Real-VulLLM: An LLM Based Assessment Framework in the Wild

Artificial Intelligence AI and more specifically Large Language Models LLMs have demonstrated exceptional progress in multiple areas including software engineering, however, their capability for vulnerability detection in the wild scenario and its corresponding reasoning remains underexplored...

EUVD-2022-44775

Malicious code in bioql PyPI...

EUVD-2025-25090

Malicious code in bioql PyPI...

MAVUL: Multi-Agent Vulnerability Detection Via Contextual Reasoning and Interactive Refinement

The widespread adoption of open-source software OSS necessitates the mitigation of vulnerability risks. Most vulnerability detection VD methods are limited by inadequate contextual understanding, restrictive single-round interactions, and coarse-grained evaluations, resulting in undesired model...

PT-2025-49032

Name of the Vulnerable Software and Affected Versions Linux kernel affected versions not specified Description The Linux kernel’s DAMON virtual address space operation set implementation vaddr contains a flaw related to the pte offset map lock function within the page table walk callback. Repeate...

Large Language Models for Security Operations Centers: a Comprehensive Survey

Large Language Models LLMs have emerged as powerful tools capable of understanding and generating human-like text, offering transformative potential across diverse domains. The Security Operations Center SOC, responsible for safeguarding digital infrastructure, represents one of these domains. SO...

CVE-2025-58753

Copyparty vulnerability CVE-2025-58753 affects the Copyparty portable file server. The issue is a missing permission check in the shares feature (shr global option) that allowed access to other files in the same folder when a share was created for a single file, by guessing filenames. Subdirector...

A Kolmogorov-Arnold Network for Interpretable Cyberattack Detection in AGC Systems

Automatic Generation Control AGC is essential for power grid stability but remains vulnerable to stealthy cyberattacks, such as False Data Injection Attacks FDIAs, which can disturb the system's stability while evading traditional detection methods. Unlike previous works that relied on blackbox...

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...

An Empirical Study of Vulnerabilities in Python Packages and Their Detection

In the rapidly evolving software development landscape, Python stands out for its simplicity, versatility, and extensive ecosystem. Python packages, as units of organization, reusability, and distribution, have become a pressing concern, highlighted by the considerable number of vulnerability...

VULSOVER: Vulnerability Detection Via LLM-Driven Constraint Solving

Traditional vulnerability detection methods rely heavily on predefined rule matching, which often fails to capture vulnerabilities accurately. With the rise of large language models LLMs, leveraging their ability to understand code semantics has emerged as a promising direction for achieving more...

Linux Distros Unpatched Vulnerability : CVE-2021-34147

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - The Bluetooth Classic implementation in the Cypress WICED BT stack through 2.9.0 for CYW20735B1 does not properly handle the reception of a malformed LMP timing...

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...

UBUNTU-CVE-2025-38508

In the Linux kernel, the following vulnerability has been resolved: x86/sev: Use TSCFACTOR for Secure TSC frequency calculation When using Secure TSC, the GUESTTSCFREQ MSR reports a frequency based on the nominal P0 frequency, which deviates slightly typically 0.2% from the actual mean TSC...

CVE-2025-38508

The CVE-2025-38508 entry describes a Linux kernel vulnerability in x86/SEV: when Secure TSC is enabled, GUEST_TSC_FREQ is derived from the P0 frequency and diverges from the mean TSC frequency, causing clock skew in SEV-SNP VMs and resulting in hrtimer interrupts firing earlier than expected. The...

CVE-2025-38508 x86/sev: Use TSC_FACTOR for Secure TSC frequency calculation

In the Linux kernel, the following vulnerability has been resolved: x86/sev: Use TSCFACTOR for Secure TSC frequency calculation When using Secure TSC, the GUESTTSCFREQ MSR reports a frequency based on the nominal P0 frequency, which deviates slightly typically 0.2% from the actual mean TSC...

Enhance the Machine Learning Algorithm Performance in Phishing Detection with Keyword Features

Recently, we can observe a significant increase of the phishing attacks in the Internet. In a typical phishing attack, the attacker sets up a malicious website that looks similar to the legitimate website in order to obtain the end-users' information. This may cause the leakage of the sensitive...

How the Solid Protocol Restores Digital Agency

The current state of digital identity is a mess. Your personal information is scattered across hundreds of locations: social media companies, IoT companies, government agencies, websites you have accounts on, and data brokers you've never heard of. These entities collect, store, and trade your...

SVAgent: AI Agent for Hardware Security Verification Assertion

Verification using SystemVerilog assertions SVA is one of the most popular methods for detecting circuit design vulnerabilities. However, with the globalization of integrated circuit design and the continuous upgrading of security requirements, the SVA development model has exposed major...

Efficient Private Inference Based on Helper-Assisted Malicious Security Dishonest Majority MPC

Private inference based on Secure Multi-Party Computation MPC addresses data privacy risks in Machine Learning as a Service MLaaS. However, existing MPC-based private inference frameworks focuses on semi-honest or honest majority models, whose threat models are overly idealistic, while malicious...