Cyber Threat Hunting: Non-Parametric Mining of Attack Patterns from Cyber Threat Intelligence for Precise Threats Attribution

With the ever-changing landscape of cyber threats, identifying their origin has become paramount, surpassing the simple task of attack classification. Cyber threat attribution gives security analysts the insights they need to device effective threat mitigation strategies. Such strategies empower...

Time-Constrained Intelligent Adversaries for Automation Vulnerability Testing: a Multi-Robot Patrol Case Study

Simulating hostile attacks of physical autonomous systems can be a useful tool to examine their robustness to attack and inform vulnerability-aware design. In this work, we examine this through the lens of multi-robot patrol, by presenting a machine learning-based adversary model that observes...

Exploiting Timing Side-Channels in Quantum Circuits Simulation Via ML-Based Methods

As quantum computing advances, quantum circuit simulators serve as critical tools to bridge the current gap caused by limited quantum hardware availability. These simulators are typically deployed on cloud platforms, where users submit proprietary circuit designs for simulation. In this work, we...

browsersploit

This is an advanced browser exploit pack for internal and external pentesting, aiming to gain access to internal computers. The tool is not for script kiddies or non-advanced coders, as it contains bugs and is intended for experienced users. The pack includes various techniques to bypass antiviru...

Your Compiler Is Backdooring Your Model: Understanding and Exploiting Compilation Inconsistency Vulnerabilities in Deep Learning Compilers

Deep learning DL compilers are core infrastructure in modern DL systems, offering flexibility and scalability beyond vendor-specific libraries. This work uncovers a fundamental vulnerability in their design: can an official, unmodified compiler alter a model's semantics during compilation and...

Weakly Supervised Vulnerability Localization Via Multiple Instance Learning

Software vulnerability detection has emerged as a significant concern in the field of software security recently, capturing the attention of numerous researchers and developers. Most previous approaches focus on coarse-grained vulnerability detection, such as at the function or file level. Howeve...

Pikachu

This is a proof-of-concept PoC exploit for a vulnerable web application system called Pikachu. The system contains a variety of common web security vulnerabilities, including SQL injection, cross-site scripting XSS, cross-site request forgery CSRF, remote code execution RCE, and more. The...

A Comparison of Selected Image Transformation Techniques for Malware Classification

Recently, a considerable amount of malware research has focused on the use of powerful image-based machine learning techniques, which generally yield impressive results. However, before image-based techniques can be applied to malware, the samples must be converted to images, and there is no...

Finding SSH Strict Key Exchange Violations by State Learning

SSH is an important protocol for secure remote shell access to servers on the Internet. At USENIX 2024, B�umer et al. presented the Terrapin attack on SSH, which relies on the attacker injecting optional messages during the key exchange. To mitigate this attack, SSH vendors adopted an extension...

ExploitNotes

It is an offline collection of notes and examples for exploit...

SQL-Injection-Scanner

SQL-Injection-Scanner The following program is an injection sc...

CVE-2025-58993

Improper Neutralization of Special Elements used in an SQL Command 'SQL Injection' vulnerability in Themeum Tutor LMS tutor allows SQL Injection.This issue affects Tutor LMS: from n/a through = 3.7.4...

SAGE: Sample-Aware Guarding Engine for Robust Intrusion Detection against Adversarial Attacks

The rapid proliferation of the Internet of Things IoT continues to expose critical security vulnerabilities, necessitating the development of efficient and robust intrusion detection systems IDS. Machine learning-based intrusion detection systems ML-IDS have significantly improved threat detectio...

Contrastive Self-Supervised Network Intrusion Detection Using Augmented Negative Pairs

Network intrusion detection remains a critical challenge in cybersecurity. While supervised machine learning models achieve state-of-the-art performance, their reliance on large labelled datasets makes them impractical for many real-world applications. Anomaly detection methods, which train...

ALPHA: LLM-Enabled Active Learning for Human-Free Network Anomaly Detection

Network log data analysis plays a critical role in detecting security threats and operational anomalies. Traditional log analysis methods for anomaly detection and root cause analysis rely heavily on expert knowledge or fully supervised learning models, both of which require extensive labeled dat...

Malicious code in quant-learning (npm)

The package quant-learning was found to contain malicious code...

MAL-2025-45716 Malicious code in quant-learning (npm)

The package quant-learning was found to contain malicious code...

A Transformer-BiGRU-Based Framework with Data Augmentation and Confident Learning for Network Intrusion Detection

In today's fast-paced digital communication, the surge in network traffic data and frequency demands robust and precise network intrusion solutions. Conventional machine learning methods struggle to grapple with complex patterns within the vast network intrusion datasets, which suffer from data...

PT-2025-36249

Name of the Vulnerable Software and Affected Versions: MasterStudy LMS versions through 3.6.15 Description: The software contains a missing authorization flaw due to incorrectly configured access control security levels. Recommendations: Update MasterStudy LMS to a version later than 3.6.15...

Quantum AI Algorithm Development for Enhanced Cybersecurity: a Hybrid Approach to Malware Detection

This study explores the application of quantum machine learning QML algorithms to enhance cybersecurity threat detection, particularly in the classification of malware and intrusion detection within high-dimensional datasets. Classical machine learning approaches encounter limitations when dealin...