Semantic Superiority Vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection

Business Email Compromise BEC is a sophisticated social engineering threat that manipulates organizational hierarchies and exploits psychological vulnerabilities, leading to significant financial damage. According to the 2024 FBI Internet Crime Report, BEC accounts for over $2.9 billion in annual...

Next-Generation MIMO Transceivers for Integrated Sensing and Communications: Unique Security Vulnerabilities and Solutions

Integrated sensing and communications ISAC, which is recognized as a key enabler for sixth generation 6G, has brought new opportunities for intelligent, sustainable, and connected wireless networks. Multiple-input multiple-output MIMO transceiver technology lies at the core of this paradigm,...

Securing the Model Context Protocol (MCP): Risks, Controls, and Governance

The Model Context Protocol MCP replaces static, developer-controlled API integrations with more dynamic, user-driven agent systems, which also introduces new security risks. As MCP adoption grows across community servers and major platforms, organizations encounter threats that existing AI...

Effective Command-Line Interface Fuzzing with Path-Aware Large Language Model Orchestration

Command-line interface CLI fuzzing tests programs by mutating both command-line options and input file contents, thus enabling discovery of vulnerabilities that only manifest under specific option-input combinations. Prior works of CLI fuzzing face the challenges of generating semantics-rich opti...

A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation

Graph Neural Networks GNNs have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challenges remain regarding scalability, interpretability, and the availability of reliable datasets. This paper brings togeth...

BrowseSafe: Understanding and Preventing Prompt Injection within AI Browser Agents

The integration of artificial intelligence AI agents into web browsers introduces security challenges that go beyond traditional web application threat models. Prior work has identified prompt injection as a new attack vector for web agents, yet the resulting impact within real-world environments...

Netscaler / Citrix ADC / Gateway Memory Overflow

This is a multi-host, multi-port scanner and auditor for CVE-2025-6543-affected NetScaler devices. Supports SNMP and SSH enumeration with optional CSV reporting and exploit stubs...

Improving the Identification of Real-World Malware's DNS Covert Channels Using Locality Sensitive Hashing

Nowadays, malware increasingly uses DNS-based covert channels in order to evade detection and maintain stealthy communication with its command-and-control servers. While prior work has focused on detecting such activity, identifying specific malware families and their behaviors from captured...

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...

Hey There! You Are Using WhatsApp: Enumerating Three Billion Accounts for Security and Privacy

WhatsApp, with 3.5 billion active accounts as of early 2025, is the world's largest instant messaging platform. Given its massive user base, WhatsApp plays a critical role in global communication. To initiate conversations, users must first discover whether their contacts are registered on the...

From One Attack Domain to Another: Contrastive Transfer Learning with Siamese Networks for APT Detection

Advanced Persistent Threats APT pose a major cybersecurity challenge due to their stealth, persistence, and adaptability. Traditional machine learning detectors struggle with class imbalance, high dimensional features, and scarce real world traces. They often lack transferability-performing well ...

A Single-Root, Multi-Curve, Context-Isolated, PQC-Pluggable Cryptographic Identity Primitive with Stateless Secret Rotation

Cryptographic identity anchors modern decentralized systems, yet current standards like BIP-39 and BIP-32 are structurally insufficient for the demands of multi-curve, multi-domain, and post-quantum PQC environments. These legacy schemes rely on a monolithic identity root with no inherent context...

Adaptive Detection of Polymorphic Malware: Leveraging Mutation Engines and YARA Rules for Enhanced Security

Polymorphic malware continually alters its structure to evade signature-based defences, challenging both commercial antivirus AV and enterprise detection systems. This study introduces a reproducible framework for analysing eight polymorphic behaviours-junk code insertion, control-flow obfuscatio...

Engel P-Adic Isogeny-Based Cryptography over Laurent Series: Foundations, Security, and an ESP32 Implementation

Securing the Internet of Things IoT against quantum attacks requires public-key cryptography that i remains compact and ii runs efficiently on microcontrollers, capabilities many post-quantum PQ schemes lack due to large keys and heavy arithmetic. We address both constraints simultaneously with, ...

A Reality Check on SBOM-Based Vulnerability Management: An Empirical Study and a Path Forward

The Software Bill of Materials SBOM is a critical tool for securing the software supply chain SSC, but its practical utility is undermined by inaccuracies in both its generation and its application in vulnerability scanning. This paper presents a large-scale empirical study on 2,414 open-source...

Quantum Key Distribution: Bridging Theoretical Security Proofs, Practical Attacks, and Error Correction for Quantum-Augmented Networks

Quantum Key Distribution QKD is revolutionizing cryptography by promising information-theoretic security through the immutable laws of quantum mechanics. Yet, the challenge of transforming these idealized security models into practical, resilient systems remains a pressing issue, especially as...

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...

FedPoisonTTP: A Threat Model and Poisoning Attack for Federated Test-Time Personalization

Test-time personalization in federated learning enables models at clients to adjust online to local domain shifts, enhancing robustness and personalization in deployment. Yet, existing federated learning work largely overlooks the security risks that arise when local adaptation occurs at test tim...

Accuracy and Efficiency Trade-Offs in LLM-Based Malware Detection and Explanation: A Comparative Study of Parameter Tuning Vs. Full Fine-Tuning

This study examines whether Low-Rank Adaptation LoRA fine-tuned Large Language Models LLMs can approximate the performance of fully fine-tuned models in generating human-interpretable decisions and explanations for malware classification. Achieving trustworthy malware detection, particularly when...

BASICS: Binary Analysis and Stack Integrity Checker System for Buffer Overflow Mitigation

Cyber-Physical Systems have played an essential role in our daily lives, providing critical services such as power and water, whose operability, availability, and reliability must be ensured. The C programming language, prevalent in CPS development, is crucial for system control where reliability...

Synthetic Data: AI'S New Weapon against Android Malware

The ever-increasing number of Android devices and the accelerated evolution of malware, reaching over 35 million samples by 2024, highlight the critical importance of effective detection methods. Attackers are now using Artificial Intelligence to create sophisticated malware variations that can...

Cross-LLM Generalization of Behavioral Backdoor Detection in AI Agent Supply Chains

As AI agents become integral to enterprise workflows, their reliance on shared tool libraries and pre-trained components creates significant supply chain vulnerabilities. While previous work has demonstrated behavioral backdoor detection within individual LLM architectures, the critical question ...

Prompt Fencing: A Cryptographic Approach to Establishing Security Boundaries in Large Language Model Prompts

Large Language Models LLMs remain vulnerable to prompt injection attacks, representing the most significant security threat in production deployments. We present Prompt Fencing, a novel architectural approach that applies cryptographic authentication and data architecture principles to establish...

Notepad++ Plugin Persistence

This Metasploit module create persistence by adding a malicious plugin to Notepad++, as it blindly loads and executes DLL from its plugin directory on startup, meaning that the payload will be executed every time Notepad++ is launched...

Frequency Bias Matters: Diving into Robust and Generalized Deep Image Forgery Detection

As deep image forgery powered by AI generative models, such as GANs, continues to challenge today's digital world, detecting AI-generated forgeries has become a vital security topic. Generalizability and robustness are two critical concerns of a forgery detector, determining its reliability when...

DUALGUAGE: Automated Joint Security-Functionality Benchmarking for Secure Code Generation

Large language models LLMs and autonomous coding agents are increasingly used to generate software across a wide range of domains. Yet a core requirement remains unmet: ensuring that generated code is secure without compromising its functional correctness. Existing benchmarks and evaluations for...

Defending Large Language Models against Jailbreak Exploits with Responsible AI Considerations

Large Language Models LLMs remain susceptible to jailbreak exploits that bypass safety filters and induce harmful or unethical behavior. This work presents a systematic taxonomy of existing jailbreak defenses across prompt-level, model-level, and training-time interventions, followed by three...

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,...

Evolution of Cybersecurity Subdisciplines: A Science of Science Study

The science of science is an emerging field that studies the practice of science itself. We present the first study of the cybersecurity discipline from a science of science perspective. We examine the evolution of two comparable interdisciplinary communities in cybersecurity: the Symposium on...

IRSDA: An Agent-Orchestrated Framework for Enterprise Intrusion Response

Modern enterprise systems face escalating cyber threats that are increasingly dynamic, distributed, and multi-stage in nature. Traditional intrusion detection and response systems often rely on static rules and manual workflows, which limit their ability to respond with the speed and precision...

OpenSCAP Libraries 1.4.3

The openscap project is a set of open source libraries that support the SCAP Security Content Automation Protocol set of standards from NIST. It supports CPE, CCE, CVE, CVSS, OVAL, and XCCDF...

Zero-Trust Strategies for O-RAN Cellular Networks: Principles, Challenges and Research Directions

Cellular networks have become foundational to modern communication, supporting a broad range of applications, from civilian use to enterprise systems and military tactical networks. The advent of fifth-generation and beyond cellular networks B5G introduces emerging compute capabilities into the...

TASO: Jailbreak LLMs Via Alternative Template and Suffix Optimization

Many recent studies showed that LLMs are vulnerable to jailbreak attacks, where an attacker can perturb the input of an LLM to induce it to generate an output for a harmful question. In general, existing jailbreak techniques either optimize a semantic template intended to induce the LLM to produc...

Evaluation of Real-Time Mitigation Techniques for Cyber Security in IEC 61850 / IEC 62351 Substations

The digitalization of substations enlarges the cyber-attack surface, necessitating effective detection and mitigation of cyber attacks in digital substations. While machine learning-based intrusion detection has been widely explored, such methods have not demonstrated detection and mitigation...

From Reviewers' Lens: Understanding Bug Bounty Report Invalid Reasons with LLMs

Bug bounty platforms e.g., HackerOne, BugCrowd leverage crowd-sourced vulnerability discovery to improve continuous coverage, reduce the cost of discovery, and serve as an integral complement to internal red teams. With the rise of AI-generated bug reports, little work exists to help bug hunters...

LLMs As Firmware Experts: A Runtime-Grown Tree-Of-Agents Framework

Large Language Models LLMs and their agent systems have recently demonstrated strong potential in automating code reasoning and vulnerability detection. However, when applied to large-scale firmware, their performance degrades due to the binary nature of firmware, complex dependency structures, a...

Federated Anomaly Detection and Mitigation for EV Charging Forecasting under Cyberattacks

Electric Vehicle EV charging infrastructure faces escalating cybersecurity threats that can severely compromise operational efficiency and grid stability. Existing forecasting techniques are limited by the lack of combined robust anomaly mitigation solutions and data privacy preservation...

A Novel and Practical Universal Adversarial Perturbations against Deep Reinforcement Learning Based Intrusion Detection Systems

Intrusion Detection Systems IDS play a vital role in defending modern cyber physical systems against increasingly sophisticated cyber threats. Deep Reinforcement Learning-based IDS, have shown promise due to their adaptive and generalization capabilities. However, recent studies reveal their...

Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway

As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in environments with limited computational and energy resources. This paper presents an edge-centric Intrusion Detection System IDS framework that integrate...

EBPF-PATROL: Protective Agent for Threat Recognition and Overreach Limitation Using EBPF in Containerized and Virtualized Environments

With the increasing use and adoption of cloud and cloud-native computing, the underlying technologies i.e., containerization and virtualization have become foundational. However, strict isolation and maintaining runtime security in these environments has become increasingly challenging. Existing...

Building Browser Agents: Architecture, Security, and Practical Solutions

Browser agents enable autonomous web interaction but face critical reliability and security challenges in production. This paper presents findings from building and operating a production browser agent. The analysis examines where current approaches fail and what prevents safe autonomous operatio...

Deepfake Geography: Detecting AI-Generated Satellite Images

The rapid advancement of generative models such as StyleGAN2 and Stable Diffusion poses a growing threat to the authenticity of satellite imagery, which is increasingly vital for reliable analysis and decision-making across scientific and security domains. While deepfake detection has been...

The Star Product of Uniformly Random Codes

We consider the problem of determining the expected dimension of the star product of two uniformly random linear codes that are not necessarily of the same dimension. We achieve this by establishing a correspondence between the star product and the evaluation of bilinear forms, which we use to...

Steering in the Shadows: Causal Amplification for Activation Space Attacks in Large Language Models

Modern large language models LLMs are typically secured by auditing data, prompts, and refusal policies, while treating the forward pass as an implementation detail. We show that intermediate activations in decoder-only LLMs form a vulnerable attack surface for behavioral control. Building on...

ThreadFuzzer: Fuzzing Framework for Thread Protocol

With the rapid growth of IoT, secure and efficient mesh networking has become essential. Thread has emerged as a key protocol, widely used in smart-home and commercial systems, and serving as a core transport layer in the Matter standard. This paper presents ThreadFuzzer, the first dedicated...

The Dark Side of Flexibility: How Aggregated Cyberattacks Threaten the Power Grid

Flexible energy resources are increasingly becoming common in smart grids. These resources are typically managed and controlled by aggregators that coordinate many resources to provide flexibility services. However, these aggregators and flexible energy resources are vulnerable, which could allow...

ReVul-CoT: Towards Effective Software Vulnerability Assessment with Retrieval-Augmented Generation and Chain-Of-Thought Prompting

Context: Software Vulnerability Assessment SVA plays a vital role in evaluating and ranking vulnerabilities in software systems to ensure their security and reliability. Objective: Although Large Language Models LLMs have recently shown remarkable potential in SVA, they still face two major...

AutoGraphAD: A Novel Approach Using Variational Graph Autoencoders for Anomalous Network Flow Detection

Network Intrusion Detection Systems NIDS are essential tools for detecting network attacks and intrusions. While extensive research has explored the use of supervised Machine Learning for attack detection and characterisation, these methods require accurately labelled datasets, which are very...

StealthCup: Realistic, Multi-Stage, Evasion-Focused CTF for Benchmarking IDS

Intrusion Detection Systems IDS are critical to defending enterprise and industrial control environments, yet evaluating their effectiveness under realistic conditions remains an open challenge. Existing benchmarks rely on synthetic datasets e.g., NSL-KDD, CICIDS2017 or scripted replay frameworks...

Lessons Lost: Incident Response in the Age of Cyber Insurance and Breach Attorneys

Incident Response IR allows victim firms to detect, contain, and recover from security incidents. It should also help the wider community avoid similar attacks in the future. In pursuit of these goals, technical practitioners are increasingly influenced by stakeholders like cyber insurers and...