CrowdStrike Expands Real-Time Cloud Detection and Response to Google Cloud
CrowdStrike Expands Real-Time Cloud Detection and Response to Google Cloud
2026-06-01 归档
今日共收录 782 条安全情报，包含 65 个 CVE，382 篇安全通告，以及 87 篇研究论文。
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CrowdStrike Falcon Cloud Security Delivered 264% ROI Through Unified Cloud Protection
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Threat IntelligenceNorth Korea-Nexus Threat Actor Compromises Widely Used Axios NPM Package in Supply Chain AttackBy Google Threat Intelligence Group • 16-minute read
Threat IntelligenceNorth Korea-Nexus Threat Actor Compromises Widely Used Axios NPM Package in Supply Chain AttackBy Goo
Google Cloud Products
Google Cloud Products
Cloud & Application Security
Cloud & Application Security
From Scanner to Stealer: Inside the trivy-action Supply Chain Compromise
From Scanner to Stealer: Inside the trivy-action Supply Chain Compromise
Next-Gen Identity Security
Next-Gen Identity Security
CrowdStrike FalconID Brings Phishing-Resistant MFA to Falcon Next-Gen Identity Security
CrowdStrike FalconID Brings Phishing-Resistant MFA to Falcon Next-Gen Identity Security
CrowdStrike Named a Leader in Identity Threat Detection and Response
CrowdStrike Named a Leader in Identity Threat Detection and Response
Security Analysis of Wearable Smart Health Devices and Their Companion Apps.
本文对可穿戴智能健康设备（WSHD）及其配套应用程序的安全性进行了系统分析。研究背景是WSHD近年来快速发展，但安全风险同步增长，尤其是用户个人健康信息的泄露问题。核心问题是WSHD设备、配套应用及通信渠道中存在的漏洞如何导致隐私泄露。研究
EarArray: Defending against DolphinAttack via Acoustic Attenuation.
本文关注针对语音助手的 DolphinAttack（不可听语音命令攻击），该攻击将可听语音调制到超声波上，从而无声地注入恶意命令，例如控制智能门锁或音箱。由于攻击利用了超声波的人耳不可听特性，且不需要物理接触，传统方法难以防御。现有防御方案
Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice.
该论文针对苹果公司在其本地差分隐私（LDP）系统中使用的Count Mean Sketch（CMS）机制进行了实际的隐私保证量化研究。苹果的CMS用于从用户设备收集使用数据，其宣称的隐私保证基于ε=4的LDP模型。然而，作者指出该保证仅在单
AutoDA: Automated Decision-based Iterative Adversarial Attacks.
本文提出 AutoDA（Automated Decision-based Iterative Adversarial Attacks），一种自动化决策型迭代对抗攻击方法。传统决策型攻击需要人工设计查询策略和超参数，效率低且泛化性差。Auto
PrivacyFlash Pro: Automating Privacy Policy Generation for Mobile Apps.
该研究针对移动应用隐私政策生成不准确的问题，提出了一种结合静态代码分析的自动化方法。现有基于问卷的隐私政策生成器依赖于开发者正确回答隐私相关问题，但实际生成的策略往往无法反映应用的真正隐私实践。研究者设计并实现了PrivacyFlash P