AutoPentest-DRL

AutoPentest-DRL: Pruebas de Penetración Automatizadas Usando Aprendizaje por Refuerzo Profundo AutoPentest-DRL es un marco de pruebas de penetración automatizadas basado en técnicas de Aprendizaje por Refuerzo Profundo DRL. AutoPentest-DRL puede determinar la ruta de ataque más apropiada para una...

Pesidious

使用深度强化学习和GAN进行恶意软件变异 该工具的目的是利用人工智能对恶意软件（仅限PE32格式）样本进行变异，以绕过基于AI的分类器，同时保持其原有功能。过去，该领域已有显著的研究成果，研究人员分别采用强化学习或生成对抗网络作为首选武器来修改恶意软件可执行文件的状态，以欺骗杀毒引擎。我们的解决方案结合了深度强化学习和GAN，以克服单独使用这些方法时面临的一些局限性，如下所示。 .png 完整的工具文档请参见此处 安装说明 ⚠️ 由于本工具处理恶意软件文件，强烈建议在虚拟机中使用。安装工具后，请确保断开网络连接。 以下步骤将指导您完成所有环境安装。 1. 安装并设置Python 3.6。...

PISmith

PISmith: Red Teaming basado en Aprendizaje por Refuerzo para Defensas contra Inyección de PromptsCOLM 2026 Esta es una implementación oficial de PISmith: Red Teaming basado en Aprendizaje por Refuerzo para Defensas contra Inyección de Prompts Configuración del Entorno PISmith ha sido probado con...

GuardReasoner-VL

GuardReasoner-VL: Salvaguardando los VLM mediante razonamiento reforzado Yue Liu, Shengfang Zhai, Mingzhe Du Yulin Chen, Tri Cao, Hongcheng Gao, Cheng Wang Xinfeng Li, Kun Wang, Junfeng Fang, Jiaheng Zhang, Bryan Hooi 1National University of Singapore, 2Nanyang Technological University Para mejor...

StealthRL

StealthRL: Ataques de Paráfrasis mediante Aprendizaje por Refuerzo para la Evasión Multi-Detector de Detectores de Texto IA Artículo arXiv Demo Modelo Hugging Face Dataset de Referencia Hugging Face Resumen Los detectores de texto IA se utilizan cada vez más en entornos de alto riesgo, pero su...

CyberBattleSim

CyberBattleSim 2021年4月8日：请参阅 Microsoft 安全博客上的公告。 CyberBattleSim 是一个实验研究平台，用于研究在模拟的抽象企业网络环境中运行的自动化智能体的交互行为。该模拟提供了计算机网络和网络安全概念的高层抽象。其基于 Python 的 OpenAI Gym 接口支持使用强化学习算法训练自动化智能体。...

OpenAI Pauses Frontier RL Training as It Tightens Defenses Against Unsafe AI Behavior

OpenAI on Tuesday revealed that it paused reinforcement learning RL training for its latest artificial intelligence AI models for two weeks while it shored up additional defenses and increased the scope of its monitoring to avert another Hugging Face-like incident. "As models become more capable,...

ARTA: Adaptive Reinforcement-Learning-Based Throttling Agent for RowHammer Vulnerabilities

RowHammer vulnerability continues to intensify with DRAM scaling, reducing the activation threshold needed to induce bitflips and rendering existing defenses such as TRR, ECC, and refresh-based mechanisms vulnerable to sophisticated multi-bank hammering patterns. This work presents ARTA, a...

A Red Teaming Framework for Evaluating Robustness of AI-Enabled Security Orchestration, Automation, and Response Systems

AI-enabled Security Orchestration, Automation, and Response SOAR systems increasingly employ autonomous agents for cyber defense, yet their resilience to adaptive adversaries is underexplored. We introduce an autonomous red teaming framework that integrates large language models LLMs with...

Operationalizing Cybersecurity Governance for Mitigation Planning with Attack-Path Modeling and Reinforcement Learning

We address a fundamental challenge in cybersecurity operations of translating governance frameworks into actionable mitigation decisions under realistic resource constraints. Frameworks such as the NIST Cybersecurity Framework CSF provide widely adopted measures of organizational maturity, but do...

STARE: Step-Wise Temporal Alignment and Red-Teaming Engine for Multi-Modal Toxicity Attack

Red-teaming Vision-Language Models is essential for identifying vulnerabilities where adversarial image-text inputs trigger toxic outputs. Existing approaches treat image generation as a black box, returning only terminal toxicity scores and leaving open the question of when and how toxic semanti...

XekRung Technical Report

We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we develop diverse data synthesis pipelines tailored to the cybersecurity domain, enabling the scalable construction of high-quality training data and...

Risk Models As Mediating Artifacts: A Postphenomenological Analysis of the CIIM Framework in Cybersecurity Practice

This article applies postphenomenological theory to the field of cybersecurity risk management, arguing that formal risk models function as mediating artifacts that shape how security practitioners or analysts perceive, interpret, and act on threats. Based on Don Ihde's taxonomy on human-technolo...

VeRL 权限许可和访问控制问题漏洞

VeRL is an open-source reinforcement learning framework developed by ByteDance, aimed at optimizing large model training and inference processes. Versions of VeRL prior to 0.7.0 contained vulnerabilities related to permission licensing and access control. These vulnerabilities stemmed from a...

Adaptive Instruction Composition for Automated LLM Red-Teaming

Many approaches to LLM red-teaming leverage an attacker LLM to discover jailbreaks against a target. Several of them task the attacker with identifying effective strategies through trial and error, resulting in a semantically limited range of successes. Another approach discovers diverse attacks ...

TL-RL-FusionNet: An Adaptive and Efficient Reinforcement Learning-Driven Transfer Learning Framework for Detecting Evolving Ransomware Threats

Modern ransomware exhibits polymorphic and evasive behaviors by frequently modifying execution patterns to evade detection. This dynamic nature disrupts feature spaces and limits the effectiveness of static or predefined models. To address this challenge, we propose TL-RL-FusionNet, a reinforceme...

ARES: Adaptive Red-Teaming and End-To-End Repair of Policy-Reward System

Reinforcement Learning from Human Feedback RLHF is central to aligning Large Language Models LLMs, yet it introduces a critical vulnerability: an imperfect Reward Model RM can become a single point of failure when it fails to penalize unsafe behaviors. While existing red-teaming approaches...

Privacy-Aware Machine Unlearning with SISA for Reinforcement Learning-Based Ransomware Detection

Ransomware detection systems increasingly rely on behavior-based machine learning to address evolving attack strategies. However, emerging privacy compliance, data governance, and responsible AI deployment demand not only accurate detection but also the ability to efficiently remove the influence...

CSLE: A Reinforcement Learning Platform for Autonomous Security Management

Reinforcement learning is a promising approach to autonomous and adaptive security management in networked systems. However, current reinforcement learning solutions for security management are mostly limited to simulation environments and it is unclear how they generalize to operational systems...

Beyond Static Sandboxing: Learned Capability Governance for Autonomous AI Agents

Autonomous AI agents built on open-source runtimes such as OpenClaw expose every available tool to every session by default, regardless of the task. A summarization task receives the same shell execution, subagent spawning, and credential access capabilities as a code deployment task, a 15x...