GNU Privacy Guard 2.5.16

GnuPG the GNU Privacy Guard or GPG is GNU's tool for secure communication and data storage. It can be used to encrypt data and to create digital signatures. It includes an advanced key management facility and is compliant with the proposed OpenPGP Internet standard as described in RFC2440. As suc...

Breaking the Illusion: Automated Reasoning of GDPR Consent Violations

Recent privacy regulations such as the General Data Protection Regulation GDPR and the California Consumer Privacy Act CCPA have established legal requirements for obtaining user consent regarding the collection, use, and sharing of personal data. These regulations emphasize that consent must be...

Securing Cross-Domain Internet of Drones: An RFF-PUF Allied Authenticated Key Exchange Protocol with Over-The-Air Enrollment

The Internet of Drones IoD is an emerging and crucial paradigm enabling advanced applications that require seamless, secure communication across heterogeneous and untrusted domains. In such environments, access control and the transmission of sensitive data pose significant security challenges fo...

The Imitation Game: Using Large Language Models As Chatbots to Combat Chat-Based Cybercrimes

Chat-based cybercrime has emerged as a pervasive threat, with attackers leveraging real-time messaging platforms to conduct scams that rely on trust-building, deception, and psychological manipulation. Traditional defense mechanisms, which operate on static rules or shallow content filters,...

Assessing the Software Security Comprehension of Large Language Models

Large language models LLMs are increasingly used in software development, but their level of software security expertise remains unclear. This work systematically evaluates the security comprehension of five leading LLMs: GPT-4o-Mini, GPT-5-Mini, Gemini-2.5-Flash, Llama-3.1, and Qwen-2.5, using...

CISA: Safe Handling Considerations for Downed Unmanned Aircraft Systems

Safe Handling Considerations for Downed Unmanned Aircraft Systems provides information on how to prepare for and respond to downed unmanned aircraft systems UAS that may pose a safety or security concern...

PROVEX: Enhancing SOC Analyst Trust with Explainable Provenance-Based IDS

Modern intrusion detection systems IDS leverage graph neural networks GNNs to detect malicious activity in system provenance data, but their decisions often remain a black box to analysts. This paper presents a comprehensive XAI framework designed to bridge the trust gap in Security Operations...

A First Look at Common RPKI Publication Practices

The RPKI is crucial for securing the routing system of the Internet. With the RPKI, owners of Internet resources can make cryptographically backed claims, for example about the legitimate origin of their IP space. Thousands of networks use this information to detect malicious or accidental route...

AutoDFBench 1.0: A Benchmarking Framework for Digital Forensic Tool Testing and Generated Code Evaluation

The National Institute of Standards and Technology NIST Computer Forensic Tool Testing CFTT programme has become the de facto standard for providing digital forensic tool testing and validation. However to date, no comprehensive framework exists to automate benchmarking across the diverse forensi...

Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions

The increasing number of cyber threats and rapidly evolving tactics, as well as the high volume of data in recent years, have caused classical machine learning, rules, and signature-based defence strategies to fail, rendering them unable to keep up. An alternative, Quantum Machine Learning QML, h...

CIS-BA: Continuous Interaction Space Based Backdoor Attack for Object Detection in the Real-World

Object detection models deployed in real-world applications such as autonomous driving face serious threats from backdoor attacks. Despite their practical effectiveness,existing methods are inherently limited in both capability and robustness due to their dependence on single-trigger-single-objec...

Nuclei 3.6.1

Nuclei is a modern, high-performance vulnerability scanner that leverages simple YAML-based templates. It empowers you to design custom vulnerability detection scenarios that mimic real-world conditions, leading to zero false positives...

Weak Enforcement and Low Compliance in PCI~DSS: A Comparative Security Study

Although credit and debit card data continue to be a prime target for attackers, organizational adherence to the Payment Card Industry Data Security Standard PCI DSS remains surprisingly low. Despite prior work showing that PCI DSS can reduce card fraud, only 32.4% of organizations were fully...

A Systematic Mapping Study on Risks and Vulnerabilities in Software Containers

Software containers are widely adopted for developing and deploying software applications. Despite their popularity, major security concerns arise during container development and deployment. Software Engineering SE research literature reveals a lack of reviewed, aggregated, and organized knowled...

Cybersecurity Policy Adoption in South Africa: Does Public Trust Matter?

This study examines how public perception influences the implementation and adoption of cybersecurity frameworks in South Africa. Using the PRISMA methodology, a systematic literature review was conducted across reputable scholarly databases, yielding 34 relevant sources aligned with predefined...

True Random Number Generators on IQM Spark

Random number generation is fundamental for many modern applications including cryptography, simulations and machine learning. Traditional pseudo-random numbers may offer statistical unpredictability, but are ultimately deterministic. On the other hand, True Random Number Generation TRNG offers...

LLM-Based Vulnerable Code Augmentation: Generate or Refactor?

Vulnerability code-bases often suffer from severe imbalance, limiting the effectiveness of Deep Learning-based vulnerability classifiers. Data Augmentation could help solve this by mitigating the scarcity of under-represented CWEs. In this context, we investigate LLM-based augmentation for...

Information-Dense Reasoning for Efficient and Auditable Security Alert Triage

Security Operations Centers face massive, heterogeneous alert streams under minute-level service windows, creating the Alert Triage Latency Paradox: verbose reasoning chains ensure accuracy and compliance but incur prohibitive latency and token costs, while minimal chains sacrifice transparency a...

AgenticCyber: A GenAI-Powered Multi-Agent System for Multimodal Threat Detection and Adaptive Response in Cybersecurity

The increasing complexity of cyber threats in distributed environments demands advanced frameworks for real-time detection and response across multimodal data streams. This paper introduces AgenticCyber, a generative AI powered multi-agent system that orchestrates specialized agents to monitor...

The Treasury Proof Ledger: A Cryptographic Framework for Accountable Bitcoin Treasuries

Public companies and institutional investors that hold Bitcoin face increasing pressure to show solvency, manage risk, and satisfy regulatory expectations without exposing internal wallet structures or trading strategies. This paper introduces the Treasury Proof Ledger TPL, a Bitcoin-anchored...

WildCode: An Empirical Analysis of Code Generated by ChatGPT

LLM models are increasingly used to generate code, but the quality and security of this code are often uncertain. Several recent studies have raised alarm bells, indicating that such AI-generated code may be particularly vulnerable to cyberattacks. However, most of these studies rely on code that...

WhiteLie: A Robust System for Spoofing User Data in Android Platforms

Android employs a permission framework that empowers users to either accept or deny sharing their private data for example, location with an app. However, many apps tend to crash when they are denied permission, leaving users no choice but to allow access to their data in order to use the app. In...

CVE Breadcrumbs: Tracking Vulnerabilities through Versioned Apache Libraries

The Apache Software Foundation ASF ecosystem underpins a vast portion of modern software infrastructure, powering widely used components such as Log4j, Tomcat, and Struts. However, the ubiquity of these libraries has made them prime targets for high-impact security vulnerabilities, as illustrated...

Securing Large Language Models (LLMs) from Prompt Injection Attacks

Large Language Models LLMs are increasingly being deployed in real-world applications, but their flexibility exposes them to prompt injection attacks. These attacks leverage the model's instruction-following ability to make it perform malicious tasks. Recent work has proposed JATMO, a task-specif...

Reverse Engineering and Control-Aware Security Analysis of the ArduPilot UAV Framework

Unmanned Aerial Vehicle UAV technologies are gaining high interest for many domains, which makes UAV security of utmost importance. ArduPilot is among the most widely used open-source autopilot UAV frameworks; yet, many studies demonstrate the vulnerabilities affecting such systems. Vulnerabiliti...

Evaluating LLMs for One-Shot Patching of Real and Artificial Vulnerabilities

Automated vulnerability patching is crucial for software security, and recent advancements in Large Language Models LLMs present promising capabilities for automating this task. However, existing research has primarily assessed LLMs using publicly disclosed vulnerabilities, leaving their...

Constructing and Benchmarking: A Labeled Email Dataset for Text-Based Phishing and Spam Detection Framework

Phishing and spam emails remain a major cybersecurity threat, with attackers increasingly leveraging Large Language Models LLMs to craft highly deceptive content. This study presents a comprehensive email dataset containing phishing, spam, and legitimate messages, explicitly distinguishing betwee...

Exploring Hidden Geographic Disparities in Android Apps

While mobile app evolution has been widely studied, geographical variation in app behavior remains largely unexplored. This paper presents a large-scale study of location-based Android app differentiation, uncovering two important and underexamined phenomena with security and fairness implication...

Quantum-Resistant Authentication Scheme for RFID Systems Using Lattice-Based Cryptography

We propose a novel quantum-resistant mutual authentication scheme for radio-frequency identification RFID systems. Our scheme uses lattice-based cryptography and, in particular, achieves quantum-resistance by leveraging the hardness of the inhomogeneous short integer solution ISIS problem. In...

Processing Entangled Links into Secure Cryptographic Keys

The following paper presents a holistic approach to the processing of entangled links within entanglement based quantum key distribution protocols, whose security relies on the Bell inequality. We investigate the interactions, and the collective impact, of the whole processing chain on the final...

LLM-CSEC: Empirical Evaluation of Security in C/C++ Code Generated by Large Language Models

The security of code generated by large language models LLMs is a significant concern, as studies indicate that such code often contains vulnerabilities and lacks essential defensive programming constructs. This work focuses on examining and evaluating the security of LLM-generated code,...

Wireshark Analyzer 4.6.1

Wireshark is a GTK+-based network protocol analyzer that lets you capture and interactively browse the contents of network frames. The goal of the project is to create a commercial-quality analyzer for Unix and Win32 and to give Wireshark features that are missing from closed-source sniffers. Thi...

Future-Back Threat Modeling: A Foresight-Driven Security Framework

Traditional threat modeling remains reactive-focused on known TTPs and past incident data, while threat prediction and forecasting frameworks are often disconnected from operational or architectural artifacts. This creates a fundamental weakness: the most serious cyber threats often do not arise...

TopoReformer: Mitigating Adversarial Attacks Using Topological Purification in OCR Models

Adversarially perturbed images of text can cause sophisticated OCR systems to produce misleading or incorrect transcriptions from seemingly invisible changes to humans. Some of these perturbations even survive physical capture, posing security risks to high-stakes applications such as document...

Can MLLMs Detect Phishing? A Comprehensive Security Benchmark Suite Focusing on Dynamic Threats and Multimodal Evaluation in Academic Environments

The rapid proliferation of Multimodal Large Language Models MLLMs has introduced unprecedented security challenges, particularly in phishing detection within academic environments. Academic institutions and researchers are high-value targets, facing dynamic, multilingual, and context-dependent...

A Unified Compositional View of Attack Tree Metrics

Attack trees ATs are popular graphical models for reasoning about the security of complex systems, allowing for the quantification of risk through so-called AT metrics. A large variety of different such AT metrics have been proposed, and despite their wide-spread practical use, no systematic...

Towards Classifying Benign and Malicious Packages Using Machine Learning

Recently, the number of malicious open-source packages in package repositories has been increasing dramatically. While major security scanners focus on identifying known Common Vulnerabilities and Exposures CVEs in open-source packages, there are very few studies on detecting malicious packages...

It's a Feature, Not a Bug: Secure and Auditable State Rollback for Confidential Cloud Applications

Replay and rollback attacks threaten cloud application integrity by reintroducing authentic yet stale data through an untrusted storage interface to compromise application decision-making. Prior security frameworks mitigate these attacks by enforcing forward-only state transitions state continuit...

New Data Security Requirements and the Proceduralization of Mass Surveillance Law after the European Data Retention Case

This paper discusses the regulation of mass metadata surveillance in Europe through the lens of the landmark judgment in which the Court of Justice of the European Union struck down the Data Retention Directive. The controversial directive obliged telecom and Internet access providers in Europe t...

Scaling Patterns in Adversarial Alignment: Evidence from Multi-LLM Jailbreak Experiments

Large language models LLMs increasingly operate in multi-agent and safety-critical settings, raising open questions about how their vulnerabilities scale when models interact adversarially. This study examines whether larger models can systematically jailbreak smaller ones - eliciting harmful or...

Human-Centered Threat Modeling in Practice: Lessons, Challenges, and Paths Forward

Human-centered threat modeling HCTM is an emerging area within security and privacy research that focuses on how people define and navigate threats in various social, cultural, and technological contexts. While researchers increasingly approach threat modeling from a human-centered perspective,...

TOR Virtual Network Tunneling Tool 0.4.8.20

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...

Suricata IDPE 8.0.2

Suricata is a network intrusion detection and prevention engine developed by the Open Information Security Foundation and its supporting vendors. The engine is multi-threaded and has native IPv6 support. It's capable of loading existing Snort rules and signatures and supports the Barnyard and...

Frequency Diverse (FD)-RIS-Enhanced Covert Communications: Defense against Wiretapping Via Joint Distance-Angle Beamforming

In response to the security blind zone challenges faced by traditional reconfigurable intelligent surface RIS-aided covert communication CC systems, the joint distance-angle beamforming capability of frequency diverse RIS FD-RIS shows significant potential for addressing these limitations...

SteganoSNN: SNN-Based Audio-In-Image Steganography with Encryption

Secure data hiding remains a fundamental challenge in digital communication, requiring a careful balance between computational efficiency and perceptual transparency. The balance between security and performance is increasingly fragile with the emergence of generative AI systems capable of...

Adversarially Robust and Interpretable Magecart Malware Detection

Magecart skimming attacks have emerged as a significant threat to client-side security and user trust in online payment systems. This paper addresses the challenge of achieving robust and explainable detection of Magecart attacks through a comparative study of various Machine Learning ML models...

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...

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...

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...

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...