Pesidious

Mutazione del Malware usando Deep Reinforcement Learning e GAN Lo scopo dello strumento è utilizzare l'intelligenza artificiale per mutare un campione di malware solo PE32 al fine di eludere i classificatori basati sull'IA, mantenendo intatta la sua funzionalità. In passato, sono stati svolti...

AutoPentest-DRL

AutoPentest-DRL: Test di penetrazione automatizzati utilizzando l'apprendimento per rinforzo profondo AutoPentest-DRL è un framework di test di penetrazione automatizzato basato su tecniche di Apprendimento per Rinforzo Profondo DRL. AutoPentest-DRL è in grado di determinare il percorso di attacc...

hontel

HonTel HonTel is a Honeypot for Telnet service. Basically, it is a Python v2.x application emulating the service inside the chroot environment. Originally it has been designed to be run inside the Ubuntu/Debian environment, though it could be easily adapted to run inside any Linux environment...

StealthRL

StealthRL: Attacchi di Parafrasi basati su Apprendimento per Rinforzo per l'Evasione Multi-Detector dei Rilevatori di Testo AI Articolo arXiv Demo Modello Hugging Face Dataset di benchmark Hugging Face Abstract I rilevatori di testo AI sono sempre più utilizzati in contesti ad alto rischio, eppur...

GuardReasoner-VL

GuardReasoner-VL: Sicherung von VLMs durch verstärktes Reasoning 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 Um die Sicherheit...

PISmith

PISmith: Reinforcement Learning-basiertes Red Teaming für Prompt-Injection-AbwehrmechanismenCOLM 2026 Dies ist die offizielle Implementierung von PISmith: Reinforcement Learning-basiertes Red Teaming für Prompt-Injection-Abwehrmechanismen Umgebungseinrichtung PISmith wurde mit Python 3.10 und CUD...

CyberBattleSim

CyberBattleSim 8 aprile 2021: Vedi l'annuncio sul blog Microsoft Security. CyberBattleSim è una piattaforma di ricerca sperimentale per studiare l'interazione di agenti automatizzati che operano in un ambiente di rete aziendale simulato e astratto. La simulazione fornisce un'astrazione di alto...

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,...

CVE-2026-63232

A SQL injection and unsafe deserialisation vulnerability in Koollab LMS allowed an authenticated attacker to inject through the assessment reinforcement endpoint, control data passed to unserialize, write a webshell to a publicly accessible location, and execute arbitrary code on the server...

EUVD-2026-50127

A SQL injection and unsafe deserialisation vulnerability in Koollab LMS allowed an authenticated attacker to inject through the assessment reinforcement endpoint, control data passed to unserialize, write a webshell to a publicly accessible location, and execute arbitrary code on the server...

PT-2026-65703

A SQL injection and unsafe deserialisation vulnerability in Koollab LMS allowed an authenticated attacker to inject through the assessment reinforcement endpoint, control data passed to unserialize, write a webshell to a publicly accessible location, and execute arbitrary code on the server...

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...