Zero Trust Security Model Implementation in Microservices Architectures Using Identity Federation

The microservice bombshells that have been linked with the microservice expansion have altered the application architectures, offered agility and scalability in terms of complexity in security trade-offs. Feeble legacy-based perimeter-based policies are unable to offer safeguard to distributed...

Tight Analysis of a Grover-Based Quantum Secret Sharing Scheme

Secret-sharing schemes allow a dealer to split a secret into multiple "shares" and distribute them individually among many parties while mandating certain constraints on its reconstruction. Such protocols are usually executed over a secure communication channel since an eavesdropper, after...

GPT-5 at CTFs: Case Studies from Top-Tier Cybersecurity Events

OpenAI and DeepMind's AIs recently got gold at the IMO math olympiad and ICPC programming competition. We show frontier AI is similarly good at hacking by letting GPT-5 compete in elite CTF cybersecurity competitions. In one of this year's hardest events, it outperformed 93% of humans finishing...

Automated and Explainable Denial of Service Analysis for AI-Driven Intrusion Detection Systems

With the increasing frequency and sophistication of Distributed Denial of Service DDoS attacks, it has become critical to develop more efficient and interpretable detection methods. Traditional detection systems often struggle with scalability and transparency, hindering real-time response and...

Unclonable Cryptography in Linear Quantum Memory

Quantum cryptography is a rapidly-developing area which leverages quantum information to accomplish classically-impossible tasks. In many of these protocols, quantum states are used as long-term cryptographic keys. Typically, this is to ensure the keys cannot be copied by an adversary, owing to t...

Large Language Models for Cyber Security

This paper studies the integration off Large Language Models into cybersecurity tools and protocols. The main issue discussed in this paper is how traditional rule-based and signature based security systems are not enough to deal with modern AI powered cyber threats. Cybersecurity industry is...

SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection

The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for 1 detecting malicious cyberattacks and 2 identifying faulty...

Hybrid Fuzzing with LLM-Guided Input Mutation and Semantic Feedback

Software fuzzing has become a cornerstone in automated vulnerability discovery, yet existing mutation strategies often lack semantic awareness, leading to redundant test cases and slow exploration of deep program states. In this work, I present a hybrid fuzzing framework that integrates static an...

Security and Privacy Management of IoT Using Quantum Computing

The convergence of the Internet of Things IoT and quantum computing is redefining the security paradigm of interconnected digital systems. Classical cryptographic algorithms such as RSA, Elliptic Curve Cryptography ECC, and Advanced Encryption Standard AES have long provided the foundation for...

Certified Randomness Amplification by Dynamically Probing Remote Random Quantum States

Cryptography depends on truly unpredictable numbers, but physical sources emit biased or correlated bits. Quantum mechanics enables the amplification of imperfect randomness into nearly perfect randomness, but prior demonstrations have required physically co-located, loophole-free Bell tests,...

Specification-Guided Vulnerability Detection with Large Language Models

Large language models LLMs have achieved remarkable progress in code understanding tasks. However, they demonstrate limited performance in vulnerability detection and struggle to distinguish vulnerable code from patched code. We argue that LLMs lack understanding of security specifications -- the...

Smartphone User Fingerprinting on Wireless Traffic

Due to the openness of the wireless medium, smartphone users are susceptible to user privacy attacks, where user privacy information is inferred from encrypted Wi-Fi wireless traffic. Existing attacks are limited to recognizing mobile apps and their actions and cannot infer the smartphone user...

Temporal Analysis Framework for Intrusion Detection Systems: A Novel Taxonomy for Time-Aware Cybersecurity

Most intrusion detection systems still identify attacks only after significant damage has occurred, detecting late-stage tactics rather than early indicators of compromise. This paper introduces a temporal analysis framework and taxonomy for time-aware network intrusion detection. Through a...

Security Analysis of Agentic AI Communication Protocols: A Comparative Evaluation

Multi-agent systems MAS powered by artificial intelligence AI are increasingly foundational to complex, distributed workflows. Yet, the security of their underlying communication protocols remains critically under-examined. This paper presents the first empirical, comparative security analysis of...

Whisper Leak: A Side-Channel Attack on Large Language Models

Large Language Models LLMs are increasingly deployed in sensitive domains including healthcare, legal services, and confidential communications, where privacy is paramount. This paper introduces Whisper Leak, a side-channel attack that infers user prompt topics from encrypted LLM traffic by...

Design and Detection of Covert Man-In-The-Middle Cyberattacks on Water Treatment Plants

Cyberattacks targeting critical infrastructures, such as water treatment facilities, represent significant threats to public health, safety, and the environment. This paper introduces a systematic approach for modeling and assessing covert man-in-the-middle MitM attacks that leverage system...

Jailbreaking in the Haystack

Recent advances in long-context language models LMs have enabled million-token inputs, expanding their capabilities across complex tasks like computer-use agents. Yet, the safety implications of these extended contexts remain unclear. To bridge this gap, we introduce NINJA short for...

On the Dangers of Poisoned LLMs in Security Automation

This paper investigates some of the risks introduced by "LLM poisoning," the intentional or unintentional introduction of malicious or biased data during model training. We demonstrate how a seemingly improved LLM, fine-tuned on a limited dataset, can introduce significant bias, to the extent tha...

Enhancing NTRUEncrypt Security Using Markov Chain Monte Carlo Methods: Theory and Practice

This paper presents a novel framework for enhancing the quantum resistance of NTRUEncrypt using Markov Chain Monte Carlo MCMC methods. We establish formal bounds on sampling efficiency and provide security reductions to lattice problems, bridging theoretical guarantees with practical...

Designing Proportionate Cybersecurity Frameworks for European Micro-Enterprises: Lessons from the Squad 2025 Case

Micro and small enterprises SMEs account for most European businesses yet remain highly vulnerable to cyber threats. This paper analyses the design logic of a recent European policy initiative -- the Squad 2025 Playbook on Cybersecurity Awareness for Micro-SMEs -- to extract general principles fo...

Aether - Adaptive Exploit and Threat Hunting Engine for EVM-based Repositories

Aether is a Python-based framework for analyzing Solidity smart contracts, generating vulnerability findings, producing Foundry-based proof-of-concept PoC tests, and optionally validating those tests on mainnet forks. It combines static analysis, prompt-driven LLM analysis, and AI-ensemble...

AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models

Large Language Models LLMs remain vulnerable to jailbreaking attacks where adversarial prompts elicit harmful outputs, yet most evaluations focus on single-turn interactions while real-world attacks unfold through adaptive multi-turn conversations. We present AutoAdv, a training-free framework fo...

Trustworthy Quantum Machine Learning: A Roadmap for Reliability, Robustness, and Security in the NISQ Era

Quantum machine learning QML is a promising paradigm for tackling computational problems that challenge classical AI. Yet, the inherent probabilistic behavior of quantum mechanics, device noise in NISQ hardware, and hybrid quantum-classical execution pipelines introduce new risks that prevent...

1 PoCo: Agentic Proof-Of-Concept Exploit Generation for Smart Contracts

Smart contracts operate in a highly adversarial environment, where vulnerabilities can lead to substantial financial losses. Thus, smart contracts are subject to security audits. In auditing, proof-of-concept PoC exploits play a critical role by demonstrating to the stakeholders that the reported...

Federated Cyber Defense: Privacy-Preserving Ransomware Detection across Distributed Systems

Detecting malware, especially ransomware, is essential to securing today's interconnected ecosystems, including cloud storage, enterprise file-sharing, and database services. Training high-performing artificial intelligence AI detectors requires diverse datasets, which are often distributed acros...

Detecting Vulnerabilities from Issue Reports for Internet-Of-Things

Timely identification of issue reports reflecting software vulnerabilities is crucial, particularly for Internet-of-Things IoT where analysis is slower than non-IoT systems. While Machine Learning ML and Large Language Models LLMs detect vulnerability-indicating issues in non-IoT systems, their I...

Scam Shield: Multi-Model Voting and Fine-Tuned LLMs against Adversarial Attacks

Scam detection remains a critical challenge in cybersecurity as adversaries craft messages that evade automated filters. We propose a Hierarchical Scam Detection System HSDS that combines a lightweight multi-model voting front end with a fine-tuned LLaMA 3.1 8B Instruct back end to improve accura...

Machine and Deep Learning for Indoor UWB Jammer Localization

Ultra-wideband UWB localization delivers centimeter-scale accuracy but is vulnerable to jamming attacks, creating security risks for asset tracking and intrusion detection in smart buildings. Although machine learning ML and deep learning DL methods have improved tag localization, localizing...

Characterizing Build Compromises through Vulnerability Disclosure Analysis

The software build process transforms source code into deployable artifacts, representing a critical yet vulnerable stage in software development. Build infrastructure security poses unique challenges: the complexity of multi-component systems source code, dependencies, build tools, the difficult...

Android Malware Detection: A Machine Learning Approach

This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and...

AthenaBench: A Dynamic Benchmark for Evaluating LLMs in Cyber Threat Intelligence

Large Language Models LLMs have demonstrated strong capabilities in natural language reasoning, yet their application to Cyber Threat Intelligence CTI remains limited. CTI analysis involves distilling large volumes of unstructured reports into actionable knowledge, a process where LLMs could...

Towards Ultra-Low Latency: Binarized Neural Network Architectures for In-Vehicle Network Intrusion Detection

The Control Area Network CAN protocol is essential for in-vehicle communication, facilitating high-speed data exchange among Electronic Control Units ECUs. However, its inherent design lacks robust security features, rendering vehicles susceptible to cyberattacks. While recent research has...

A Large Scale Study of AI-Based Binary Function Similarity Detection Techniques for Security Researchers and Practitioners

Binary Function Similarity Detection BFSD is a foundational technique in software security, underpinning a wide range of applications including vulnerability detection, malware analysis. Recent advances in AI-based BFSD tools have led to significant performance improvements. However, existing...

Meta-Learning Based Radio Frequency Fingerprinting for GNSS Spoofing Detection

The rapid development of technology has led to an increase in the number of devices that rely on position, velocity, and time PVT information to perform their functions. As such, the Global Navigation Satellite Systems GNSS have been adopted as one of the most promising solutions to provide PVT...

An Efficient Anomaly Detection Framework for Wireless Sensor Networks Using Markov Process

Wireless Sensor Networks forms the backbone of modern cyber physical systems used in various applications such as environmental monitoring, healthcare monitoring, industrial automation, and smart infrastructure. Ensuring the reliability of data collected through these networks is essential as the...

Penetrating the Hostile: Detecting DeFi Protocol Exploits through Cross-Contract Analysis

Decentralized finance DeFi protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding $80 billion. Current tools detect and locate possible vulnerabilities in contracts by analyzing the state changes...

Mind the Gap: Missing Cyber Threat Coverage in NIDS Datasets for the Energy Sector

Network Intrusion Detection Systems NIDS developed using publicly available datasets predominantly focus on enterprise environments, raising concerns about their effectiveness for converged Information Technology IT and Operational Technology OT in energy infrastructures. This study evaluates the...

Exploiting Latent Space Discontinuities for Building Universal LLM Jailbreaks and Data Extraction Attacks

The rapid proliferation of Large Language Models LLMs has raised significant concerns about their security against adversarial attacks. In this work, we propose a novel approach to crafting universal jailbreaks and data extraction attacks by exploiting latent space discontinuities, an architectur...

MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection

High-quality data scarcity hinders malware detection, limiting ML performance. We introduce MalDataGen, an open-source modular framework for generating high-fidelity synthetic tabular data using modular deep learning models e.g., WGAN-GP, VQ-VAE. Evaluated via dual validation TR-TS/TS-TR, seven...

Windows Persistent Task Scheduler

This Metasploit module establishes persistence by creating a scheduled task to run a payload...

On Selecting Few-Shot Examples for LLM-Based Code Vulnerability Detection

Large language models LLMs have demonstrated impressive capabilities for many coding tasks, including summarization, translation, completion, and code generation. However, detecting code vulnerabilities remains a challenging task for LLMs. An effective way to improve LLM performance is in-context...

Prevalence of Security and Privacy Risk-Inducing Usage of AI-Based Conversational Agents

Recent improvement gains in large language models LLMs have lead to everyday usage of AI-based Conversational Agents CAs. At the same time, LLMs are vulnerable to an array of threats, including jailbreaks and, for example, causing remote code execution when fed specific inputs. As a result, users...

MH-1M: A 1.34 Million-Sample Comprehensive Multi-Feature Android Malware Dataset for Machine Learning, Deep Learning, Large Language Models, and Threat Intelligence Research

We present MH-1M, one of the most comprehensive and up-to-date datasets for advanced Android malware research. The dataset comprises 1,340,515 applications, encompassing a wide range of features and extensive metadata. To ensure accurate malware classification, we employ the VirusTotal API,...

Sustaining Cyber Awareness: The Long-Term Impact of Continuous Phishing Training and Emotional Triggers

Phishing constitutes more than 90% of successful cyberattacks globally, remaining one of the most persistent threats to organizational security. Despite organizations tripling their cybersecurity budgets between 2015 and 2025, the human factor continues to pose a critical vulnerability. This stud...

Security Audit of Intel ICE Driver for E810 Network Interface Card

The security of enterprise-grade networking hardware and software is critical to ensuring the integrity, availability, and confidentiality of data in modern cloud and data center environments. Network interface controllers NICs play a pivotal role in high-performance computing and virtualization,...

Service Upstart Persistence

This Metasploit module will create a service on the box, and mark it for auto-restart. You need enough access to write service files and potentially restart services...

Coordinated Position Falsification Attacks and Countermeasures for Location-Based Services

With the rise of location-based service LBS applications that rely on terrestrial and satellite infrastructures e.g., GNSS and crowd-sourced Wi-Fi, Bluetooth, cellular, and IP databases for positioning, ensuring their integrity and security is paramount. However, we demonstrate that these...

A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection

Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience. Graph-based approaches have become increasingly important for modeling entity interactions, yet most rely on homogeneou...

TEE.fail: Breaking Trusted Execution Environments via DDR5 Memory Bus Interposition

In this paper, the researchers show that the security guarantees of modern TEE offerings by Intel and AMD can be broken cheaply and easily, by building a memory interposition device that allows attackers to physically inspect all memory traffic inside a DDR5 server...

Twin-Field Quantum Key Distribution: Protocols, Security, and Open Problems

Twin-Field Quantum Key Distribution TF-QKD has emerged as a potential protocol for long distance secure communication, overcoming the rate-distance limitations of conventional quantum key distribution without requiring trusted repeaters. By having two parties transmit phase encoded weak coherent...