Mopri - an Analysis Framework for Unveiling Privacy Violations in Mobile Apps

Everyday services of society increasingly rely on mobile applications, resulting in a conflicting situation between the possibility of participation on the one side and user privacy and digital freedom on the other. In order to protect users' rights to informational self-determination, regulatory...

Reading between the Code Lines: On the Use of Self-Admitted Technical Debt for Security Analysis

Static Analysis Tools SATs are central to security engineering activities, as they enable early identification of code weaknesses without requiring execution. However, their effectiveness is often limited by high false-positive rates and incomplete coverage of vulnerability classes. At the same...

Toxic_Flow_Analysis_Framework_For_Agentic_AI

Toxic Flow Analysis TFA Framework A Secure-by-Design framew...

SUSE CVE-2026-23026

In the Linux kernel, the following vulnerability has been resolved: dmaengine: qcom: gpi: Fix memory leak in gpiperipheralconfig Fix a memory leak in gpiperipheralconfig where the original memory pointed to by gchan-config could be lost if krealloc fails. The issue occurs when: 1. gchan-config...

Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filtering

Static Application Security Testing SAST tools are essential for identifying software vulnerabilities, but they often produce a high volume of false positives FPs, imposing a substantial manual triage burden on developers. Recent advances in Large Language Model LLM agents offer a promising...

Cross-site Scripting (XSS)

Overview mobsf is a Mobile Security Framework MobSF is an automated, all-in-one mobile application Android/iOS/Windows pen-testing, malware analysis and security assessment framework capable of performing static and dynamic analysis. Affected versions of this package are vulnerable to Cross-site...

AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection

Secure code review is critical at the pre-commit stage, where vulnerabilities must be caught early under tight latency and limited-context constraints. Existing SAST-based checks are noisy and often miss immature, context-dependent vulnerabilities, while standalone Large Language Models LLMs are...

Azure Linux 3.0 Security Update: kernel (CVE-2025-21736)

The version of kernel installed on the remote Azure Linux 3.0 host is prior to tested version. It is, therefore, affected by a vulnerability as referenced in the CVE-2025-21736 advisory. - In the Linux kernel, the following vulnerability has been resolved: nilfs2: fix possible int overflows in...

Azure Linux 3.0 Security Update: kernel (CVE-2025-37755)

The version of kernel installed on the remote Azure Linux 3.0 host is prior to tested version. It is, therefore, affected by a vulnerability as referenced in the CVE-2025-37755 advisory. - In the Linux kernel, the following vulnerability has been resolved: net: libwx: handle pagepooldevallocpages...

Detection Bypass

fickling is vulnerable to detection bypass. The vulnerability is due to improper handling and analysis of Python builtins during pickle decompilation, which allows an attacker to bypass detection mechanisms and evade static analysis of malicious pickle payloads...

An Empirical Study on Remote Code Execution in Machine Learning Model Hosting Ecosystems

Model-sharing platforms, such as Hugging Face, ModelScope, and OpenCSG, have become central to modern machine learning development, enabling developers to share, load, and fine-tune pre-trained models with minimal effort. However, the flexibility of these ecosystems introduces a critical security...

Static Detection of Core Structures in Tigress Virtualization-Based Obfuscation Using an LLVM Pass

Malware often uses obfuscation to hinder security analysis. Among these techniques, virtualization-based obfuscation is particularly strong because it protects programs by translating original instructions into attacker-defined virtual machine VM bytecode, producing long and complex code that is...

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...

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,...

CVE-2025-68820

In the Linux kernel, the following vulnerability has been resolved: ext4: xattr: fix null pointer deref in ext4rawinode If ext4getinodeloc fails e.g. if it returns -EFSCORRUPTED, iloc.bh will remain set to NULL. Since ext4xattrinodedecrefall lacks error checking, this will lead to a null pointer...

CVE-2026-22609 Fickling has Static Analysis Bypass via Incomplete Dangerous Module Blocklist

Fickling is a Python pickling decompiler and static analyzer. Prior to version 0.1.7, the unsafeimports method in Fickling's static analyzer fails to flag several high-risk Python modules that can be used for arbitrary code execution. Malicious pickles importing these modules will not be detected...

CVE-2026-22609 Fickling has Static Analysis Bypass via Incomplete Dangerous Module Blocklist

Fickling is a Python pickling decompiler and static analyzer. Prior to version 0.1.7, the unsafeimports method in Fickling's static analyzer fails to flag several high-risk Python modules that can be used for arbitrary code execution. Malicious pickles importing these modules will not be detected...

EUVD-2026-1685

Fickling is a Python pickling decompiler and static analyzer. Prior to version 0.1.7, the unsafeimports method in Fickling's static analyzer fails to flag several high-risk Python modules that can be used for arbitrary code execution. Malicious pickles importing these modules will not be detected...

Realworld-for-Application_FUGIO_FirstFrameworkFuzzingDetectPOI

FUGIO Production Guide Introduction FUGIO is the firs...

AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections

The rise in frequency and complexity of malware attacks are viewed as a major threat to modern digital infrastructure, which means that traditional signature-based detection methods are becoming less effective. As cyber threats continue to evolve, there is a growing need for intelligent systems t...