redexer

redexer Redexer は Android アプリのバイナリを操作するリエンジニアリングツールです。 このツールは DEX ファイルをメモリ上のデータ構造に解析したり、 アプリが特定のパーミッションをどのパラメータで使用しているかを推論したり （この機能を RefineDroid と呼びます）、そのデータ構造を変更して 再出力し DEX ファイルを生成したり（この機能を Dr. Android、 すなわち Android 向け Dalvik 書き換えと呼びます）することができます。 出版物 Dr. Android and Mr. Hide: Fine-grained...

AutoRAN-public

🧠 AutoRAN：大型推理模型中安全推理的自动化劫持 AutoRAN 是一种自动化的安全推理劫持方法，利用对齐程度较低（次要）的辅助模型模拟推理轨迹、生成叙事性提示，并迭代优化这些提示，以绕过现代大型推理模型（LRMs）中的安全推理。 ⚠️ 免责声明 ：本仓库仅用于受控安全研究和 AI 安全红队测试。 🔍 核心特性 ⚙️ 自动化多轮越狱 ：通过迭代提示优化实现 🧩 叙事模板 ：在看似合理的教育/道德幌子下包装恶意目标 🔁 优化策略 ：利用中间推理轨迹演化提示 📈 在商业 LRM 上近乎 100% 的攻击成功率 🔬 在 AdvBench 、HarmBench 和 StrongReject...

qlcoder

QLCoder 用于合成 CodeQL 查询的智能体框架 目录 概述 安装 Docker（推荐） 原生安装（Linux） 使用方法 快速开始 开发工具 示例 论文环境 贡献 团队 引用 关联项目 概述 QLCoder 是一个利用 LLM 合成端到端 CodeQL 查询以进行漏洞检测的框架。给定现有 CVE 的元数据、LLM 和编码智能体，QLCoder 会迭代地合成一个 CodeQL 查询来检测该现有 CVE。初始查询是一个由提取的 diff AST 填充的 CodeQL 路径查询模板。在合成查询的过程中，编码智能体可以访问与 RAG 数据库和 CodeQL...

Unity Linux 20.1050a Security Update: kernel (UTSA-2026-098777)

The Unity Linux 20 host has a package installed that is affected by a vulnerability as referenced in the UTSA-2026-098777 advisory. In the Linux kernel, the following vulnerability has been resolved: bpf: Forget ranges when refining tnum after JSET Syzbot reported a kernel warning due to a range...

XSSaudit

XSSAudit v2.0 — Advanced XSS Vulnerability Scanner For au...

UNAD+: An Explainable Hybrid Framework for Unknown Network Attack Detection

The detection of previously unseen network attacks remains a major challenge for intrusion detection systems. Although supervised learning methods often perform well on known attack classes, they are limited when new attack types are not represented in the training data. Unsupervised methods are...

A Large Language Model Approach to Generating Bypass Rules for Malware Evasion in Analysis Sandbox

Sandbox evasion remains a critical challenge for automated malware analysis, as modern malware employs environment checks to detect analysis platforms and suppress malicious behavior. Existing approaches rely on manually crafted bypass rules that require deep reverse engineering of each evasion...

Babel: Jailbreaking Safety Attention Via Obfuscation Distribution Optimized Sampling

Despite rigorous safety alignment, Large Language Models LLMs remain vulnerable to jailbreak attacks. Existing black-box methods often rely on heuristic templates or exhaustive trials, lacking mechanistic interpretability and query efficiency. In this study, we investigate an intrinsic...

MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair

Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities, increasing the need for automated repair techniques that can operate reliably at repository scale. Although Large Language Model LLM-based agents have recently shown promise for automated vulnerability repair...

FuzzAgent: Multi-Agent System for Evolutionary Library Fuzzing

Library fuzzing is essential for hardening the software supply chain, but adopting it at scale remains expensive. Practitioners still spend substantial effort on environment setup, struggle to generate harnesses that respect intricate API constraints, and lack reliable means to tell genuine libra...

MT-JailBench: A Modular Benchmark for Understanding Multi-Turn Jailbreak Attacks

Multi-turn jailbreaks exploit the ability of large language models to accumulate and act on conversational context. Instead of stating a harmful request directly, an attacker can gradually steer the conversation toward an unsafe answer. Recent methods demonstrate this risk, but they are usually...

SkillAttack: Automated Red Teaming of Agent Skills through Attack Path Refinement

LLM-based agent systems increasingly rely on agent skills sourced from open registries to extend their capabilities, yet the openness of such ecosystems makes skills difficult to thoroughly vet. Existing attacks rely on injecting malicious instructions into skills, making them easily detectable b...

Threat actor abuse of AI accelerates from tool to cyberattack surface

For the last year, one word has represented the conversation living at the intersection of AI and cybersecurity: speed. Speed matters, but it’s not the most important shift we are observing across the threat landscape today. Now, threat actors from nation states to cybercrime groups are embedding...

AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applications

Large-scale web applications are widely deployed with complex third-party components, inheriting security risks arising from component vulnerabilities. Security assessment is therefore required to determine whether such known vulnerabilities remain practically exploitable in real applications...

Red-MIRROR: Agentic LLM-Based Autonomous Penetration Testing with Reflective Verification and Knowledge-Augmented Interaction

Web applications remain the dominant attack surface in cybersecurity, where vulnerabilities such as SQL injection, XSS, and business logic flaws continue to cause significant data breaches. While penetration testing is effective for identifying these weaknesses, traditional manual approaches are...

SUSE-SU-2026:20720-1 Security update for the Linux Kernel

The SUSE Linux Enterprise Micro 6.0 and Micro 6.1 kernel was updated to fix various security issues The following security issues were fixed: - CVE-2023-53817: crypto: lib/mpi - avoid null pointer deref in mpicmpui bsc1254992. - CVE-2025-37861: scsi: mpi3mr: Synchronous access b/w reset and tm...

Robust Provably Secure Image Steganography Via Latent Iterative Optimization

We propose a robust and provably secure image steganography framework based on latent-space iterative optimization. Within this framework, the receiver treats the transmitted image as a fixed reference and iteratively refines a latent variable to minimize the reconstruction error, thereby improvi...

Coverage-Guided Multi-Agent Harness Generation for Java Library Fuzzing

Coverage-guided fuzzing has proven effective for software testing, but targeting library code requires specialized fuzz harnesses that translate fuzzer-generated inputs into valid API invocations. Manual harness creation is time-consuming and requires deep understanding of API semantics,...

SCAFFOLD-CEGIS: Preventing Latent Security Degradation in LLM-Driven Iterative Code Refinement

The application of large language models to code generation has evolved from one-shot generation to iterative refinement, yet the evolution of security throughout iteration remains insufficiently understood. Through comparative experiments on three mainstream LLMs, this paper reveals the iterativ...

CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions

The evaluation of Large Language Models LLMs for code generation relies heavily on the quality and robustness of test cases. However, existing benchmarks often lack coverage for subtle corner cases, allowing incorrect solutions to pass. To bridge this gap, we propose CodeHacker, an automated agen...