Evidence of Cognitive Biases in Capture-The-Flag Cybersecurity Competitions

Understanding how cognitive biases influence adversarial decision-making is essential for developing effective cyber defenses. Capture-the-Flag CTF competitions provide an ecologically valid testbed to study attacker behavior at scale, simulating real-world intrusion scenarios under pressure. We...

Breaking Precision Time: OS Vulnerability Exploits against IEEE 1588

The Precision Time Protocol PTP, standardized as IEEE 1588, provides sub-microsecond synchronization across distributed systems and underpins critical infrastructure in telecommunications, finance, power systems, and industrial automation. While prior work has extensively analyzed PTP's...

Clam AntiVirus Toolkit 1.5.0

Clam AntiVirus is an anti-virus toolkit for Unix. The main purpose of this software is the integration with mail servers attachment scanning. The package provides a flexible and scalable multi-threaded daemon, a command-line scanner, and a tool for automatic updating via Internet. The programs ar...

"Your Doctor Is Spying on You": An Analysis of Data Practices in Mobile Healthcare Applications

Mobile healthcare mHealth applications promise convenient, continuous patient-provider interaction but also introduce severe and often underexamined security and privacy risks. We present an end-to-end audit of 272 Android mHealth apps from Google Play, combining permission forensics, static...

Applying Graph Analysis for Unsupervised Fast Malware Fingerprinting

Malware proliferation is increasing at a tremendous rate, with hundreds of thousands of new samples identified daily. Manual investigation of such a vast amount of malware is an unrealistic, time-consuming, and overwhelming task. To cope with this volume, there is a clear need to develop...

AutoPentester: An LLM Agent-Based Framework for Automated Pentesting

Penetration testing and vulnerability assessment are essential industry practices for safeguarding computer systems. As cyber threats grow in scale and complexity, the demand for pentesting has surged, surpassing the capacity of human professionals to meet it effectively. With advances in AI,...

Adversarial-Resilient RF Fingerprinting: A CNN-GAN Framework for Rogue Transmitter Detection

Radio Frequency Fingerprinting RFF has evolved as an effective solution for authenticating devices by leveraging the unique imperfections in hardware components involved in the signal generation process. In this work, we propose a Convolutional Neural Network CNN based framework for detecting rog...

Enhancing Automotive Security with a Hybrid Approach Towards Universal Intrusion Detection System

Security measures are essential in the automotive industry to detect intrusions in-vehicle networks. However, developing a one-size-fits-all Intrusion Detection System IDS is challenging because each vehicle has unique data profiles. This is due to the complex and dynamic nature of the data...

AutoDAN-Reasoning: Enhancing Strategies Exploration Based Jailbreak Attacks with Test-Time Scaling

Recent advancements in jailbreaking large language models LLMs, such as AutoDAN-Turbo, have demonstrated the power of automated strategy discovery. AutoDAN-Turbo employs a lifelong learning agent to build a rich library of attack strategies from scratch. While highly effective, its test-time...

Encoded Jamming Secure Communication for RIS-Assisted and ISAC Systems

This paper considers a cooperative jamming CJ-aided secure wireless communication system. Conventionally, the jammer transmits Gaussian noise GN to enhance security; however, the GN scheme also degrades the legitimate receiver's performance. Encoded jamming EJ mitigates this interference but does...

Imperceptible Jailbreaking against Large Language Models

Jailbreaking attacks on the vision modality typically rely on imperceptible adversarial perturbations, whereas attacks on the textual modality are generally assumed to require visible modifications e.g., non-semantic suffixes. In this paper, we introduce imperceptible jailbreaks that exploit a...

P2P: A Poison-To-Poison Remedy for Reliable Backdoor Defense in LLMs

During fine-tuning, large language models LLMs are increasingly vulnerable to data-poisoning backdoor attacks, which compromise their reliability and trustworthiness. However, existing defense strategies suffer from limited generalization: they only work on specific attack types or task settings...

What Is Quantum Computer Security?

Quantum computing is rapidly emerging as one of the most transformative technologies of our time. With the potential to tackle problems that remain intractable for even the most powerful classical supercomputers, quantum hardware has advanced at an extraordinary pace. Today, major platforms such ...

FreePBX Simple SQL Injection Checker for Your Needs

This application allows you to safely check for SQL injection vulnerabilities in FreePBX. It uses simple techniques to provide accurate results without harming your system...

PoS-CoPOR: Proof-Of-Stake Consensus Protocol with Native Onion Routing Providing Scalability and DoS-Resistance

Proof-of-Stake PoS consensus protocols often face a trade-off between performance and security. Protocols that pre-elect leaders for subsequent rounds are vulnerable to Denial-of-Service DoS attacks, which can disrupt the network and compromise liveness. In this work, we present PoS-CoPOR, a...

Why Software Signing (Still) Matters: Trust Boundaries in the Software Supply Chain

Software signing provides a formal mechanism for provenance by ensuring artifact integrity and verifying producer identity. It also imposes tooling and operational costs to implement in practice. In an era of centralized registries such as PyPI, npm, Maven Central, and Hugging Face, it is...

Faraday 5.17.0

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...

Forensic Timeliner 2.2

Forensic Timeliner is a high-speed forensic processing engine built for DFIR investigators. It quickly consolidates CSV output from top-tier triage tools into a unified mini timeline with built-in filtering, artifact detection, date filtering, keyword tagging, and deduplication...

NatGVD: Natural Adversarial Example Attack Towards Graph-Based Vulnerability Detection

Graph-based models learn rich code graph structural information and present superior performance on various code analysis tasks. However, the robustness of these models against adversarial example attacks in the context of vulnerability detection remains an open question. This paper proposes...

Learning Cybersecurity Vs. Ethical Hacking: A Comparative Pathway for Aspiring Students

This paper explores the distinctions and connections between cybersecurity and ethical hacking, two vital disciplines in the protection of digital systems. It defines each field, outlines their goals and methodologies, and compares the academic and professional paths available to aspiring student...

Selecting Cybersecurity Requirements: Effects of LLM Use and Professional Software Development Experience

This study investigates how access to Large Language Models LLMs and varying levels of professional software development experience affect the prioritization of cybersecurity requirements for web applications. Twenty-three postgraduate students participated in a research study to prioritize...

Real-VulLLM: An LLM Based Assessment Framework in the Wild

Artificial Intelligence AI and more specifically Large Language Models LLMs have demonstrated exceptional progress in multiple areas including software engineering, however, their capability for vulnerability detection in the wild scenario and its corresponding reasoning remains underexplored...

MulVuln: Enhancing Pre-Trained LMs with Shared and Language-Specific Knowledge for Multilingual Vulnerability Detection

Software vulnerabilities SVs pose a critical threat to safety-critical systems, driving the adoption of AI-based approaches such as machine learning and deep learning for software vulnerability detection. Despite promising results, most existing methods are limited to a single programming languag...

Cyber Warfare during Operation Sindoor: Malware Campaign Analysis and Detection Framework

Rapid digitization of critical infrastructure has made cyberwarfare one of the important dimensions of modern conflicts. Attacking the critical infrastructure is an attractive pre-emptive proposition for adversaries as it can be done remotely without crossing borders. Such attacks disturb the...

Agentic Misalignment: How LLMs Could Be Insider Threats

We stress-tested 16 leading models from multiple developers in hypothetical corporate environments to identify potentially risky agentic behaviors before they cause real harm. In the scenarios, we allowed models to autonomously send emails and access sensitive information. They were assigned only...

OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT

In critical IoT environments, such as smart homes and industrial systems, effective Intrusion Detection Systems IDS are essential for ensuring security. However, developing robust IDS solutions remains a significant challenge. Traditional machine learning-based IDS models typically require large...

Security Analysis of Ponzi Schemes in Ethereum Smart Contracts

The rapid advancement of blockchain technology has precipitated the widespread adoption of Ethereum and smart contracts across a variety of sectors. However, this has also given rise to numerous fraudulent activities, with many speculators embedding Ponzi schemes within smart contracts, resulting...

Pilot Contamination Attacks Detection with Machine Learning for Multi-User Massive MIMO

Massive multiple-input multiple-output MMIMO is essential to modern wireless communication systems, like 5G and 6G, but it is vulnerable to active eavesdropping attacks. One type of such attack is the pilot contamination attack PCA, where a malicious user copies pilot signals from an authentic us...

LegalSim: Multi-Agent Simulation of Legal Systems for Discovering Procedural Exploits

We present LegalSim, a modular multi-agent simulation of adversarial legal proceedings that explores how AI systems can exploit procedural weaknesses in codified rules. Plaintiff and defendant agents choose from a constrained action space for example, discovery requests, motions, meet-and-confer,...

NEXUS: Network Exploration for EXploiting Unsafe Sequences in Multi-Turn LLM Jailbreaks

Large Language Models LLMs have revolutionized natural language processing but remain vulnerable to jailbreak attacks, especially multi-turn jailbreaks that distribute malicious intent across benign exchanges and bypass alignment mechanisms. Existing approaches often explore the adversarial space...

A Novel Unified Lightweight Temporal-Spatial Transformer Approach for Intrusion Detection in Drone Networks

The growing integration of drones across commercial, industrial, and civilian domains has introduced significant cybersecurity challenges, particularly due to the susceptibility of drone networks to a wide range of cyberattacks. Existing intrusion detection mechanisms often lack the adaptability,...

CryptOracle: A Modular Framework to Characterize Fully Homomorphic Encryption

Privacy-preserving machine learning has become an important long-term pursuit in this era of artificial intelligence AI. Fully Homomorphic Encryption FHE is a uniquely promising solution, offering provable privacy and security guarantees. Unfortunately, computational cost is impeding its mass...

CST-AFNet: A Dual Attention-Based Deep Learning Framework for Intrusion Detection in IoT Networks

The rapid expansion of the Internet of Things IoT has revolutionized modern industries by enabling smart automation and real time connectivity. However, this evolution has also introduced complex cybersecurity challenges due to the heterogeneous, resource constrained, and distributed nature of...

PentestMCP: A Toolkit for Agentic Penetration Testing

Agentic AI is transforming security by automating many tasks being performed manually. While initial agentic approaches employed a monolithic architecture, the Model-Context-Protocol has now enabled a remote-procedure call RPC paradigm to agentic applications, allowing for the flexible constructi...

A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT

The rapid growth of the Internet of Things IoT has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privacy, and adaptability in resource-constrained IoT environments. To address these...

A Quantum-Secure Voting Framework Using QKD, Dual-Key Symmetric Encryption, and Verifiable Receipts

Electronic voting systems face growing risks from cyberattacks and data breaches, which are expected to intensify with the advent of quantum computing. To address these challenges, we introduce a quantum-secure voting framework that integrates Quantum Key Distribution QKD, Dual-Key Symmetric...

A Statistical Method for Attack-Agnostic Adversarial Attack Detection with Compressive Sensing Comparison

Adversarial attacks present a significant threat to modern machine learning systems. Yet, existing detection methods often lack the ability to detect unseen attacks or detect different attack types with a high level of accuracy. In this work, we propose a statistical approach that establishes a...

Explainable but Vulnerable: Adversarial Attacks on XAI Explanation in Cybersecurity Applications

Explainable Artificial Intelligence XAI has aided machine learning ML researchers with the power of scrutinizing the decisions of the black-box models. XAI methods enable looking deep inside the models' behavior, eventually generating explanations along with a perceived trust and transparency...

External Data Extraction Attacks against Retrieval-Augmented Large Language Models

In recent years, RAG has emerged as a key paradigm for enhancing large language models LLMs. By integrating externally retrieved information, RAG alleviates issues like outdated knowledge and, crucially, insufficient domain expertise. While effective, RAG introduces new risks of external data...

Amcache Evilhunter Tool

AmCache-EvilHunter is a command-line tool to parse and analyze Windows Amcache.hve registry hives, identify evidence of execution, suspicious executables, and integrate VirusTotal/OpenTIP lookups for enhanced threat intelligence...

Unmasking Puppeteers: Leveraging Biometric Leakage to Disarm Impersonation in AI-Based Videoconferencing

AI-based talking-head videoconferencing systems reduce bandwidth by sending a compact pose-expression latent and re-synthesizing RGB at the receiver, but this latent can be puppeteered, letting an attacker hijack a victim's likeness in real time. Because every frame is synthetic, deepfake and...

OpenSSL Toolkit 3.6.0

OpenSSL is a robust, fully featured Open Source toolkit implementing the Secure Sockets Layer and Transport Layer Security protocols with full-strength cryptography world-wide. This is the 3.6 release...

Authentication Security of PRF GNSS Ranging

This work derives the authentication security of pseudorandom function PRF GNSS ranging under multiple GNSS spoofing models, including the Security Code Estimation and Replay SCER spoofer. When GNSS ranging codes derive from a PRF utilizing a secret known only to the broadcaster, the spoofer cann...

WireTap: Breaking Server SGX via DRAM Bus Interposition

Whitepaper that delves into Intel’s Software Guard eXtension SGX. A common misconception is that physical attacks on SGX require expensive laboratory equipment, thus putting them out of reach of hobbyist-level attackers. In this work, the authors challenge this belief, showing how simple memory b...

FalseCrashReducer: Mitigating False Positive Crashes in OSS-Fuzz-Gen Using Agentic AI

Fuzz testing has become a cornerstone technique for identifying software bugs and security vulnerabilities, with broad adoption in both industry and open-source communities. Directly fuzzing a function requires fuzz drivers, which translate random fuzzer inputs into valid arguments for the target...

MALF: A Multi-Agent LLM Framework for Intelligent Fuzzing of Industrial Control Protocols

Industrial control systems ICS are vital to modern infrastructure but increasingly vulnerable to cybersecurity threats, particularly through weaknesses in their communication protocols. This paper presents MALF Multi-Agent LLM Fuzzing Framework, an advanced fuzzing solution that integrates large...

A Cybersecurity AI Agent Selection and Decision Support Framework

This paper presents a novel, structured decision support framework that systematically aligns diverse artificial intelligence AI agent architectures, reactive, cognitive, hybrid, and learning, with the comprehensive National Institute of Standards and Technology NIST Cybersecurity Framework CSF...

Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks

As deep learning models become widely deployed as components within larger production systems, their individual shortcomings can create system-level vulnerabilities with real-world impact. This paper studies how adversarial attacks targeting an ML component can degrade or bypass an entire...

TLoRa: Implementing TLS over LoRa for Secure HTTP Communication in IoT

We present TLoRa, an end-to-end architecture for HTTPS communication over LoRa by integrating TCP tunneling and a complete TLS 1.3 handshake. It enables a seamless and secure communication channel between WiFi-enabled end devices and the Internet over LoRa using an End Hub EH and a Net Relay NR...

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense

This paper presents CADL Cognitive-Adaptive Deception Layer, an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning Random Forest, XGBoost, Neural Networks combined with behavioral...