EUVD-2015-0945

Malware in sbrugna...

EUVD-2015-4543

Malware in sbrugna...

EUVD-2017-18708

Malware in sbrugna...

EUVD-2024-2491

Malicious code in bioql PyPI...

EUVD-2024-16116

Malicious code in bioql PyPI...

EUVD-2025-0237

Malicious code in bioql PyPI...

EUVD-2022-25998

Malicious code in bioql PyPI...

EUVD-2024-52233

Malicious code in bioql PyPI...

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning

Today's cyber defenders are overwhelmed by a deluge of security alerts, threat intelligence signals, and shifting business context, creating an urgent need for AI systems to enhance operational security work. While Large Language Models LLMs have the potential to automate and scale Security...

ChatGPT solves CAPTCHAs if you tell it they’re fake

If you’re seeing fewer or different CAPTCHA puzzles in the near future, that’s not because website owners have agreed that they’re annoying, but it might be because they no longer prove that the visitor is human. For those that forgot what CAPTCHA stands for: Completely Automated Public Turing te...

charlotte

This is a C++ shellcode launcher, fully undetected as of May 13th, 2021. It dynamically invokes Windows API functions, XOR encrypts shellcode and function names, and uses random XOR keys and variables per run. The code is designed to be stealthy and evade detection. The code is written in C++ and...

Evaluating Diverse Feature Extraction Techniques of Multifaceted IoT Malware Analysis: a Survey

As IoT devices continue to proliferate, their reliability is increasingly constrained by security concerns. In response, researchers have developed diverse malware analysis techniques to detect and classify IoT malware. These techniques typically rely on extracting features at different levels fr...

MalLoc: toward Fine-Grained Android Malicious Payload Localization Via LLMs

The rapid evolution of Android malware poses significant challenges to the maintenance and security of mobile applications apps. Traditional detection techniques often struggle to keep pace with emerging malware variants that employ advanced tactics such as code obfuscation and dynamic behavior...

A Novel Study on Intelligent Methods and Explainable AI for Dynamic Malware Analysis

Deep learning models are one of the security strategies, trained on extensive datasets, and play a critical role in detecting and responding to these threats by recognizing complex patterns in malicious code. However, the opaque nature of these models-often described as "black boxes"-makes their...

Symbolic Execution in Practice: a Survey of Applications in Vulnerability, Malware, Firmware, and Protocol Analysis

Symbolic execution is a powerful program analysis technique that allows for the systematic exploration of all program paths. Path explosion, where the number of states to track becomes unwieldy, is one of the biggest challenges hindering symbolic execution's practical application. To combat this,...

CISA Releases Malware Analysis Report Associated with Microsoft SharePoint Vulnerabilities

CISA published a Malware Analysis Report MAR with analysis and associated detection signatures on files related to Microsoft SharePoint vulnerabilities: CVE-2025-49704link is external CWE-94: Code Injectionlink is external, CVE-2025-49706link is external CWE-287: Improper Authenticationlink is...

Thorium Platform Public Availability

Today, CISA, in partnership with Sandia National Laboratories, announced the public availability of Thoriumlink is external, a scalable and distributed platform for automated file analysis and result aggregation. Thorium enhances cybersecurity teams' capabilities by automating analysis workflows...

Using LLMs as a reverse engineering sidekick

This research explores how large language models LLMs can complement, rather than replace, the efforts of malware analysts in the complex field of reverse engineering. LLMs may serve as powerful assistants to streamline workflows, enhance efficiency, and provide actionable insights during malware...

MAL-2025-5835 Malicious code in @lensapp/eslint-config (npm)

--- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 61bc10b6edfec3225d467f169ee0c13a8d66637e96186f959196d0ae15822ad6 Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be...

Automagic Reverse Engineering

Automagic Reverse Engineering By Trellix · July 1, 2025 This blog was written by Max Kersten Over the last few years, I have looked into methods to improve the reverse engineering process. This saves essential time during the analysis, which helps while defending from well prepared threat actors...