Hybrid IDS Using Signature-Based and Anomaly-Based Detection

Intrusion detection systems IDS are essential for protecting computer systems and networks against a wide range of cyber threats that continue to evolve over time. IDS are commonly categorized into two main types, each with its own strengths and limitations, such as difficulty in detecting...

Enhancing Fuzz Testing Efficiency through Automated Fuzz Target Generation

Fuzzing continues to be the most effective method for identifying security vulnerabilities in software. In the context of fuzz testing, the fuzzer supplies varied inputs to fuzz targets, which are designed to comprehensively exercise critical sections of the client code. Various studies have...

Many Hands Make Light Work: An LLM-Based Multi-Agent System for Detecting Malicious PyPI Packages

Malicious code in open-source repositories such as PyPI poses a growing threat to software supply chains. Traditional rule-based tools often overlook the semantic patterns in source code that are crucial for identifying adversarial components. Large language models LLMs show promise for software...

A Defender-Attacker-Defender Model for Optimizing the Resilience of Hospital Networks to Cyberattacks

Considering the increasing frequency of cyberattacks affecting multiple hospitals simultaneously, improving resilience at a network level is essential. Various countermeasures exist to improve resilience against cyberattacks, such as deploying controls that strengthen IT infrastructures to limit...

Shaping a Quantum-Resistant Future: Strategies for Post-Quantum PKI

As the quantum computing era approaches, securing classical cryptographic protocols becomes imperative. Public key cryptography is widely used for signature and key exchange but it is the type of cryptography more threatened by quantum computing. Its application typically requires support via a...

LoRA As Oracle

Backdoored and privacy-leaking deep neural networks pose a serious threat to the deployment of machine learning systems in security-critical settings. Existing defenses for backdoor detection and membership inference typically require access to clean reference models, extensive retraining, or...

SimFuzz: Similarity-Guided Block-Level Mutation for RISC-V Processor Fuzzing

The Instruction Set Architecture ISA defines processor operations and serves as the interface between hardware and software. As an open ISA, RISC-V lowers the barriers to processor design and encourages widespread adoption, but also exposes processors to security risks such as functional bugs...

Predicting Tail-Risk Escalation in IDS Alert Time Series

Network defenders face a steady stream of attacks, observed as raw Intrusion Detection System IDS alerts. The sheer volume of alerts demands prioritization, typically based on high-level risk classifications. This work expands the scope of risk measurement by examining alerts not only through the...

Gamifying Cyber Governance: A Virtual Escape Room to Transform Cybersecurity Policy Education

Serious games are gaining popularity as effective teaching and learning tools, providing engaging, interactive, and practical experiences for students. Gamified learning experiences, such as virtual escape rooms, have emerged as powerful tools in bridging theory and practice, fostering deeper...

Wireshark Analyzer 4.6.3

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...

Multi-Agent Taint Specification Extraction for Vulnerability Detection

Static Application Security Testing SAST tools using taint analysis are widely viewed as providing higher-quality vulnerability detection results compared to traditional pattern-based approaches. However, performing static taint analysis for JavaScript poses two major challenges. First,...

Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale

The rise of AI agent frameworks has introduced agent skills, modular packages containing instructions and executable code that dynamically extend agent capabilities. While this architecture enables powerful customization, skills execute with implicit trust and minimal vetting, creating a...

AJAR: Adaptive Jailbreak Architecture for Red-Teaming

As Large Language Models LLMs evolve from static chatbots into autonomous agents capable of tool execution, the landscape of AI safety is shifting from content moderation to action security. However, existing red-teaming frameworks remain bifurcated: they either focus on rigid, script-based text...

Advanced Encryption Technique for Multimedia Data Using Sudoku-Based Algorithms for Enhanced Security

Encryption and Decryption is the process of sending a message in a ciphered way that appears meaningless and could be deciphered using a key for security purposes to avoid data breaches. This paper expands on the previous work on Sudoku-based encryption methods, applying it to other forms of medi...

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...

WMI Event Subscription Event Log Persistence

This Metasploit module will create a permanent WMI event subscription to achieve file-less persistence using an event filter that will query the event log for an EVENTIDTRIGGER default: failed logon request id 4625 that also contains a specified USERNAMETRIGGER note: failed logon auditing must be...

A Novel Contrastive Loss for Zero-Day Network Intrusion Detection

Machine learning has achieved state-of-the-art results in network intrusion detection; however, its performance significantly degrades when confronted by a new attack class -- a zero-day attack. In simple terms, classical machine learning-based approaches are adept at identifying attack classes o...

Diffusion-Driven Deceptive Patches: Adversarial Manipulation and Forensic Detection in Facial Identity Verification

This work presents an end-to-end pipeline for generating, refining, and evaluating adversarial patches to compromise facial biometric systems, with applications in forensic analysis and security testing. We utilize FGSM to generate adversarial noise targeting an identity classifier and employ a...

EuskalHack Security Congress IX Call for Papers

EuskalHack Security Congress ninth edition is a new proposal from the EuskalHack Computer Security Association, with the aim to promote the community growth and the culture in the digital security field. As usual, in this new edition proximity to our public and technical quality will be our...

WMI Event Subscription Logon Timer Persistence

This Metasploit module will create a permanent WMI event subscription to achieve file-less persistence using an event filter that will trigger the payload after the system has a certain uptime. Payloads will trigger every minute until the set end time. Additionally a custom command can be specifi...

WMI Event Subscription Interval Persistence

This Metasploit module will create a permanent WMI event subscription to achieve file-less persistence using an event filter that triggers the payload after the specified CALLBACKINTERVAL. If the persistence is not installed, it will keep triggering payloads to spawn. Additionally a custom comman...

WMI Event Subscription Process Persistence

This Metasploit module will create a permanent WMI event subscription to achieve file-less persistence using an event filter that triggers the payload when the specified process is started. Additionally a custom command can be specified to run once the trigger is activated using the advanced opti...

A Risk-Stratified Benchmark Dataset for Bad Randomness (SWC-120) Vulnerabilities in Ethereum Smart Contracts

Many Ethereum smart contracts rely on block attributes such as block.timestamp or blockhash to generate random numbers for applications like lotteries and games. However, these values are predictable and miner-manipulable, creating the Bad Randomness vulnerability SWC-120 that has led to real-wor...

The Promptware Kill Chain: How Prompt Injections Gradually Evolved into a Multi-Step Malware

Whitepaper called The Promptware Kill Chain: How Prompt Injections Gradually Evolved Into A Multi-Step Malware...

Private Links, Public Leaks: Consequences of Frictionless User Experience on the Security and Privacy Posture of SMS-Delivered URLs

Digital service providers often prioritize a frictionless user experience by adopting technologies that simplify access to their services. One widely used mechanism is the Short Message Service SMS to deliver links URLs that enable single-click access to online services with little to no...

Blue Teaming Function-Calling Agents

We present an experimental evaluation that assesses the robustness of four open source LLMs claiming function-calling capabilities against three different attacks, and we measure the effectiveness of eight different defences. Our results show how these models are not safe by default, and how the...

Malware Classification Using Diluted Convolutional Neural Network with Fast Gradient Sign Method

Android malware has become an increasingly critical threat to organizations, society and individuals, posing significant risks to privacy, data security and infrastructure. As malware continues to evolve in terms of complexity and sophistication, the mitigation and detection of these malicious...

AmbShield: Enhancing Physical Layer Security with Ambient Backscatter Devices against Eavesdroppers

Passive eavesdropping compromises confidentiality in wireless networks, especially in resource-constrained environments where heavyweight cryptography is impractical. Physical layer security PLS exploits channel randomness and spatial selectivity to confine information to an intended receiver wit...

Baiting AI: Deceptive Adversary against AI-Protected Industrial Infrastructures

This paper explores a new cyber-attack vector targeting Industrial Control Systems ICS, particularly focusing on water treatment facilities. Developing a new multi-agent Deep Reinforcement Learning DRL approach, adversaries craft stealthy, strategically timed, wear-out attacks designed to subtly...

KryptoPilot: An Open-World Knowledge-Augmented LLM Agent for Automated Cryptographic Exploitation

Capture-the-Flag CTF competitions play a central role in modern cybersecurity as a platform for training practitioners and evaluating offensive and defensive techniques derived from real-world vulnerabilities. Despite recent advances in large language models LLMs, existing LLM-based agents remain...

LLMs in Code Vulnerability Analysis: A Proof of Concept

Context: Traditional software security analysis methods struggle to keep pace with the scale and complexity of modern codebases, requiring intelligent automation to detect, assess, and remediate vulnerabilities more efficiently and accurately. Objective: This paper explores the incorporation of...

Proactively Detecting Threats: A Novel Approach Using LLMs

Enterprise security faces escalating threats from sophisticated malware, compounded by expanding digital operations. This paper presents the first systematic evaluation of large language models LLMs to proactively identify indicators of compromise IOCs from unstructured web-based threat...

A Decompilation-Driven Framework for Malware Detection with Large Language Models

The parallel evolution of Large Language Models LLMs with advanced code-understanding capabilities and the increasing sophistication of malware presents a new frontier for cybersecurity research. This paper evaluates the efficacy of state-of-the-art LLMs in classifying executable code as either...

Deep Learning-Based Binary Analysis for Vulnerability Detection in X86-64 Machine Code

While much of the current research in deep learning-based vulnerability detection relies on disassembled binaries, this paper explores the feasibility of extracting features directly from raw x86-64 machine code. Although assembly language is more interpretable for humans, it requires more comple...

Integrating APK Image and Text Data for Enhanced Threat Detection: A Multimodal Deep Learning Approach to Android Malware

As zero-day Android malware attacks grow more sophisticated, recent research highlights the effectiveness of using image-based representations of malware bytecode to detect previously unseen threats. However, existing studies often overlook how image type and resolution affect detection and ignor...

Memory DisOrder: Memory Re-Orderings As a Timerless Side-Channel

To improve efficiency, nearly all parallel processing units CPUs and GPUs implement relaxed memory models in which memory operations may be re-ordered, i.e., executed out-of-order. Prior testing work in this area found that memory re-orderings are observed more frequently when other cores are...

Suricata IDPE 8.0.3

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...

On-Chip Semi-Device-Independent Quantum Random Number Generator Exploiting Contextuality

We present a semi-device-independent quantum random number generator QRNG based on the violation of a contextuality inequality, implemented by the integration of two silicon photonic chips. Our system combines a heralded single-photon source with a reconfigurable interferometric mesh to implement...

A Survey of Security Challenges and Solutions for UAS Traffic Management (UTM) and Small Unmanned Aerial Systems (SUAS)

The rapid growth of small Unmanned Aerial Systems sUAS for civil and commercial missions has intensified concerns about their resilience to cyber-security threats. Operating within the emerging UAS Traffic Management UTM framework, these lightweight and highly networked platforms depend on secure...

Aura Inspector

aura-inspector is a Swiss Army knife of Salesforce Experience Cloud testing. It facilitates in discovering misconfigured Salesforce Experience Cloud applications as well as automates much of the testing process...

Malware Detection Based on API Calls: A Reproducibility Study

This study independently reproduces the malware detection methodology presented by Felli cious et al. 7, which employs order-invariant API call frequency analysis using Random Forest classification. We utilized the original public dataset 250,533 training samples, 83,511 test samples and replicat...

Zeek 8.0.5

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...

Memory-Based Malware Detection under Limited Data Conditions: A Comparative Evaluation of TabPFN and Ensemble Models

Artificial intelligence and machine learning have significantly advanced malware research by enabling automated threat detection and behavior analysis. However, the availability of exploitable data is limited, due to the absence of large datasets with real-world data. Despite the progress of AI i...

InvisibleJS JavaScript Hiding Tool

Welcome to InvisibleJS, an experimental tool for hiding your JavaScript source code in plain sight using zero-width characters. This repository features two distinct versions of the obfuscator, tailored for different execution environments...

When Bots Take the Bait: Exposing and Mitigating the Emerging Social Engineering Attack in Web Automation Agent

Web agents, powered by large language models LLMs, are increasingly deployed to automate complex web interactions. The rise of open-source frameworks e.g., Browser Use, Skyvern-AI has accelerated adoption, but also broadened the attack surface. While prior research has focused on model threats su...

A High-Recall Cost-Sensitive Machine Learning Framework for Real-Time Online Banking Transaction Fraud Detection

Fraudulent activities on digital banking services are becoming more intricate by the day, challenging existing defenses. While older rule driven methods struggle to keep pace, even precision focused algorithms fall short when new scams are introduced. These tools typically overlook subtle shifts ...

CarRentalMS 2.0 Cross Site Request Forgery

CarRentalMS version 2.0 suffers from a cross site request forgery vulnerability...

YARA-X 1.11.0

YARA-X is a re-incarnation of YARA, a pattern matching tool designed with malware researchers in mind. This new incarnation intends to be faster, safer and more user-friendly than its predecessor. The ultimate goal of YARA-X is replacing YARA as the default pattern matching tool for malware...

SecureCAI: Injection-Resilient LLM Assistants for Cybersecurity Operations

Large Language Models have emerged as transformative tools for Security Operations Centers, enabling automated log analysis, phishing triage, and malware explanation; however, deployment in adversarial cybersecurity environments exposes critical vulnerabilities to prompt injection attacks where...

Operational Runtime Behavior Mining for Open-Source Supply Chain Security

Open-source software OSS is a critical component of modern software systems, yet supply chain security remains challenging in practice due to unavailable or obfuscated source code. Consequently, security teams often rely on runtime observations collected from sandboxed executions to investigate...