I Can'T Patch My OT Systems! a Look at CISA'S KEVC Workarounds and Mitigations for OT

We examine the state of publicly available information about known exploitable vulnerabilities applicable to operational technology OT environments. Specifically, we analyze the Known Exploitable Vulnerabilities Catalog KEVC maintained by the US Department of Homeland Security Cybersecurity and...

Cybersecurity Competence for Organisations in Inner Scandinavia

A rapidly growing number of cybersecurity threats and incidents demands that Swedish organisations increase their efforts to improve their cybersecurity capacities. This paper presents results from interviews and a prior survey with key representatives from enterprises and public sector...

Are LLMs Reliable Rankers? Rank Manipulation Via Two-Stage Token Optimization

Large language models LLMs are increasingly used as rerankers in information retrieval, yet their ranking behavior can be steered by small, natural-sounding prompts. To expose this vulnerability, we present Rank Anything First RAF, a two-stage token optimization method that crafts concise textual...

Security-Robustness Trade-Offs in Diffusion Steganography: A Comparative Analysis of Pixel-Space and VAE-Based Architectures

Current generative steganography research mainly pursues computationally expensive mappings to perfect Gaussian priors within single diffusion model architectures. This work introduces an efficient framework based on approximate Gaussian mapping governed by a scale factor calibrated through...

A Multi-Layered Embedded Intrusion Detection Framework for Programmable Logic Controllers

Industrial control system ICS operations use trusted endpoints like human machine interfaces HMIs and workstations to relay commands to programmable logic controllers PLCs. Because most PLCs lack layered defenses, compromise of a trusted endpoint can drive unsafe actuator commands and risk...

Towards Reliable and Practical LLM Security Evaluations Via Bayesian Modelling

Before adopting a new large language model LLM architecture, it is critical to understand vulnerabilities accurately. Existing evaluations can be difficult to trust, often drawing conclusions from LLMs that are not meaningfully comparable, relying on heuristic inputs or employing metrics that fai...

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...

PhishSSL: Self-Supervised Contrastive Learning for Phishing Website Detection

Phishing websites remain a persistent cybersecurity threat by mimicking legitimate sites to steal sensitive user information. Existing machine learning-based detection methods often rely on supervised learning with labeled data, which not only incurs substantial annotation costs but also limits...

SpyChain: Multi-Vector Supply Chain Attacks on Small Satellite Systems

Small satellites are integral to scientific, commercial, and defense missions, but reliance on commercial off-the-shelf COTS hardware broadens their attack surface. Although supply chain threats are well studied in other cyber-physical domains, their feasibility and stealth in space systems remai...

"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...

A Survey on Agentic Security: Applications, Threats and Defenses

The rapid shift from passive LLMs to autonomous LLM-agents marks a new paradigm in cybersecurity. While these agents can act as powerful tools for both offensive and defensive operations, the very agentic context introduces a new class of inherent security risks. In this work we present the first...

TOR Virtual Network Tunneling Tool 0.4.8.19

Tor is a network of virtual tunnels that allows people and groups to improve their privacy and security on the Internet. It also enables software developers to create new communication tools with built-in privacy features. It provides the foundation for a range of applications that allow...

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...

An Empirical Study of Security-Policy Related Issues in Open Source Projects

GitHub recommends that projects adopt a SECURITY.md file that outlines vulnerability reporting procedures. However, the effectiveness and operational challenges of such files are not yet fully understood. This study aims to clarify the challenges that SECURITY.md files face in the vulnerability...

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...

Code Agent Can Be an End-To-End System Hacker: Benchmarking Real-World Threats of Computer-Use Agent

Computer-use agent CUA frameworks, powered by large language models LLMs or multimodal LLMs MLLMs, are rapidly maturing as assistants that can perceive context, reason, and act directly within software environments. Among their most critical applications is operating system OS control. As CUAs in...

Leveraging Large Language Models for Cybersecurity Risk Assessment -- a Case from Forestry Cyber-Physical Systems

In safety-critical software systems, cybersecurity activities become essential, with risk assessment being one of the most critical. In many software teams, cybersecurity experts are either entirely absent or represented by only a small number of specialists. As a result, the workload for these...

Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race

As advances in synthetic voice generation accelerate, an increasing variety of fake voice generators have emerged, producing audio that is often indistinguishable from real human speech. This evolution poses new and serious threats across sectors where audio recordings serve as critical evidence...

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...

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,...

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...

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...

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...

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...

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 ...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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,...

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...

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 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,...

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 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...