Zeek 8.0.4

Zeek is a powerful network analysis framework that is much different from the typical IDS you may know. While focusing on network security monitoring, Zeek provides a comprehensive platform for more general network traffic analysis as well. Well grounded in more than 15 years of research, Zeek ha...

JPRO: Automated Multimodal Jailbreaking Via Multi-Agent Collaboration Framework

The widespread application of large VLMs makes ensuring their secure deployment critical. While recent studies have demonstrated jailbreak attacks on VLMs, existing approaches are limited: they require either white-box access, restricting practicality, or rely on manually crafted patterns, leadin...

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...

Wapiti Web Application Vulnerability Scanner 3.2.9

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...

Synergistic Development of Cybersecurity and Functional Safety for Smart Electric Vehicles

The introduction of Smart Electric Vehicles SEVs represents an increasingly disruption on automotive area, once integrates advanced computer and communication technologies to highly electrical cars, which come with high performances, environment friendly and user friendly characteristics . But th...

Botan C++ Crypto Algorithms Library 3.10.0

Botan is a C++ library of cryptographic algorithms, including AES, DES, SHA-1, RSA, DSA, Diffie-Hellman, and many others. It also supports X.509 certificates and CRLs, and PKCS 10 certificate requests, and has a high level filter/pipe message processing system. The library is easily portable to...

Singling out People without Knowing Their Names - Behavioural Targeting, Pseudonymous Data, and the New Data Protection Regulation

Information about millions of people is collected for behavioural targeting, a type of marketing that involves tracking people's online behaviour for targeted advertising. It is hotly debated whether data protection law applies to behavioural targeting. Many behavioural targeting companies say...

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...

Secure Low-Altitude Maritime Communications Via Intelligent Jamming

Low-altitude wireless networks LAWNs have emerged as a viable solution for maritime communications. In these maritime LAWNs, unmanned aerial vehicles UAVs serve as practical low-altitude platforms for wireless communications due to their flexibility and ease of deployment. However, the open and...

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...

Inside LockBit: Technical, Behavioral, and Financial Anatomy of a Ransomware Empire

LockBit has evolved from an obscure Ransomware-as-a-Service newcomer in 2019 to the most prolific ransomware franchise of 2024. Leveraging a recently leaked MySQL dump of the gang's management panel, this study offers an end-to-end reconstruction of LockBit's technical, behavioral, and financial...

Enhancing Deep Learning-Based Rotational-XOR Attacks on Lightweight Block Ciphers Simon32/64 and Simeck32/64

At CRYPTO 2019, Gohr pioneered neural cryptanalysis by introducing differential-based neural distinguishers to attack Speck32/64, establishing a novel paradigm combining deep learning with differential cryptanalysis.Since then, constructing neural distinguishers has become a significant approach ...

CYPRESS: Transferring Secrets in the Shadow of Visible Packets

Network steganography and covert communication channels have been studied extensively in the past. However, prior works offer minimal practical use for their proposed techniques and are limited to specific use cases and network protocols. In this paper, we show that covert channels in networking...

KG-DF: A Black-Box Defense Framework against Jailbreak Attacks Based on Knowledge Graphs

With the widespread application of large language models LLMs in various fields, the security challenges they face have become increasingly prominent, especially the issue of jailbreak. These attacks induce the model to generate erroneous or uncontrolled outputs through crafted inputs, threatenin...

A Visual Perception-Based Tunable Framework and Evaluation Benchmark for H.265/HEVC ROI Encryption

ROI selective encryption, as an efficient privacy protection technique, encrypts only the key regions in the video, thereby ensuring security while minimizing the impact on coding efficiency. However, existing ROI-based video encryption methods suffer from insufficient flexibility and lack of a...

EASE: Practical and Efficient Safety Alignment for Small Language Models

Small language models SLMs are increasingly deployed on edge devices, making their safety alignment crucial yet challenging. Current shallow alignment methods that rely on direct refusal of malicious queries fail to provide robust protection, particularly against adversarial jailbreaks. While...

SoK: Systematizing a Decade of Architectural RowHammer Defenses through the Lens of Streaming Algorithms

A decade after its academic introduction, RowHammer RH remains a moving target that continues to challenge both the industry and academia. With its potential to serve as a critical attack vector, the ever-decreasing RH threshold now threatens DRAM process technology scaling, with a superlinearly...

RAG-Targeted Adversarial Attack on LLM-Based Threat Detection and Mitigation Framework

The rapid expansion of the Internet of Things IoT is reshaping communication and operational practices across industries, but it also broadens the attack surface and increases susceptibility to security breaches. Artificial Intelligence has become a valuable solution in securing IoT networks, wit...

Enhancing Adversarial Robustness of IoT Intrusion Detection Via SHAP-Based Attribution Fingerprinting

The rapid proliferation of Internet of Things IoT devices has transformed numerous industries by enabling seamless connectivity and data-driven automation. However, this expansion has also exposed IoT networks to increasingly sophisticated security threats, including adversarial attacks targeting...

Cryptographic Binding Should Not Be Optional: A Formal-Methods Analysis of FIDO UAF Channel Binding

As a case study in cryptographic binding, we present a formal-methods analysis of the cryptographic channel binding mechanisms in the Fast IDentity Online FIDO Universal Authentication Framework UAF authentication protocol, which seeks to reduce the use of traditional passwords in favor of...

HYDRA: A Hybrid Heuristic-Guided Deep Representation Architecture for Predicting Latent Zero-Day Vulnerabilities in Patched Functions

Software security testing, particularly when enhanced with deep learning models, has become a powerful approach for improving software quality, enabling faster detection of known flaws in source code. However, many approaches miss post-fix latent vulnerabilities that remain even after patches...

Injecting Falsehoods: Adversarial Man-In-The-Middle Attacks Undermining Factual Recall in LLMs

LLMs are now an integral part of information retrieval. As such, their role as question answering chatbots raises significant concerns due to their shown vulnerability to adversarial man-in-the-middle MitM attacks. Here, we propose the first principled attack evaluation on LLM factual memory unde...

A Secured Intent-Based Networking (SIBN) with Data-Driven Time-Aware Intrusion Detection

While Intent-Based Networking IBN promises operational efficiency through autonomous and abstraction-driven network management, a critical unaddressed issue lies in IBN's implicit trust in the integrity of intent ingested by the network. This inherent assumption of data reliability creates a blin...

BLADE: Behavior-Level Anomaly Detection Using Network Traffic in Web Services

With their widespread popularity, web services have become the main targets of various cyberattacks. Existing traffic anomaly detection approaches focus on flow-level attacks, yet fail to recognize behavior-level attacks, which appear benign in individual flows but reveal malicious purpose using...

PhantomFetch: Obfuscating Loads against Prefetcher Side-Channel Attacks

The IP-stride prefetcher has recently been exploited to leak secrets through side-channel attacks. It, however, cannot be simply disabled for security with prefetching speedup as a sacrifice. The state-of-the-art defense tries to retain the prefetching effect by hardware modification. In this...

Large Language Models for Explainable Threat Intelligence

As cyber threats continue to grow in complexity, traditional security mechanisms struggle to keep up. Large language models LLMs offer significant potential in cybersecurity due to their advanced capabilities in text processing and generation. This paper explores the use of LLMs with...

Quantifying the Risk of Transferred Black Box Attacks

Neural networks have become pervasive across various applications, including security-related products. However, their widespread adoption has heightened concerns regarding vulnerability to adversarial attacks. With emerging regulations and standards emphasizing security, organizations must...

When AI Meets the Web: Prompt Injection Risks in Third-Party AI Chatbot Plugins

Prompt injection attacks pose a critical threat to large language models LLMs, with prior work focusing on cutting-edge LLM applications like personal copilots. In contrast, simpler LLM applications, such as customer service chatbots, are widespread on the web, yet their security posture and...

Chasing One-Day Vulnerabilities across Open Source Forks

Tracking vulnerabilities inherited from third-party open-source components is a well-known challenge, often addressed by tracing the threads of dependency information. However, vulnerabilities can also propagate through forking: a repository forked after the introduction of a vulnerability, but...

From Model to Breach: Towards Actionable LLM-Generated Vulnerabilities Reporting

As the role of Large Language Models LLM-based coding assistants in software development becomes more critical, so does the role of the bugs they generate in the overall cybersecurity landscape. While a number of LLM code security benchmarks have been proposed alongside approaches to improve the...

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...

GPT-5 at CTFs: Case Studies from Top-Tier Cybersecurity Events

OpenAI and DeepMind's AIs recently got gold at the IMO math olympiad and ICPC programming competition. We show frontier AI is similarly good at hacking by letting GPT-5 compete in elite CTF cybersecurity competitions. In one of this year's hardest events, it outperformed 93% of humans finishing...

Explaining Software Vulnerabilities with Large Language Models

The prevalence of security vulnerabilities has prompted companies to adopt static application security testing SAST tools for vulnerability detection. Nevertheless, these tools frequently exhibit usability limitations, as their generic warning messages do not sufficiently communicate important...

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...

Security Evaluation of Quantum Circuit Split Compilation under an Oracle-Guided Attack

Quantum circuits are the fundamental representation of quantum algorithms and constitute valuable intellectual property IP. Multiple quantum circuit obfuscation QCO techniques have been proposed in prior research to protect quantum circuit IP against malicious compilers. However, there has not be...

Black-Box Guardrail Reverse-Engineering Attack

Large language models LLMs increasingly employ guardrails to enforce ethical, legal, and application-specific constraints on their outputs. While effective at mitigating harmful responses, these guardrails introduce a new class of vulnerabilities by exposing observable decision patterns. In this...

Large Language Models for Cyber Security

This paper studies the integration off Large Language Models into cybersecurity tools and protocols. The main issue discussed in this paper is how traditional rule-based and signature based security systems are not enough to deal with modern AI powered cyber threats. Cybersecurity industry is...

Trustworthiness Calibration Framework for Phishing Email Detection Using Large Language Models

Phishing emails continue to pose a persistent challenge to online communication, exploiting human trust and evading automated filters through realistic language and adaptive tactics. While large language models LLMs such as GPT-4 and LLaMA-3-8B achieve strong accuracy in text classification, thei...

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...

Unclonable Cryptography in Linear Quantum Memory

Quantum cryptography is a rapidly-developing area which leverages quantum information to accomplish classically-impossible tasks. In many of these protocols, quantum states are used as long-term cryptographic keys. Typically, this is to ensure the keys cannot be copied by an adversary, owing to t...

Confidential Computing for Cloud Security: Exploring Hardware Based Encryption Using Trusted Execution Environments

The growth of cloud computing has revolutionized data processing and storage capacities to another levels of scalability and flexibility. But in the process, it has created a huge challenge of security, especially in terms of safeguarding sensitive data. Classical security practices, including...

Tight Analysis of a Grover-Based Quantum Secret Sharing Scheme

Secret-sharing schemes allow a dealer to split a secret into multiple "shares" and distribute them individually among many parties while mandating certain constraints on its reconstruction. Such protocols are usually executed over a secure communication channel since an eavesdropper, after...

Certified Randomness Amplification by Dynamically Probing Remote Random Quantum States

Cryptography depends on truly unpredictable numbers, but physical sources emit biased or correlated bits. Quantum mechanics enables the amplification of imperfect randomness into nearly perfect randomness, but prior demonstrations have required physically co-located, loophole-free Bell tests,...

Temporal Analysis Framework for Intrusion Detection Systems: A Novel Taxonomy for Time-Aware Cybersecurity

Most intrusion detection systems still identify attacks only after significant damage has occurred, detecting late-stage tactics rather than early indicators of compromise. This paper introduces a temporal analysis framework and taxonomy for time-aware network intrusion detection. Through a...

Hybrid Fuzzing with LLM-Guided Input Mutation and Semantic Feedback

Software fuzzing has become a cornerstone in automated vulnerability discovery, yet existing mutation strategies often lack semantic awareness, leading to redundant test cases and slow exploration of deep program states. In this work, I present a hybrid fuzzing framework that integrates static an...

Specification-Guided Vulnerability Detection with Large Language Models

Large language models LLMs have achieved remarkable progress in code understanding tasks. However, they demonstrate limited performance in vulnerability detection and struggle to distinguish vulnerable code from patched code. We argue that LLMs lack understanding of security specifications -- the...

SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection

The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for 1 detecting malicious cyberattacks and 2 identifying faulty...

Whisper Leak: A Side-Channel Attack on Large Language Models

Large Language Models LLMs are increasingly deployed in sensitive domains including healthcare, legal services, and confidential communications, where privacy is paramount. This paper introduces Whisper Leak, a side-channel attack that infers user prompt topics from encrypted LLM traffic by...

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...

Security Analysis of Agentic AI Communication Protocols: A Comparative Evaluation

Multi-agent systems MAS powered by artificial intelligence AI are increasingly foundational to complex, distributed workflows. Yet, the security of their underlying communication protocols remains critically under-examined. This paper presents the first empirical, comparative security analysis of...