CVE-2026-41096-Attack-Surface

CVE-2026-41096 Real World Attack Surface Attack surface in the real-world environment of CVE-2026-41096 Links Detailed analytical article Author: Two Seven One Three...

clusterfuzz

ClusterFuzz ClusterFuzz is a scalable fuzzing infrastructure that finds security and stability issues in software. Google uses ClusterFuzz to fuzz all Google products and as the fuzzing backend for OSS-Fuzz. ClusterFuzz provides many features which help seamlessly integrate fuzzing into a softwar...

pantagrule

Pantagrule gargantuan hashcat rulesets generated from compromised passwords Project maintenance warning : This project is deemed completed. No pull requests or changes will be made to this project in the future unless they are actual bugs or migrations to allow these rules to work with newer...

exploitgym

ExploitGym ExploitGym 是一个大规模、真实的基准测试，基于用户态程序、Google V8 引擎以及 Linux 内核中的真实安全漏洞构建，旨在评估 AI 代理开发漏洞利用的能力。 快速开始 root@kitploit: 1. Python 依赖 uv sync --extra proxy 2. 构建运行时构件（gdb、socat、nc、node + 代理 CLI）并 提取任务数据 bash scripts/setup/setupdata.sh 3. 验证安装 bash scripts/setup/validate.sh 4. 拉取 Firewall Squid 镜像...

fuzzbench

FuzzBench: ファザーベンチマークのサービス FuzzBenchは、実際のベンチマークの多種多様なセットに対してファザーを評価する無料サービスであり、Google規模で動作します。FuzzBenchの目標は、ファジング研究を厳密に評価し、コミュニティがその成果を採用しやすくすることです。研究コミュニティのメンバーがファザーを提供し、評価手法の改善に関するフィードバックをいただくことを歓迎します。 FuzzBenchが提供するもの： ファザーを統合するための簡単なAPI。...

CVE-2019-6447-in-Mobile-Application

CVE-2019-6447-在移动应用中的漏洞 在这份研究报告中，你可以完整了解关于CVE-2019-6447在实际应用程序中的概念以及利用示例。 如果你想创建漏洞利用视频，请使用下面提到的链接： https://drive.google.com/file/d/1ccj7ZH9kYFlIscTZ02ImA7b2bLCiFzZ6/view?usp=sharelink...

Piggybacking on Perception: Stealthy Concurrent Audio Prompt Injections against Multimodal LLM Agents

Large Language Model LLM-driven multimodal agents are increasingly deployed to execute autonomous tasks via continuous audio interaction. While this paradigm enhances interaction naturalness, it introduces a critical yet under-explored attack surface, as audio inputs inevitably contain...

CVE-2026-43499: A Critical Analysis of the "Zero-Self" Fallacy

This document presents a comprehensive technical analysis refuting the claim that CVE-2026-43499 GhostLock constitutes "no real vulnerability" due to its requirement for local access and user interaction. Through rigorous examination of the attack surface, exploitation vectors, and real-world...

EternalBlue-MS17-010-Vulnerability

EternalBlue MS17-010 Vulnerability Analysis This repository...

MacBook Neo vs Windows Laptops for Cybersecurity Tasks

There is no one-size-fits-all cybersecurity laptop. We’ll examine real-world work scenarios, tool compatibility, and trade-offs that impact a security professional’s day-to-day work...

Nuclei 3.9.0

Nuclei is a modern, high-performance vulnerability scanner that leverages simple YAML-based templates. It empowers you to design custom vulnerability detection scenarios that mimic real-world conditions, leading to zero false positives...

The Chronicles of Radio Frequency Fingerprinting

Radio Frequency Fingerprinting RFF has evolved from an early idea for radar emitter identification into a broad research field for wireless device identification and spectrum monitoring for security. Rather than presenting a conventional literature survey, this work provides a critical historical...

ExploitGym AI Exploit Benchmark Tool

ExploitGym is a large-scale, realistic benchmark built from real-world vulnerabilities designed to evaluate AI agents' ability to develop exploits...

CyberGym-E2E: Scalable Real-World Benchmark for AI Agents' End-To-End Cybersecurity Capabilities

AI has the potential to transform cybersecurity by enabling systems that can autonomously detect, analyze, and remediate software vulnerabilities. However, existing cybersecurity evaluations of AI systems are limited in scale or scope, and fail to capture the end-to-end lifecycle of real-world...

The Role of Domain-Specific Features in Malware Detection: A MacOS Case Study

Despite the growing popularity of macOS among end users and enterprise systems, malware research has primarily focused on Windows and Android operating systems, leaving the problem of macOS malware detection relatively unexplored. Indeed, the specificity of the operating system and the unique...

Less panic patching, more precision

Welcome to this week's edition of the Threat Source newsletter. Recently, Martin closed his introduction with a warning: Ready or not, the time of much patching is coming. I've been chewing on that one for a while because I'm rethinking my own enrichment pipelines along these lines, and the...

Towards Demystifying and Repairing LLM-In-The-Loop Vulnerabilities

Large Language ModelsLLMs have been actively integrated into modern software systems as critical components. LLM-in-the-loop vulnerabilities, where vulnerabilities are introduced by LLMs and their dependent downstream components, such as frameworks, introduce new risks. Although some benchmark...

Measuring Real-World Prompt Injection Attacks in LLM-Based Resume Screening

LLMs are vulnerable to prompt injection attacks. However, this vulnerability has been primarily demonstrated conceptually in academic studies or through a few anecdotal case studies. Its prevalence and impact in real-world LLM-based applications are largely unexplored. In this work, we present th...

VIPER-MCP: Detecting and Exploiting Taint-Style Vulnerabilities in Model Context Protocol Servers

Model Context Protocol MCP has emerged as a standard interface for connecting LLM agents to external tools. Because MCP servers expose privileged operations such as shell execution, network access, and file-system manipulation to agent-driven invocation, implementation flaws in tool handlers can...

Agent Security Is a Systems Problem

We take the position that agent security must be approached as a systems problem: the AI model powering the agent must be treated as an untrusted component, and security invariants must be enforced at the system level. Through this lens, efforts to increase model robustness the dominant viewpoint...