secretflow

简体中文｜English SecretFlow是一个统一的框架，用于保护隐私的数据智能和机器学习。为了实现这个目标，它提供了以下内容： 抽象设备层，包括明文设备和封装了各种密态协议的密态设备。 设备流层，将高阶算法转为设备对象流和DAG。 算法层，使用水平或垂直分区的数据进行数据分析和机器学习。 工作流层，无缝集成数据处理、模型训练和超参调整。 文档 SecretFlow 快速开始 用户指南 API文档 教程 相关项目 Kuscia: 一款基于 K3s 的轻量级隐私计算任务编排框架。 SCQL: 允许多个不信任方在不泄露其私人数据的情况下进行联合分析的系统。 SPU:...

secretflow

简体中文｜English SecretFlow is a unified framework for privacy-preserving data intelligence and machine learning. To achieve this goal, it provides: An abstract device layer consists of plain devices and secret devices which encapsulate various cryptographic protocols. A device flow layer modeling...

Dark-Moon

DarkMoon La plataforma de pentesting con IA de código abierto que ejecuta un pentest completo por sí sola y nunca filtra tus datos Apunta DarkMoon a un objetivo autorizado. 50 agentes de IA especializados razonan, encadenan exploits reales a través de web, nube, Active Directory y Kubernetes, y...

Mandating-Honeypots-Project

Mandating-Honeypots-Project A medida que leyes como la Parents Decide Act H.R.8250 y la Digital Age Assurance Act de California Assembly Bill 1043 avanzan por los órganos legislativos, la privacidad, el anonimato y la seguridad de los datos se cargan de amenazas sistémicas y de una nueva...

CVE-2026-54533

vantage6 is an open-source infrastructure for privacy preserving analysis. Prior to version 5.0.0, malicious algorithms can potentially access other algorithms input and output files. Version 5.0.0 fixes the issue. As a workaround, verify and restrict the algorithm containers that are allowed to...

CVE-2024-27928

vantage6 is an open-source infrastructure for privacy preserving analysis. Prior to version 5.0.0, if an attacker hacks into a vantage6 user's email account, they can 1 reset the password via email and then 2 reset the 2FA token via email. This way they reduce 2FA to 1FA email access. Note that...

CVE-2026-54533

vantage6 node (open-source infrastructure for privacy-preserving analysis) contains an Improper Access Control vulnerability prior to version 5.0.0 that could allow malicious algorithms to access other algorithms’ input and output files. Version 5.0.0 fixes the issue. As a workaround, verify and ...

EUVD-2024-55640

vantage6 is an open-source infrastructure for privacy preserving analysis. Prior to version 5.0.0, users can reset their MFA token via API routes that send them an email. Currently the number of emails that is sent is not limited. This gives attackers the option to flood someones mailbox with a l...

PT-2026-50570

Name of the Vulnerable Software and Affected Versions vantage6 versions prior to 5.0.0 Description Malicious algorithms can potentially access input and output files belonging to other algorithms. Recommendations Update to version 5.0.0. As a temporary workaround, verify and restrict the algorith...

CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering

Large language models LLMs are increasingly applied to cybersecurity question answering QA for critical tasks such as incident response and vulnerability analysis. However, real-world operational contexts, including system logs and network configurations, inherently contain sensitive identifiers,...

XAI FL-IDS: A Federated Learning and SHAP-Based Explainable Framework for Distributed Intrusion Detection Systems

An Intrusion Detection System IDS is vital in cybersecurity, detecting unauthorized activity across networks. With attacks on network layers increasing, stronger IDSs are needed. Yet most IDSs rely on centralized detection, forcing IoT nodes to ship data to a server, adding overhead and offering ...

AoI-Guided Client Selection for Robust and Timely Federated Intrusion Detection in Cloud-Edge Security Analytics

Federated learning FL is attractive for cloud-edge intrusion detection because it enables collaborative training over distributed telemetry without centralizing raw logs. In production security analytics pipelines, however, only a subset of clients participates in each round, and heterogeneous...

Analyzing Unsolicited Internet Traffic: Measuring IoT Security Threats Via Network Telescopes

Network telescopes serve as a critical passive monitoring tool for capturing unsolicited Internet traffic, providing insights into global scanning and reconnaissance behavior. This study analyzes a 10-day dataset during January 2025 consisting of approximately 22 million packets collected by the...

QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection

Modern computing systems inherently trust human input devices, creating an exploitable attack surface for adversarial automation. USB Human Interface Device HID emulation attacks, such as those enabled by the USB Rubber Ducky, exploit this assumption to inject arbitrary keystroke sequences while...

Towards Automated Pentesting with Large Language Models

Large Language Models LLMs are redefining offensive cybersecurity by allowing the generation of harmful machine code with minimal human intervention. While attackers take advantage of dark LLMs such as XXXGPT and WolfGPT to produce malicious code, ethical hackers can follow similar approaches to...

Malware and Ransomware Detection in M365

Availability Requirement Threat Detection is available to Veeam Data Cloud for Microsoft 365 customers with Premium or Advanced plans. Customers must opt in to AI settings to enable this feature. Contact your Veeam account team or see your plan details to confirm availability. Supported Workloads...

Security Awareness in LLM Agents: The NDAI Zone Case

NDAI zones let inventor and investor agents negotiate inside a Trusted Execution Environment TEE where any disclosed information is deleted if no deal is reached. This makes full IP disclosure the rational strategy for the inventor's agent. Leveraging this infrastructure, however, requires agents...

CLIOPATRA: Extracting Private Information from LLM Insights

As AI assistants become widely used, privacy-aware platforms like Anthropic's Clio have been introduced to generate insights from real-world AI use. Clio's privacy protections rely on layering multiple heuristic techniques together, including PII redaction, clustering, filtering, and LLM-based...

RobPI: Robust Private Inference against Malicious Client

The increased deployment of machine learning inference in various applications has sparked privacy concerns. In response, private inference PI protocols have been created to allow parties to perform inference without revealing their sensitive data. Despite recent advances in the efficiency of PI,...

SecureSplit: Mitigating Backdoor Attacks in Split Learning

Split Learning SL offers a framework for collaborative model training that respects data privacy by allowing participants to share the same dataset while maintaining distinct feature sets. However, SL is susceptible to backdoor attacks, in which malicious clients subtly alter their embeddings to...