ALFA: A Safe-By-Design Approach to Mitigate Quishing Attacks Launched Via Fancy QR Codes

Phishing with Quick Response QR codes is termed as Quishing. The attackers exploit this method to manipulate individuals into revealing their confidential data. Recently, we see the colorful and fancy representations of QR codes, the 2D matrix of QR codes which does not reflect a typical mixture ...

ZkRansomware: Proof-Of-Data Recoverability and Multi-Round Game Theoretic Modeling of Ransomware Decisions

Ransomware is still one of the most serious cybersecurity threats. Victims often pay but fail to regain access to their data, while also facing the danger of losing data privacy. These uncertainties heavily shape the attacker-victim dynamics in decision-making. In this paper, we introduce and...

S-DAPT-2026: A Stage-Aware Synthetic Dataset for Advanced Persistent Threat Detection

The detection of advanced persistent threats APTs remains a crucial challenge due to their stealthy, multistage nature and the limited availability of realistic, labeled datasets for systematic evaluation. Synthetic dataset generation has emerged as a practical approach for modeling APT campaigns...

QES-Backed Virtual FIDO2 Authenticators: Architectural Options for Secure, Synchronizable WebAuthn Credentials

FIDO2 and the WebAuthn standard offer phishing-resistant, public-key based authentication but traditionally rely on device-bound cryptographic keys that are not naturally portable across user devices. Recent passkey deployments address this limitation by enabling multi-device credentials...

Multi-Regional Cloud Honeypot Dataset (MURHCAD)

This data article introduces a comprehensive, high-resolution honeynet dataset designed to support standalone analyses of global cyberattack behaviors. Collected over a continuous 72-hour window June 9 to 11, 2025 on Microsoft Azure, the dataset comprises 132,425 individual attack events captured...

Udev Persistence Script

This Metasploit module will add a script in /lib/udev/rules.d/ in order to execute a payload written on disk. It will be executed with root privileges every time a network interface other than l0 comes up. Execution is triggered through the at command, so it must be installed on the target...

The Echo Chamber Multi-Turn LLM Jailbreak

The availability of Large Language Models LLMs has led to a new generation of powerful chatbots that can be developed at relatively low cost. As companies deploy these tools, security challenges need to be addressed to prevent financial loss and reputational damage. A key security challenge is...

HogVul: Black-Box Adversarial Code Generation Framework against LM-Based Vulnerability Detectors

Recent advances in software vulnerability detection have been driven by Language Model LM-based approaches. However, these models remain vulnerable to adversarial attacks that exploit lexical and syntax perturbations, allowing critical flaws to evade detection. Existing black-box attacks on...

Cybersecurity AI: A Game-Theoretic AI for Guiding Attack and Defense

AI-driven penetration testing now executes thousands of actions per hour but still lacks the strategic intuition humans apply in competitive security. To build cybersecurity superintelligence --Cybersecurity AI exceeding best human capability-such strategic intuition must be embedded into agentic...

Accessibility Features Persistence Via Debugger Registry Key

This Metasploit module makes it possible to apply the sticky keys hack to a session with appropriate rights. The hack provides a means to get a SYSTEM shell using UI-level interaction at an RDP login screen or via a UAC confirmation dialog. The module modifies the Debug registry setting for certa...

AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections

The rise in frequency and complexity of malware attacks are viewed as a major threat to modern digital infrastructure, which means that traditional signature-based detection methods are becoming less effective. As cyber threats continue to evolve, there is a growing need for intelligent systems t...

Knowledge-Driven Multi-Turn Jailbreaking on Large Language Models

Large Language Models LLMs face a significant threat from multi-turn jailbreak attacks, where adversaries progressively steer conversations to elicit harmful outputs. However, the practical effectiveness of existing attacks is undermined by several critical limitations: they struggle to maintain ...

Unified Framework for Qualifying Security Boundary of PUFs against Machine Learning Attacks

Physical Unclonable Functions PUFs serve as lightweight, hardware-intrinsic entropy sources widely deployed in IoT security applications. However, delay-based PUFs are vulnerable to Machine Learning Attacks MLAs, undermining their assumed unclonability. There are no valid metrics for evaluating P...

Cyber Threat Detection and Vulnerability Assessment System Using Generative AI and Large Language Model

Background: Cyber-attacks have evolved rapidly in recent years, many individuals and business owners have been affected by cyber-attacks in various ways. Cyber-attacks include various threats such as ransomware, malware, phishing, and Denial of Service DoS-related attacks. Challenges: Traditional...

Quantum Secure Biometric Authentication in Decentralised Systems

Biometric authentication has become integral to digital identity systems, particularly in smart cities where it en-ables secure access to services across governance, trans-portation, and public infrastructure. Centralised archi-tectures, though widely used, pose privacy and scalabil-ity challenge...

CurricuLLM: Designing Personalized and Workforce-Aligned Cybersecurity Curricula Using Fine-Tuned LLMs

The cybersecurity landscape is constantly evolving, driven by increased digitalization and new cybersecurity threats. Cybersecurity programs often fail to equip graduates with skills demanded by the workforce, particularly concerning recent developments in cybersecurity, as curriculum design is...

Memory Poisoning Attack and Defense on Memory Based LLM-Agents

Large language model agents equipped with persistent memory are vulnerable to memory poisoning attacks, where adversaries inject malicious instructions through query only interactions that corrupt the agents long term memory and influence future responses. Recent work demonstrated that the MINJA...

Multi-Turn Jailbreaking Attack in Multi-Modal Large Language Models

In recent years, the security vulnerabilities of Multi-modal Large Language Models MLLMs have become a serious concern in the Generative Artificial Intelligence GenAI research. These highly intelligent models, capable of performing multi-modal tasks with high accuracy, are also severely susceptib...

Supporting Secured Integration of Microarchitectural Defenses

There has been a plethora of microarchitectural-level attacks leading to many proposed countermeasures. This has created an unexpected and unaddressed security issue where naive integration of those defenses can potentially lead to security vulnerabilities. This occurs when one defense changes an...

Jailbreaking Large Language Models through Iterative Tool-Disguised Attacks Via Reinforcement Learning

Large language models LLMs have demonstrated remarkable capabilities across diverse applications, however, they remain critically vulnerable to jailbreak attacks that elicit harmful responses violating human values and safety guidelines. Despite extensive research on defense mechanisms, existing...

Knowledge-To-Data: LLM-Driven Synthesis of Structured Network Traffic for Testbed-Free IDS Evaluation

Realistic, large-scale, and well-labeled cybersecurity datasets are essential for training and evaluating Intrusion Detection Systems IDS. However, they remain difficult to obtain due to privacy constraints, data sensitivity, and the cost of building controlled collection environments such as...

Finite-Size Security of QKD: Comparison of Three Proof Techniques

We compare three proof techniques for composable finite-size security of quantum key distribution under collective attacks, with emphasis on how the resulting secret-key rates behave at practically relevant block lengths. As a benchmark, we consider the BB84 protocol and evaluate finite-size...

AutoVulnPHP: LLM-Powered Two-Stage PHP Vulnerability Detection and Automated Localization

PHP's dominance in web development is undermined by security challenges: static analysis lacks semantic depth, causing high false positives; dynamic analysis is computationally expensive; and automated vulnerability localization suffers from coarse granularity and imprecise context. Additionally,...

Large Language Models for Detecting Cyberattacks on Smart Grid Protective Relays

This paper presents a large language model LLM-based framework for detecting cyberattacks on transformer current differential relays TCDRs, which, if undetected, may trigger false tripping of critical transformers. The proposed approach adapts and fine-tunes compact LLMs such as DistilBERT to...

Python Site-Specific Hook Persistence

This Metasploit module leverages Python's startup mechanism, where some files can be automatically processed during the initialization of the Python interpreter. One of those files are startup hooks site-specific, dist-packages. If these files are present in site-specific or dist-packages...

A Longitudinal Measurement Study of Log4Shell Exploitation from an Active Network Telescope

The disclosure of the Log4Shell vulnerability in December 2021 led to an unprecedented wave of global scanning and exploitation activity. A recent study provided important initial insights, but was largely limited in duration and geography, focusing primarily on European and U.S. network telescop...

Jailbreaking LLMs and VLMs: Mechanisms, Evaluation, and Unified Defense

This paper provides a systematic survey of jailbreak attacks and defenses on Large Language Models LLMs and Vision-Language Models VLMs, emphasizing that jailbreak vulnerabilities stem from structural factors such as incomplete training data, linguistic ambiguity, and generative uncertainty. It...

RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models

As large language models LLMs become integral to safety-critical applications, ensuring their robustness against adversarial prompts is paramount. However, existing red teaming datasets suffer from inconsistent risk categorizations, limited domain coverage, and outdated evaluations, hindering...

A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-Case Prototypes

Agentic AI marks an important transition from single-step generative models to systems capable of reasoning, planning, acting, and adapting over long-lasting tasks. By integrating memory, tool use, and iterative decision cycles, these systems enable continuous, autonomous workflows in real-world...

Decision-Aware Trust Signal Alignment for SOC Alert Triage

Detection systems that utilize machine learning are progressively implemented at Security Operations Centers SOCs to help an analyst to filter through high volumes of security alerts. Practically, such systems tend to reveal probabilistic results or confidence scores which are ill-calibrated and...

HoneyTrap: Deceiving Large Language Model Attackers to Honeypot Traps with Resilient Multi-Agent Defense

Jailbreak attacks pose significant threats to large language models LLMs, enabling attackers to bypass safeguards. However, existing reactive defense approaches struggle to keep up with the rapidly evolving multi-turn jailbreaks, where attackers continuously deepen their attacks to exploit...

Quantum Key Distribution without Authentication and Information Leakage

Quantum key distribution QKD is the most widely studied quantum cryptographic model that exploits quantum effects to achieve information-theoretically secure key establishment. Conventional QKD contains public classical post-processing steps that require authentication to prevent impersonation an...

AI-Driven Cybersecurity Threats: A Survey of Emerging Risks and Defensive Strategies

Artificial Intelligence's dual-use nature is revolutionizing the cybersecurity landscape, introducing new threats across four main categories: deepfakes and synthetic media, adversarial AI attacks, automated malware, and AI-powered social engineering. This paper aims to analyze emerging risks,...

Few-Shot Learning for Security Bug Report Identification

Security bug reports require prompt identification to minimize the window of vulnerability in software systems. Traditional machine learning ML techniques for classifying bug reports to identify security bug reports rely heavily on large amounts of labeled data. However, datasets for security bug...

A Critical Analysis of the Medibank Health Data Breach and Differential Privacy Solutions

This paper critically examines the 2022 Medibank health insurance data breach, which exposed sensitive medical records of 9.7 million individuals due to unencrypted storage, centralized access, and the absence of privacy-preserving analytics. To address these vulnerabilities, we propose an...

Security Parameter Analysis of the LINEture Post-Quantum Digital Signature Scheme

This paper presents a comprehensive cryptographic analysis of the security parameters of the LINEture post-quantum digital signature scheme, which is constructed using matrix algebra over elementary abelian 2-groups. We investigate the influence of three principal parameters. First, the word size...

SigInt-Hombre 1.0

SigInt-Hombre is a python script that generates derived Suricata detection rules from live URLhaus threat indicators at runtime and deploys them to the Security Onion platform for high-coverage real-time network monitoring...

LLMs, You Can Evaluate It! Design of Multi-Perspective Report Evaluation for Security Operation Centers

Security operation centers SOCs often produce analysis reports on security incidents, and large language models LLMs will likely be used for this task in the near future. We postulate that a better understanding of how veteran analysts evaluate reports, including their feedback, can help produce...

Autonomous Threat Detection and Response in Cloud Security: A Comprehensive Survey of AI-Driven Strategies

Cloud computing has changed online communities in three dimensions, which are scalability, adaptability and reduced overhead. But there are serious security concerns which are brought about by its distributed and multi-tenant characteristics. The old methods of detecting and reacting to threats...

Integrating Multi-Agent Simulation, Behavioral Forensics, and Trust-Aware Machine Learning for Adaptive Insider Threat Detection

We present a hybrid framework for adaptive insider-threat detection that tightly integrates multi-agent simulation MAS, layered Security Information and Event Management SIEM correlation, behavioral and communication forensics, trust-aware machine learning, and Theory-of-Mind ToM reasoning...

Focus on What Matters: Fisher-Guided Adaptive Multimodal Fusion for Vulnerability Detection

Software vulnerability detection is a critical task for securing software systems and can be formulated as a binary classification problem: given a code snippet, determine whether it contains a vulnerability. Existing multimodal approaches typically fuse Natural Code Sequence NCS representations...

Rethinking Secure Semantic Communications in the Age of Generative and Agentic AI: Threats and Opportunities

Semantic communication SemCom improves communication efficiency by transmitting task-relevant information instead of raw bits and is expected to be a key technology for 6G networks. Recent advances in generative AI GenAI further enhance SemCom by enabling robust semantic encoding and decoding und...

Pervasive Vulnerability Analysis and Defense for QKD-Based Quantum Private Query

Quantum Private Query QPQ based on Quantum Key Distribution QKD is among the most practically viable quantum communication protocols, with application value second only to QKD itself. However, prevalent security vulnerabilities in the post-processing stages of most existing QKD-based QPQ protocol...

Threat Detection in Social Media Networks Using Machine Learning Based Network Analysis

The accelerated development of social media websites has posed intricate security issues in cyberspace, where these sites have increasingly become victims of criminal activities including attempts to intrude into them, abnormal traffic patterns, and organized attacks. The conventional rule-based...

Quantum AI for Cybersecurity: A Hybrid Quantum-Classical Models for Attack Path Analysis

Modern cyberattacks are increasingly complex, posing significant challenges to classical machine learning methods, particularly when labeled data is limited and feature interactions are highly non-linear. In this study we investigates the potential of hybrid quantum-classical learning to enhance...

OpenRT: An Open-Source Red Teaming Framework for Multimodal LLMs

The rapid integration of Multimodal Large Language Models MLLMs into critical applications is increasingly hindered by persistent safety vulnerabilities. However, existing red-teaming benchmarks are often fragmented, limited to single-turn text interactions, and lack the scalability required for...

Exposing Hidden Interfaces: LLM-Guided Type Inference for Reverse Engineering MacOS Private Frameworks

Private macOS frameworks underpin critical services and daemons but remain undocumented and distributed only as stripped binaries, complicating security analysis. We present MOTIF, an agentic framework that integrates tool-augmented analysis with a finetuned large language model specialized for...

Security in the Era of Perceptive Networks: A Comprehensive Taxonomic Framework for Integrated Sensing and Communication Security

Integrated Sensing and Communication ISAC represents a significant shift in the 6G landscape, where wireless networks both sense the environment and communicate. While prior comprehensive surveys have established foundational elements of ISAC security, discussed perception-focused security models...

MCP-SandboxScan: WASM-Based Secure Execution and Runtime Analysis for MCP Tools

Tool-augmented LLM agents raise new security risks: tool executions can introduce runtime-only behaviors, including prompt injection and unintended exposure of external inputs e.g., environment secrets or local files. While existing scanners often focus on static artifacts, analyzing runtime...

Comparative Evaluation of VAE, GAN, and SMOTE for Tor Detection in Encrypted Network Traffic

Encrypted network traffic poses significant challenges for intrusion detection due to the lack of payload visibility, limited labeled datasets, and high class imbalance between benign and malicious activities. Traditional data augmentation methods struggle to preserve the complex temporal and...