A Comparative Study of Hybrid Post-Quantum Cryptographic X.509 Certificate Schemes

As quantum computing hardware continues to advance, the integration of such technology with quantum algorithms is anticipated to enable the decryption of ciphertexts produced by RSA and Elliptic Curve Cryptography ECC within polynomial time. In response to this emerging threat, the U.S. National...

LLM-Based Multi-Class Attack Analysis and Mitigation Framework in IoT/IIoT Networks

The Internet of Things has expanded rapidly, transforming communication and operations across industries but also increasing the attack surface and security breaches. Artificial Intelligence plays a key role in securing IoT, enabling attack detection, attack behavior analysis, and mitigation...

A DRL-Empowered Multi-Level Jamming Approach for Secure Semantic Communication

Semantic communication SemCom aims to transmit only task-relevant information, thereby improving communication efficiency but also exposing semantic information to potential eavesdropping. In this paper, we propose a deep reinforcement learning DRL-empowered multi-level jamming approach to enhanc...

CyberNER: A Harmonized STIX Corpus for Cybersecurity Named Entity Recognition

Extracting structured intelligence via Named Entity Recognition NER is critical for cybersecurity, but the proliferation of datasets with incompatible annotation schemas hinders the development of comprehensive models. While combining these resources is desirable, we empirically demonstrate that...

Adapting Large Language Models to Emerging Cybersecurity Using Retrieval Augmented Generation

Security applications are increasingly relying on large language models LLMs for cyber threat detection; however, their opaque reasoning often limits trust, particularly in decisions that require domain-specific cybersecurity knowledge. Because security threats evolve rapidly, LLMs must not only...

A Comprehensive Evaluation and Practice of System Penetration Testing

With the rapid advancement of information technology, the complexity of applications continues to increase, and the cybersecurity challenges we face are also escalating. This paper aims to investigate the methods and practices of system security penetration testing, exploring how to enhance syste...

Unvalidated Trust: Cross-Stage Vulnerabilities in Large Language Model Architectures

As Large Language Models LLMs are increasingly integrated into automated, multi-stage pipelines, risk patterns that arise from unvalidated trust between processing stages become a practical concern. This paper presents a mechanism-centered taxonomy of 41 recurring risk patterns in commercial LLMs...

Windows Persistent Startup Folder

This Metasploit module establishes persistence by creating a payload in the user or system startup folder. Works on Vista and newer systems...

Model Inversion Attacks Meet Cryptographic Fuzzy Extractors

Model inversion attacks pose an open challenge to privacy-sensitive applications that use machine learning ML models. For example, face authentication systems use modern ML models to compute embedding vectors from face images of the enrolled users and store them. If leaked, inversion attacks can...

Security Vulnerabilities in AI-Generated Code: A Large-Scale Analysis of Public GitHub Repositories

This paper presents a comprehensive empirical analysis of security vulnerabilities in AI-generated code across public GitHub repositories. We collected and analyzed 7,703 files explicitly attributed to four major AI tools: ChatGPT 91.52%, GitHub Copilot 7.50%, Amazon CodeWhisperer 0.52%, and...

APThreatHunter: An Automated Planning-Based Threat Hunting Framework

Cyber attacks threaten economic interests, critical infrastructure, and public health and safety. To counter this, entities adopt cyber threat hunting, a proactive approach that involves formulating hypotheses and searching for attack patterns within organisational networks. Automating cyber thre...

AAGATE: A NIST AI RMF-Aligned Governance Platform for Agentic AI

This paper introduces the Agentic AI Governance Assurance & Trust Engine AAGATE, a Kubernetes-native control plane designed to address the unique security and governance challenges posed by autonomous, language-model-driven agents in production. Recognizing the limitations of traditional...

AgentCyTE: Leveraging Agentic AI to Generate Cybersecurity Training and Experimentation Scenarios

Designing realistic and adaptive networked threat scenarios remains a core challenge in cybersecurity research and training, still requiring substantial manual effort. While large language models LLMs show promise for automated synthesis, unconstrained generation often yields configurations that...

SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents Via Distilled Structured Reasoning

The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring their safe deployment. We present SIRAJ: a generic red-teaming framework for arbitrary black-box LLM agents. We emplo...

Identity Management for Agentic AI: The New Frontier of Authorization, Authentication, and Security for an AI Agent World

The rapid rise of AI agents presents urgent challenges in authentication, authorization, and identity management. Current agent-centric protocols like MCP highlight the demand for clarified best practices in authentication and authorization. Looking ahead, ambitions for highly autonomous agents...

An In-Depth Analysis of Cyber Attacks in Secured Platforms

There is an increase in global malware threats. To address this, an encryption-type ransomware has been introduced on the Android operating system. The challenges associated with malicious threats in phone use have become a pressing issue in mobile communication, disrupting user experiences and...

Is Protective DNS Blocking the Wild West?

We perform a passive measurement study investigating how a Protective DNS service might perform in a Research & Education Network serving hundreds of member institutions. Utilizing freely-available DNS blocklists consisting of domain names deemed to be threats, we test hundreds of millions of...

From ECU to VSOC: UDS Security Monitoring Strategies

Increasing complexity and connectivity of modern vehicles have heightened their vulnerability to cyberattacks. This paper addresses security challenges associated with the Unified Diagnostic Services UDS protocol, a critical communication framework for vehicle diagnostics in the automotive...

tc Tor Chat Client 1.3

tc is a low-tech free solution to make yourself anonymously reachable for chat by anyone who only know your onion address and your public key. Messages are PGP encrypted end-to-end and forwarded by Tor's relays...

Packet Fence 15.0.0

PacketFence is a network access control NAC system. It is actively maintained and has been deployed in numerous large-scale institutions. It can be used to effectively secure networks, from small to very large heterogeneous networks. PacketFence provides NAC-oriented features such as registration...

Covert Surveillance in Smart Devices: A SCOUR Framework Analysis of Youth Privacy Implications

This paper investigates how smart devices covertly capture private conversations and discusses in more in-depth the implications of this for youth privacy. Using a structured review guided by the PRISMA methodology, the analysis focuses on privacy concerns, data capture methods, data storage and...

Secure Retrieval-Augmented Generation against Poisoning Attacks

Large language models LLMs have transformed natural language processing NLP, enabling applications from content generation to decision support. Retrieval-Augmented Generation RAG improves LLMs by incorporating external knowledge but also introduces security risks, particularly from data poisoning...

Quantum-Resistant Networks Using Post-Quantum Cryptography

Quantum networks rely on both quantum and classical channels for coordinated operation. Current architectures employ entanglement distribution and key exchange over quantum channels but often assume that classical communication is sufficiently secure. In practice, classical channels protected by...

Hammering the Diagnosis: Rowhammer-Induced Stealthy Trojan Attacks on ViT-Based Medical Imaging

Vision Transformers ViTs have emerged as powerful architectures in medical image analysis, excelling in tasks such as disease detection, segmentation, and classification. However, their reliance on large, attention-driven models makes them vulnerable to hardware-level attacks. In this paper, we...

Attention Augmented GNN RNN-Attention Models for Advanced Cybersecurity Intrusion Detection

In this paper, we propose a novel hybrid deep learning architecture that synergistically combines Graph Neural Networks GNNs, Recurrent Neural Networks RNNs, and multi-head attention mechanisms to significantly enhance cybersecurity intrusion detection capabilities. By leveraging the comprehensiv...

Victim As a Service: Designing a System for Engaging with Interactive Scammers

Pig butchering, and similar interactive online scams, lower their victims' defenses by building trust over extended periods of conversation - sometimes weeks or months. They have become increasingly public losses at least $75B by one recent study. However, because of their long-term conversationa...

QueryIPI: Query-Agnostic Indirect Prompt Injection on Coding Agents

Modern coding agents integrated into IDEs combine powerful tools and system-level actions, exposing a high-stakes attack surface. Existing Indirect Prompt Injection IPI studies focus mainly on query-specific behaviors, leading to unstable attacks with lower success rates. We identify a more sever...

Wapiti Web Application Vulnerability Scanner 3.2.8

Wapiti is a web application vulnerability scanner. It will scan the web pages of a deployed web application and will fuzz the URL parameters and forms to find common web vulnerabilities. This is the binary release...

Wapiti Web Application Vulnerability Scanner 3.2.8 Source Code

Wapiti is a web application vulnerability scanner. It will scan the web pages of a deployed web application and will fuzz the URL parameters and forms to find common web vulnerabilities. This is the source code release...

MCPGuard : Automatically Detecting Vulnerabilities in MCP Servers

The Model Context Protocol MCP has emerged as a standardized interface enabling seamless integration between Large Language Models LLMs and external data sources and tools. While MCP significantly reduces development complexity and enhances agent capabilities, its openness and extensibility...

Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges

Agentic AI systems powered by large language models LLMs and endowed with planning, tool use, memory, and autonomy, are emerging as powerful, flexible platforms for automation. Their ability to autonomously execute tasks across web, software, and physical environments creates new and amplified...

A Neuro-Symbolic Multi-Agent Approach to Legal-Cybersecurity Knowledge Integration

The growing intersection of cybersecurity and law creates a complex information space where traditional legal research tools struggle to deal with nuanced connections between cases, statutes, and technical vulnerabilities. This knowledge divide hinders collaboration between legal experts and...

Secure Control of Connected and Autonomous Electrified Vehicles under Adversarial Cyber-Attacks

Connected and Autonomous Electrified Vehicles CAEV is the solution to the future smart mobility having benefits of efficient traffic flow and cleaner environmental impact. Although CAEV has advantages they are still susceptible to adversarial cyber attacks due to their autonomous electric operati...

Evaluation of Vision-LLMs in Surveillance Video

The widespread use of cameras in our society has created an overwhelming amount of video data, far exceeding the capacity for human monitoring. This presents a critical challenge for public safety and security, as the timely detection of anomalous or criminal events is crucial for effective...

Network Intrusion Detection: Evolution from Conventional Approaches to LLM Collaboration and Emerging Risks

This survey systematizes the evolution of network intrusion detection systems NIDS, from conventional methods such as signature-based and neural network NN-based approaches to recent integrations with large language models LLMs. It clearly and concisely summarizes the current status, strengths, a...

Advancing Honeywords for Real-World Authentication Security

Introduced by Juels and Rivest in 2013, Honeywords, which are decoy passwords stored alongside a real password, appear to be a proactive method to help detect password credentials misuse. However, despite over a decade of research, this technique has not been adopted by major authentication...

Is Your Prompt Poisoning Code? Defect Induction Rates and Security Mitigation Strategies

Large language models LLMs have become indispensable for automated code generation, yet the quality and security of their outputs remain a critical concern. Existing studies predominantly concentrate on adversarial attacks or inherent flaws within the models. However, a more prevalent yet...

RejSCore: Rejection Sampling Core for Multivariate-Based Public Key Cryptography

Post-quantum multivariate public key cryptography MPKC schemes resist quantum threats but require heavy operations, such as rejection sampling, which challenge resource-limited devices. Prior hardware designs have addressed various aspects of MPKC signature generation. However, rejection sampling...

Breaking Agent Backbones: Evaluating the Security of Backbone LLMs in AI Agents

AI agents powered by large language models LLMs are being deployed at scale, yet we lack a systematic understanding of how the choice of backbone LLM affects agent security. The non-deterministic sequential nature of AI agents complicates security modeling, while the integration of traditional...

SecureLearn - an Attack-Agnostic Defense for Multiclass Machine Learning against Data Poisoning Attacks

Data poisoning attacks are a potential threat to machine learning ML models, aiming to manipulate training datasets to disrupt their performance. Existing defenses are mostly designed to mitigate specific poisoning attacks or are aligned with particular ML algorithms. Furthermore, most defenses a...

Securing AI Agent Execution

Large Language Models LLMs have evolved into AI agents that interact with external tools and environments to perform complex tasks. The Model Context Protocol MCP has become the de facto standard for connecting agents with such resources, but security has lagged behind: thousands of MCP servers...

Actionable Cybersecurity Notifications for Smart Homes: A User Study on the Role of Length and Complexity

The proliferation of smart home devices has increased convenience but also introduced cybersecurity risks for everyday users, as many devices lack robust security features. Intrusion Detection Systems are a prominent approach to detecting cybersecurity threats. However, their alerts often use...

Jailbreak Mimicry: Automated Discovery of Narrative-Based Jailbreaks for Large Language Models

Large language models LLMs remain vulnerable to sophisticated prompt engineering attacks that exploit contextual framing to bypass safety mechanisms, posing significant risks in cybersecurity applications. We introduce Jailbreak Mimicry, a systematic methodology for training compact attacker mode...

The Qey: Implementation and Performance Study of Post Quantum Cryptography in FIDO2

Authentication systems have evolved a lot since the 1960s when Fernando Corbato first proposed the password-based authentication. In 2013, the FIDO Alliance proposed using secure hardware for authentication, thus marking a milestone in the passwordless authentication era 1. Passwordless...

Wazuh 4.14.0

Wazuh is a free and open source security platform that unifies XDR and SIEM capabilities. It protects workloads across on-premises, virtualized, containerized, and cloud-based environments. This is the source code release...

The Trojan Example: Jailbreaking LLMs through Template Filling and Unsafety Reasoning

Large Language Models LLMs have advanced rapidly and now encode extensive world knowledge. Despite safety fine-tuning, however, they remain susceptible to adversarial prompts that elicit harmful content. Existing jailbreak techniques fall into two categories: white-box methods e.g., gradient-base...

Lightweight and Breach-Resilient Authenticated Encryption Framework for Internet of Things

The Internet of Things IoT relies heavily on resource-limited devices to communicate critical e.g., military data information under low-energy adversarial environments and low-latency wireless channels. Authenticated Encryption AE guarantees confidentiality, authenticity, and integrity, making it...

SAND: A Self-Supervised and Adaptive NAS-Driven Framework for Hardware Trojan Detection

The globalized semiconductor supply chain has made Hardware Trojans HT a significant security threat to embedded systems, necessitating the design of efficient and adaptable detection mechanisms. Despite promising machine learning-based HT detection techniques in the literature, they suffer from ...

Security Analysis of LTE Connectivity in Connected Cars: A Case Study of Tesla

Modern connected vehicles rely on persistent LTE connectivity to enable remote diagnostics, over-the-air OTA updates, and critical safety services. While mobile network vulnerabilities are well documented in the smartphone ecosystem, their impact in safety-critical automotive settings remains...

Enhanced MLLM Black-Box Jailbreaking Attacks and Defenses

Multimodal large language models MLLMs comprise of both visual and textual modalities to process vision language tasks. However, MLLMs are vulnerable to security-related issues, such as jailbreak attacks that alter the model's input to induce unauthorized or harmful responses. The incorporation o...