angr 9.2.211

angr is an open-source binary analysis platform for Python. It combines both static and dynamic symbolic "concolic" analysis, providing tools to solve a variety of tasks...

InduGuard_vul_poc

ICS Vulnerability PoC Library — SCAA Benchmark Support Proof-...

Unity Linux 20.1050e / 20.1060e / 20.1070e Security Update: kernel (UTSA-2026-007471)

The Unity Linux 20 host has a package installed that is affected by a vulnerability as referenced in the UTSA-2026-007471 advisory. In the Linux kernel, the following vulnerability has been resolved: xen/privcmd: Fix a possible warning in privcmdioctlmmapresource As 'kdata.num' is user-controlled...

PySpector has a Plugin Code Execution Bypass via Incomplete Static Analysis in PluginSecurity.validate_plugin_code

Summary The plugin security validator in PySpector uses AST-based static analysis to prevent dangerous code from being loaded as plugins. The blocklist implemented in PluginSecurity.validateplugincode is incomplete and can be bypassed using several Python constructs that are not checked. An...

NFTDELTA: Detecting Permission Control Vulnerabilities in NFT Contracts through Multi-View Learning

Permission control vulnerabilities in Non-fungible token NFT contracts can result in significant financial losses, as attackers may exploit these weaknesses to gain unauthorized access or circumvent critical permission checks. In this paper, we propose NFTDELTA, a framework that leverages static...

RealVuln: Benchmarking Rule-Based, General-Purpose LLM, and Security-Specialized Scanners on Real-World Code

How do security scanners perform on real-world code? We present RealVuln, the first open-source benchmark comparing Rule-Based SAST, General-Purpose LLMs, and Security-Specialized scanners on 26 intentionally vulnerable Python repositories educational and Capture-The-Flag applications with 796...

AndroScanner: Automated Backend Vulnerability Detection for Android Applications

Mobile applications rely on complex backends that introduce significant security risks, yet developers often lack the tools to assess these risks effectively. This paper presents AndroScanner, an automated pipeline for detecting vulnerabilities in Android application backends through combined...

EUVD-2026-22245

Kiuwan SAST improperly authorizes SSO logins for locally disabled mapped user accounts, allowing disabled users to continue accessing the application. Kiuwan Cloud was affected, and Kiuwan SAST on-premise KOP was affected before 2.8.2509.4...

BayreuthWing

A transformer-based deep learning system for detecting securit...

RansomTrack: A Hybrid Behavioral Analysis Framework for Ransomware Detection

Ransomware poses a serious and fast-acting threat to critical systems, often encrypting files within seconds of execution. Research indicates that ransomware is the most reported cybercrime in terms of financial damage, highlighting the urgent need for early-stage detection before encryption is...

Broken Quantum: A Systematic Formal Verification Study of Security Vulnerabilities across the Open-Source Quantum Computing Simulator Ecosystem

Quantum computing simulators form the classical software foundation on which virtually all quantum algorithm research depends. We present Broken Quantum, the first comprehensive formal security audit of the open-source quantum computing simulator ecosystem. Applying COBALT QAI -- a four-module...

dst-engine

DST: Deterministic Security Testing Static analysis that does...

CritBench: A Framework for Evaluating Cybersecurity Capabilities of Large Language Models in IEC 61850 Digital Substation Environments

The advancement of Large Language Models LLMs has raised concerns regarding their dual-use potential in cybersecurity. Existing evaluation frameworks overwhelmingly focus on Information Technology IT environments, failing to capture the constraints, and specialized protocols of Operational...

Guiding Symbolic Execution with Static Analysis and LLMs for Vulnerability Discovery

Symbolic execution detects vulnerabilities with precision, but applying it to large codebases requires harnesses that set up symbolic state, model dependencies, and specify assertions. Writing these harnesses has traditionally been a manual process requiring expert knowledge, which significantly...

Argus: Reorchestrating Static Analysis Via a Multi-Agent Ensemble for Full-Chain Security Vulnerability Detection

Recent advancements in Large Language Models LLMs have sparked interest in their application to Static Application Security Testing SAST, primarily due to their superior contextual reasoning capabilities compared to traditional symbolic or rule-based methods. However, existing LLM-based approache...

A Multi-Agent Framework for Automated Exploit Generation with Constraint-Guided Comprehension and Reflection

Open-source libraries are widely used in modern software development, introducing significant security vulnerabilities. While static analysis tools can identify potential vulnerabilities at scale, they often generate overwhelming reports with high false positive rates. Automated Exploit Generatio...

Credential Leakage in LLM Agent Skills: A Large-Scale Empirical Study

Third-party skills extend LLM agents with powerful capabilities but often handle sensitive credentials in privileged environments, making leakage risks poorly understood. We present the first large-scale empirical study of this problem, analyzing 17,022 skills sampled from 170,226 on SkillsMP usi...

Quantum-Safe Code Auditing: LLM-Assisted Static Analysis and Quantum-Aware Risk Scoring for Post-Quantum Cryptography Migration

The impending arrival of cryptographically relevant quantum computers CRQCs threatens the security foundations of modern software: Shor's algorithm breaks RSA, ECDSA, ECDH, and Diffie-Hellman, while Grover's algorithm reduces the effective security of symmetric and hash-based schemes. Despite NIS...

An Empirical Comparison of Security and Privacy Characteristics of Android Messaging Apps

Mobile messaging apps are a fundamental communication infrastructure, used by billions of people every day to share information, including sensitive data. Security and Privacy are thus critical concerns for such applications. Although the cryptographic protocols prevalent in messaging apps are...

Debt behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild

AI coding assistants are now widely used in software development. Software developers increasingly integrate AI-generated code into their codebases to improve productivity. Prior studies have shown that AI-generated code may contain code quality issues under controlled settings. However, we still...