcher Profitability: the Darkest of the MEV Dark Forest

This paper provides a comprehensive empirical analysis of the economics and dynamics behind arbitrages between centralized and decentralized exchanges CEX-DEX on Ethereum. We refine heuristics to identify arbitrage transactions from on-chain data and introduce a robust empirical framework to...

Privacy-Preserving Drone Navigation through Homomorphic Encryption for Collision Avoidance

As drones increasingly deliver packages in neighborhoods, concerns about collisions arise. One solution is to share flight paths within a specific zip code, but this compromises business privacy by revealing delivery routes. For example, it could disclose which stores send packages to certain...

Wireshark Analyzer 4.4.8

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers. Thi...

The CryptoNeo Threat Modelling Framework (CNTMF): Securing Neobanks and Fintech in Integrated Blockchain Ecosystems

The rapid integration of blockchain, cryptocurrency, and Web3 technologies into digital banks and fintech operations has created an integrated environment blending traditional financial systems with decentralised elements. This paper introduces the CryptoNeo Threat Modelling Framework CNTMF, a...

An Adversarial-Driven Experimental Study on Deep Learning for RF Fingerprinting

Radio frequency RF fingerprinting, which extracts unique hardware imperfections of radio devices, has emerged as a promising physical-layer device identification mechanism in zero trust architectures and beyond 5G networks. In particular, deep learning DL methods have demonstrated state-of-the-ar...

Stablecoins: Fundamentals, Emerging Issues, and Open Challenges

Stablecoins, with a capitalization exceeding 200 billion USD as of January 2025, have shown significant growth, with annual transaction volumes exceeding 10 trillion dollars in 2023 and nearly doubling that figure in 2024. This exceptional success has attracted the attention of traditional...

Chain Table: Protecting Table-Level Data Integrity by Digital Ledger Technology

The rise of blockchain and Digital Ledger Technology DLT has gained wide traction. Instead of relying on a traditional centralized data authority, a blockchain system consists of digitally entangled block data shared across a distributed network. The specially designed chain data structure and it...

Developers Insight on Manifest V3 Privacy and Security Webextensions

Webextensions can improve web browser privacy, security, and user experience. The APIs offered by the browser to webextensions affect possible functionality. Currently, Chrome transitions to a modified set of APIs called Manifest v3. This paper studies the challenges and opportunities of Manifest...

Breaking the Illusion of Security Via Interpretation: Interpretable Vision Transformer Systems under Attack

Vision transformer ViT models, when coupled with interpretation models, are regarded as secure and challenging to deceive, making them well-suited for security-critical domains such as medical applications, autonomous vehicles, drones, and robotics. However, successful attacks on these systems ca...

TopicAttack: an Indirect Prompt Injection Attack Via Topic Transition

Large language models LLMs have shown remarkable performance across a range of NLP tasks. However, their strong instruction-following capabilities and inability to distinguish instructions from data content make them vulnerable to indirect prompt injection attacks. In such attacks, instructions...

Faraday 5.15.1

Faraday is a tool that introduces a new concept called IPE, or Integrated Penetration-Test Environment. It is a multiuser penetration test IDE designed for distribution, indexation and analysis of the generated data during the process of a security audit. The main purpose of Faraday is to re-use...

Kintsugi: Decentralized E2EE Key Recovery

Kintsugi is a protocol for key recovery, allowing a user to regain access to end-to-end encrypted data after they have lost their device, but still have their potentially low-entropy password. Existing E2EE key recovery methods, such as those deployed by Signal and WhatsApp, centralize trust by...

Quantum Blockchain Survey: Foundations, Trends, and Gaps

Quantum computing poses fundamental risks to classical blockchain systems by undermining widely used cryptographic primitives. In response, two major research directions have emerged: post-quantum blockchains, which integrate quantum-resistant algorithms, and quantum blockchains, which leverage...

Using Modular Arithmetic Optimized Neural Networks to Crack Affine Cryptographic Schemes Efficiently

We investigate the cryptanalysis of affine ciphers using a hybrid neural network architecture that combines modular arithmetic-aware and statistical feature-based learning. Inspired by recent advances in interpretable neural networks for modular arithmetic and neural cryptanalysis of classical...

A Crowdsensing Intrusion Detection Dataset for Decentralized Federated Learning Models

This paper introduces a dataset and experimental study for decentralized federated learning DFL applied to IoT crowdsensing malware detection. The dataset comprises behavioral records from benign and eight malware families. A total of 21,582,484 original records were collected from system calls,...

Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility

AI systems are rapidly advancing in capability, and frontier model developers broadly acknowledge the need for safeguards against serious misuse. However, this paper demonstrates that fine-tuning, whether via open weights or closed fine-tuning APIs, can produce helpful-only models. In contrast to...

Challenges in GenAI and Authentication: a Scoping Review

Authentication and authenticity have been a security challenge since the beginning of information sharing, especially in the context of digital information. With the advancement of generative artificial intelligence, these challenges have evolved, demanding a more up-to-date analysis of their...

Learning-Based Cost-Aware Defense of Parallel Server Systems against Malicious Attacks

We consider the cyber-physical security of parallel server systems, which is relevant for a variety of engineering applications such as networking, manufacturing, and transportation. These systems rely on feedback control and may thus be vulnerable to malicious attacks such as denial-of-service,...

Expanding ML-Documentation Standards for Better Security

This article presents the current state of ML-security and of the documentation of ML-based systems, models and datasets in research and practice based on an extensive review of the existing literature. It shows a generally low awareness of security aspects among ML-practitioners and organization...

A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning

Federated learning FL enables collaborative model training across decentralized clients while preserving data privacy. However, its open-participation nature exposes it to data-poisoning attacks, in which malicious actors submit corrupted model updates to degrade the global model. Existing defens...

Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques

Large Language Models LLMs are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response. With their advanced language understanding and contextual reasoning, LLMs surpass traditional methods in...