Intellicise Wireless Networks Meet Agentic AI: A Security and Privacy Perspective

Intellicise Intelligent and Concise wireless network is the main direction of the evolution of future mobile communication systems, a perspective now widely acknowledged across academia and industry. As a key technology within it, Agentic AI has garnered growing attention due to its advanced...

When Security Meets Usability: An Empirical Investigation of Post-Quantum Cryptography APIs

Advances in quantum computing increasingly threaten the security and privacy of data protected by current cryptosystems, particularly those relying on public-key cryptography. In response, the international cybersecurity community has prioritized the implementation of Post-Quantum Cryptography PQ...

Secure and Energy-Efficient Wireless Agentic AI Networks

In this paper, we introduce a secure wireless agentic AI network comprising one supervisor AI agent and multiple other AI agents to provision quality of service QoS for users' reasoning tasks while ensuring confidentiality of private knowledge and reasoning outcomes. Specifically, the supervisor ...

Exposing the Systematic Vulnerability of Open-Weight Models to Prefill Attacks

As the capabilities of large language models continue to advance, so does their potential for misuse. While closed-source models typically rely on external defenses, open-weight models must primarily depend on internal safeguards to mitigate harmful behavior. Prior red-teaming research has largel...

TestSSL 3.2.3

testssl.sh is a free command line tool which checks a server's service on any port for the support of TLS/SSL ciphers, protocols as well as recent cryptographic flaws, and much more. It is written in pure bash, makes only use of standard Unix utilities, openssl and last but not least bash sockets...

Wazuh 4.14.3

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...

Next-Generation Cyberattack Detection with Large Language Models: Anomaly Analysis across Heterogeneous Logs

This project explores large language models LLMs for anomaly detection across heterogeneous log sources. Traditional intrusion detection systems suffer from high false positive rates, semantic blindness, and data scarcity, as logs are inherently sensitive, making clean datasets rare. We address...

CISA: Reducing the Attack Surface for End-of-Support Edge Devices

The Cybersecurity and Infrastructure Security Agency CISA, the Federal Bureau of Investigation FBI, and the U.K.’s National Cyber Security Centre NCSC are releasing this fact sheet to urge defensive action against malicious cyber activity by nation-state threat actors. Nation-state threat actors...

Reference-Free EM Validation Flow for Detecting Triggered Hardware Trojans

Hardware Trojans HTs threaten the trust and reliability of integrated circuits ICs, particularly when triggered HTs remain dormant during standard testing and activate only under rare conditions. Existing electromagnetic EM side-channel-based detection techniques often rely on golden references o...

Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents

Large language models LLMs have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation due to limited interaction, weak execution grounding, and a lack of experience reuse. We propose Co-RedTeam, a security-aware...

Jailbreaking LLMs Via Calibration

Safety alignment in Large Language Models LLMs often creates a systematic discrepancy between a model's aligned output and the underlying pre-aligned data distribution. We propose a framework in which the effect of safety alignment on next-token prediction is modeled as a systematic distortion of...

ReasoningBomb: A Stealthy Denial-Of-Service Attack by Inducing Pathologically Long Reasoning in Large Reasoning Models

Large reasoning models LRMs extend large language models with explicit multi-step reasoning traces, but this capability introduces a new class of prompt-induced inference-time denial-of-service PI-DoS attacks that exploit the high computational cost of reasoning. We first formalize inference cost...

Stealthy Poisoning Attacks Bypass Defenses in Regression Settings

Regression models are widely used in industrial processes, engineering and in natural and physical sciences, yet their robustness to poisoning has received less attention. When it has, studies often assume unrealistic threat models and are thus less useful in practice. In this paper, we propose a...

Eclipse Attacks on Ethereum'S Peer-To-Peer Network

Eclipse attacks isolate blockchain nodes by monopolizing their peer-to-peer connections. The attacks were extensively studied in Bitcoin SP'15, SP'20, CCS'21, SP'23 and Monero NDSS'25, but their practicality against Ethereum nodes remains underexplored, particularly in the post-Merge settings. We...

ZkRansomware: Proof-Of-Data Recoverability and Multi-Round Game Theoretic Modeling of Ransomware Decisions

Ransomware is still one of the most serious cybersecurity threats. Victims often pay but fail to regain access to their data, while also facing the danger of losing data privacy. These uncertainties heavily shape the attacker-victim dynamics in decision-making. In this paper, we introduce and...

Jailbreaking Large Language Models through Iterative Tool-Disguised Attacks Via Reinforcement Learning

Large language models LLMs have demonstrated remarkable capabilities across diverse applications, however, they remain critically vulnerable to jailbreak attacks that elicit harmful responses violating human values and safety guidelines. Despite extensive research on defense mechanisms, existing...

AutoVulnPHP: LLM-Powered Two-Stage PHP Vulnerability Detection and Automated Localization

PHP's dominance in web development is undermined by security challenges: static analysis lacks semantic depth, causing high false positives; dynamic analysis is computationally expensive; and automated vulnerability localization suffers from coarse granularity and imprecise context. Additionally,...

PDPL Metric: Validating a Scale to Measure Personal Data Privacy Literacy among University Students

Personal data privacy literacy PDPL refers to a collection of digital literacy skills related to an individuals ability to understand, evaluate, and manage the collection, use, and protection of personal data in online and digital environments. This study introduces and validates a new psychometr...

Agentic AI for Autonomous Defense in Software Supply Chain Security: Beyond Provenance to Vulnerability Mitigation

The software supply chain attacks are becoming more and more focused on trusted development and delivery procedures, so the conventional post-build integrity mechanisms cannot be used anymore. The available frameworks like SLSA, SBOM and in toto are majorly used to offer provenance and traceabili...

SoK: Reviewing Two Decades of Security, Privacy, Accessibility, and Usability Studies on Internet of Things for Older Adults

The Internet of Things IoT has the potential to enhance older adults' independence and quality of life, but it also exposes them to security, privacy, accessibility, and usability SPAU risks. We conducted a systematic review of 44 peer-reviewed studies published between 2004 and 2024 using a...

Data Protection and Corporate Reputation Management in the Digital Era

This paper analyzes the relationship between cybersecurity management, data protection, and corporate reputation in the context of digital transformation. The study examines how organizations implement strategies and tools to mitigate cyber risks, comply with regulatory requirements, and maintain...

SHERLOCK: A Deep Learning Approach to Detect Software Vulnerabilities

The increasing reliance on software in various applications has made the problem of software vulnerability detection more critical. Software vulnerabilities can lead to security breaches, data theft, and other negative outcomes. Traditional software vulnerability detection techniques, such as...

Agentic AI for 6G: A New Paradigm for Autonomous RAN Security Compliance

Agentic AI systems are emerging as powerful tools for automating complex, multi-step tasks across various industries. One such industry is telecommunications, where the growing complexity of next-generation radio access networks RANs opens up numerous opportunities for applying these systems...

Llama-Based Source Code Vulnerability Detection: Prompt Engineering Vs Fine Tuning

The significant increase in software production, driven by the acceleration of development cycles over the past two decades, has led to a steady rise in software vulnerabilities, as shown by statistics published yearly by the CVE program. The automation of the source code vulnerability detection...

Integrating Public Input and Technical Expertise for Effective Cybersecurity Policy Formulation

The evolving of digital transformation and increased use of technology comes with increased cyber vulnerabilities, which compromise national security. Cyber-threats become more sophisticated as the technology advances. This emphasises the need for strong risk mitigation strategies. To define stro...

Cybersecurity AI: The World's Top AI Agent for Security Capture-The-Flag (CTF)

Are Capture-the-Flag competitions obsolete? In 2025, Cybersecurity AI CAI systematically conquered some of the world's most prestigious hacking competitions, achieving Rank 1 at multiple events and consistently outperforming thousands of human teams. Across five major circuits-HTB's AI vs Humans,...

Quantum Ramp Secret Sharing from Haar Scrambling

Quantum information scrambling has emerged as a powerful tool for studying the dynamics of chaotic quantum many-body systems, assessing benchmarking protocols, and even investigating exotic black hole models. During quantum information scrambling, localized quantum information disperses across th...

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 ...

Password Strength Analysis through Social Network Data Exposure: A Combined Approach Relying on Data Reconstruction and Generative Models

Although passwords remain the primary defense against unauthorized access, users often tend to use passwords that are easy to remember. This behavior significantly increases security risks, also due to the fact that traditional password strength evaluation methods are often inadequate. In this...

Systematically Deconstructing APVD Steganography and Its Payload with a Unified Deep Learning Paradigm

In the era of digital communication, steganography allows covert embedding of data within media files. Adaptive Pixel Value Differencing APVD is a steganographic method valued for its high embedding capacity and invisibility, posing challenges for traditional steganalysis. This paper proposes a...

Beyond Fixed and Dynamic Prompts: Embedded Jailbreak Templates for Advancing LLM Security

As the use of large language models LLMs continues to expand, ensuring their safety and robustness has become a critical challenge. In particular, jailbreak attacks that bypass built-in safety mechanisms are increasingly recognized as a tangible threat across industries, driving the need for...

ProxyPrints: From Database Breach to Spoof, a Plug-And-Play Defense for Biometric Systems

Fingerprint recognition systems are widely deployed for authentication and forensic applications, but the security of stored fingerprint data remains a critical vulnerability. While many systems avoid storing raw fingerprint images in favor of minutiae-based templates, recent research shows that...

Zero Trust Security Model Implementation in Microservices Architectures Using Identity Federation

The microservice bombshells that have been linked with the microservice expansion have altered the application architectures, offered agility and scalability in terms of complexity in security trade-offs. Feeble legacy-based perimeter-based policies are unable to offer safeguard to distributed...

Design and Detection of Covert Man-In-The-Middle Cyberattacks on Water Treatment Plants

Cyberattacks targeting critical infrastructures, such as water treatment facilities, represent significant threats to public health, safety, and the environment. This paper introduces a systematic approach for modeling and assessing covert man-in-the-middle MitM attacks that leverage system...

AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models

Large Language Models LLMs remain vulnerable to jailbreaking attacks where adversarial prompts elicit harmful outputs, yet most evaluations focus on single-turn interactions while real-world attacks unfold through adaptive multi-turn conversations. We present AutoAdv, a training-free framework fo...

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...

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...

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...

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...

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...

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...

QORE : Quantum Secure 5G/B5G Core

Quantum computing is reshaping the security landscape of modern telecommunications. The cryptographic foundations that secure todays 5G systems, including RSA, Elliptic Curve Cryptography ECC, and Diffie-Hellman DH, are all susceptible to attacks enabled by Shors algorithm. Protecting 5G networks...

A Graph-Attentive LSTM Model for Malicious URL Detection

Malicious URLs pose significant security risks as they facilitate phishing attacks, distribute malware, and empower attackers to deface websites. Blacklist detection methods fail to identify new or obfuscated URLs because they depend on pre-existing patterns. This work presents a hybrid deep...

A Systematic Study on Generating Web Vulnerability Proof-Of-Concepts Using Large Language Models

Recent advances in Large Language Models LLMs have brought remarkable progress in code understanding and reasoning, creating new opportunities and raising new concerns for software security. Among many downstream tasks, generating Proof-of-Concept PoC exploits plays a central role in vulnerabilit...

VisualDAN: Exposing Vulnerabilities in VLMs with Visual-Driven DAN Commands

Vision-Language Models VLMs have garnered significant attention for their remarkable ability to interpret and generate multimodal content. However, securing these models against jailbreak attacks continues to be a substantial challenge. Unlike text-only models, VLMs integrate additional modalitie...

Securing IoT Devices in Smart Cities: A Review of Proposed Solutions

Privacy and security in Smart Cities remain at constant risk due to the vulnerabilities introduced by Internet of Things IoT devices. The limited computational resources of these devices make them especially susceptible to attacks, while their widespread adoption increases the potential impact of...

POLAR: Automating Cyber Threat Prioritization through LLM-Powered Assessment

Large Language Models LLMs are intensively used to assist security analysts in counteracting the rapid exploitation of cyber threats, wherein LLMs offer cyber threat intelligence CTI to support vulnerability assessment and incident response. While recent work has shown that LLMs can support a wid...

Environmental Rate Manipulation Attacks on Power Grid Security

The growing complexity of global supply chains has made hardware Trojans a significant threat in sensor-based power electronics. Traditional Trojan designs depend on digital triggers or fixed threshold conditions that can be detected during standard testing. In contrast, we introduce Environmenta...

Noisy Networks, Nosy Neighbors: Inferring Privacy Invasive Information from Encrypted Wireless Traffic

This thesis explores the extent to which passive observation of wireless traffic in a smart home environment can be used to infer privacy-invasive information about its inhabitants. Using a setup that mimics the capabilities of a nosy neighbor in an adjacent flat, we analyze raw 802.11 packets an...

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense

Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns that combine natural-language text with obfuscated URLs, forged headers, and malicious attachments, adapting their strategies within days to bypass...