Picklescan missing detection when calling pytorch function torch.jit.unsupported_tensor_ops.execWrapper

Summary Using torch.jit.unsupportedtensorops.execWrapper function, which is a pytorch library function to execute remote pickle file. Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to torch.jit.unsupportedtensorops.execWrapper function...

Aura-CAPTCHA: a Reinforcement Learning and GAN-Enhanced Multi-Modal CAPTCHA System

Aura-CAPTCHA was developed as a multi-modal CAPTCHA system to address vulnerabilities in traditional methods that are increasingly bypassed by AI technologies, such as Optical Character Recognition OCR and adversarial image processing. The design integrated Generative Adversarial Networks GANs fo...

When Machine Learning Meets Vulnerability Discovery: Challenges and Lessons Learned

In recent years, machine learning has demonstrated impressive results in various fields, including software vulnerability detection. Nonetheless, using machine learning to identify software vulnerabilities presents new challenges, especially regarding the scale of data involved, which was not a...

Linux Distros Unpatched Vulnerability : CVE-2017-14099

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - In res/resrtpasterisk.c in Asterisk 11.x before 11.25.2, 13.x before 13.17.1, and 14.x before 14.6.1 and Certified Asterisk 11.x before 11.6-cert17 and 13.x...

On the Security and Privacy of Federated Learning: a Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions

Federated Learning FL is an emerging distributed machine learning paradigm enabling multiple clients to train a global model collaboratively without sharing their raw data. While FL enhances data privacy by design, it remains vulnerable to various security and privacy threats. This survey provide...

DDoS Attacks in Cloud Computing: Detection and Prevention

DDoS attacks are one of the most prevalent and harmful cybersecurity threats faced by organizations and individuals today. In recent years, the complexity and frequency of DDoS attacks have increased significantly, making it challenging to detect and mitigate them effectively. The study analyzes...

Addressing Side-Channel Threats in Quantum Key Distribution Via Deep Anomaly Detection

Traditional countermeasures against security side channels in quantum key distribution QKD systems often suffer from poor compatibility with deployed infrastructure, the risk of introducing new vulnerabilities, and limited applicability to specific types of attacks. In this work, we propose an...

Linux Distros Unpatched Vulnerability : CVE-2024-3653

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - A vulnerability was found in Undertow. This issue requires enabling the learning-push handler in the server's config, which is disabled by default, leaving the...

CVE-2025-54699

Improper Neutralization of Input During Web Page Generation 'Cross-site Scripting' vulnerability in masteriyo Masteriyo - LMS learning-management-system allows Stored XSS.This issue affects Masteriyo - LMS: from n/a through = 1.18.3...

RMSL: Weakly-Supervised Insider Threat Detection with Robust Multi-Sphere Learning

Insider threat detection aims to identify malicious user behavior by analyzing logs that record user interactions. Due to the lack of fine-grained behavior-level annotations, detecting specific behavior-level anomalies within user behavior sequences is challenging. Unsupervised methods face high...

Activate Me!: Designing Efficient Activation Functions for Privacy-Preserving Machine Learning with Fully Homomorphic Encryption

The growing adoption of machine learning in sensitive areas such as healthcare and defense introduces significant privacy and security challenges. These domains demand robust data protection, as models depend on large volumes of sensitive information for both training and inference. Fully...

Machine Learning-Based AES Key Recovery Via Side-Channel Analysis on the ASCAD Dataset

Cryptographic algorithms like AES and RSA are widely used and they are mathematically robust and almost unbreakable but its implementation on physical devices often leak information through side channels, such as electromagnetic EM emissions, potentially compromising said theoretically secure...

Malicious code in sec-learning (npm)

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

MAL-2025-32873 Malicious code in sec-learning (npm)

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

CVE-2025-54699 WordPress Masteriyo - LMS Plugin plugin <= 1.18.3 - Cross Site Scripting (XSS) Vulnerability

Improper Neutralization of Input During Web Page Generation 'Cross-site Scripting' vulnerability in masteriyo Masteriyo - LMS allows Stored XSS. This issue affects Masteriyo - LMS: from n/a through 1.18.3...

CVE-2025-54699

CVE-2025-54699 is an XSS vulnerability in Masteriyo LMS Plugin for WordPress, caused by improper input neutralization during web page generation and enabling stored XSS on pages served to users. Affected range: Masteriyo LMS up to version 1.18.3 (inclusive). Exploitation details are not provided ...

A Hierarchical IDS for Zero-Day Attack Detection in Internet of Medical Things Networks

The Internet of Medical Things IoMT is driving a healthcare revolution but remains vulnerable to cyberattacks such as denial of service, ransomware, data hijacking, and spoofing. These networks comprise resource constrained, heterogeneous devices e.g., wearable sensors, smart pills, implantables,...

MirGuard: Towards a Robust Provenance-Based Intrusion Detection System against Graph Manipulation Attacks

Learning-based Provenance-based Intrusion Detection Systems PIDSes have become essential tools for anomaly detection in host systems due to their ability to capture rich contextual and structural information, as well as their potential to detect unknown attacks. However, recent studies have shown...

REFN: a Reinforcement-Learning-From-Network Framework against 1-Day/N-Day Exploitations

The exploitation of 1 day or n day vulnerabilities poses severe threats to networked devices due to massive deployment scales and delayed patching average Mean Time To Patch exceeds 60 days. Existing defenses, including host based patching and network based filtering, are inadequate due to limite...

Code Vulnerability Detection across Different Programming Languages with AI Models

Security vulnerabilities present in a code that has been written in diverse programming languages are among the most critical yet complicated aspects of source code to detect. Static analysis tools based on rule-based patterns usually do not work well at detecting the context-dependent bugs and...