InvisibleJS JavaScript Hiding Tool

Welcome to InvisibleJS, an experimental tool for hiding your JavaScript source code in plain sight using zero-width characters. This repository features two distinct versions of the obfuscator, tailored for different execution environments...

When Bots Take the Bait: Exposing and Mitigating the Emerging Social Engineering Attack in Web Automation Agent

Web agents, powered by large language models LLMs, are increasingly deployed to automate complex web interactions. The rise of open-source frameworks e.g., Browser Use, Skyvern-AI has accelerated adoption, but also broadened the attack surface. While prior research has focused on model threats su...

Memory-Based Malware Detection under Limited Data Conditions: A Comparative Evaluation of TabPFN and Ensemble Models

Artificial intelligence and machine learning have significantly advanced malware research by enabling automated threat detection and behavior analysis. However, the availability of exploitable data is limited, due to the absence of large datasets with real-world data. Despite the progress of AI i...

CHASE: LLM Agents for Dissecting Malicious PyPI Packages

Modern software package registries like PyPI have become critical infrastructure for software development, but are increasingly exploited by threat actors distributing malicious packages with sophisticated multi-stage attack chains. While Large Language Models LLMs offer promising capabilities fo...

LINEture: Novel Signature Cryptosystem

We propose a novel digital signature cryptosystem that exploits the concept of the brute-force problem. To ensure the security of the cryptosystem, we employed several mechanisms: sharing a common secret for factorable permutations, associating permutations with the message being signed, and...

Operational Runtime Behavior Mining for Open-Source Supply Chain Security

Open-source software OSS is a critical component of modern software systems, yet supply chain security remains challenging in practice due to unavailable or obfuscated source code. Consequently, security teams often rely on runtime observations collected from sandboxed executions to investigate...

Belief in False Information: A Human-Centered Security Risk in Sociotechnical Systems

This paper provides a comprehensive literature review on the belief in false information, including misinformation, disinformation, and fake information. It addresses the increasing societal concern regarding false information, which is fueled by technological progress, especially advancements in...

Zer0n: An AI-Assisted Vulnerability Discovery and Blockchain-Backed Integrity Framework

As vulnerability research increasingly adopts generative AI, a critical reliance on opaque model outputs has emerged, creating a "trust gap" in security automation. We address this by introducing Zer0n, a framework that anchors the reasoning capabilities of Large Language Models LLMs to the...

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...

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...

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 ...

Behavioral Analytics for Continuous Insider Threat Detection in Zero-Trust Architectures

Insider threats are a particularly tricky cybersecurity issue, especially in zero-trust architectures ZTA where implicit trust is removed. Although the rule of thumb is never trust, always verify, attackers can still use legitimate credentials and impersonate the standard user activity. In...

SecureDyn-FL: A Robust Privacy-Preserving Federated Learning Framework for Intrusion Detection in IoT Networks

The rapid proliferation of Internet of Things IoT devices across domains such as smart homes, industrial control systems, and healthcare networks has significantly expanded the attack surface for cyber threats, including botnet-driven distributed denial-of-service DDoS, malware injection, and dat...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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 ...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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,...

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...

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...

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...

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...

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...

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...

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...