2026-06-22 归档

今日共收录 737 条安全情报,包含 75 个 CVE,376 篇安全通告,以及 39 篇研究论文。

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安全公告2026-06-22 发布INFO

MFSA 2026-52Security Vulnerabilities fixed in Firefox for iOS 151.1

MFSA 2026-52Security Vulnerabilities fixed in Firefox for iOS 151.1

历史摘要 · 未重新核验 · Mozilla Security Advisories

安全公告2026-06-22 发布INFO

MFSA 2026-53Security Vulnerabilities fixed in Firefox for iOS 151.2

MFSA 2026-53Security Vulnerabilities fixed in Firefox for iOS 151.2

历史摘要 · 未重新核验 · Mozilla Security Advisories

安全公告2026-06-22 发布INFO

MFSA 2026-55Security Vulnerabilities fixed in Focus for iOS / Klar 151.3.1

MFSA 2026-55Security Vulnerabilities fixed in Focus for iOS / Klar 151.3.1

历史摘要 · 未重新核验 · Mozilla Security Advisories

安全公告2026-06-22 发布INFO

MFSA 2026-56Security Vulnerabilities fixed in Firefox for iOS 152.0

MFSA 2026-56Security Vulnerabilities fixed in Firefox for iOS 152.0

历史摘要 · 未重新核验 · Mozilla Security Advisories

学术论文2026-06-18 发布INFO

AutoTam: Specifying Secure Protocol Implementations with Tamarin Model Generation

AutoTam: Specifying Secure Protocol Implementations with Tamarin Model Generation

历史摘要 · 未重新核验 · arXiv cs.CR

威胁情报2026-06-22 发布INFO

CrowdStrike Named a Leader in Frost & Sullivan 2026 Radar for Cloud-Native Application Protection Platforms

CrowdStrike Named a Leader in Frost & Sullivan 2026 Radar for Cloud-Native Application Protection Platforms

历史摘要 · 未重新核验 · CrowdStrike

威胁情报2026-06-22 发布INFO

CrowdStrike Expands Real-Time Cloud Detection and Response to Google Cloud

CrowdStrike Expands Real-Time Cloud Detection and Response to Google Cloud

历史摘要 · 未重新核验 · CrowdStrike

威胁情报2026-06-22 发布INFO

Cloud & Application Security

Cloud & Application Security

历史摘要 · 未重新核验 · CrowdStrike

威胁情报2026-06-22 发布INFO

From Scanner to Stealer: Inside the trivy-action Supply Chain Compromise

From Scanner to Stealer: Inside the trivy-action Supply Chain Compromise

历史摘要 · 未重新核验 · CrowdStrike

威胁情报2026-06-22 发布INFO

Next-Gen Identity Security

Next-Gen Identity Security

历史摘要 · 未重新核验 · CrowdStrike

威胁情报2026-06-22 发布INFO

CrowdStrike Named a Leader in Identity Threat Detection and Response

CrowdStrike Named a Leader in Identity Threat Detection and Response

历史摘要 · 未重新核验 · CrowdStrike

威胁情报2026-06-22 发布INFO

CrowdStrike Named an Innovation and Growth Leader in the 2026 Frost Radar™: Cloud and Application Runtime Security

CrowdStrike Named an Innovation and Growth Leader in the 2026 Frost Radar™: Cloud and Application Runtime Security

历史摘要 · 未重新核验 · CrowdStrike

威胁情报2026-06-22 发布INFO

CrowdStrike Expands Identity Leadership with OpenID and IDPro

CrowdStrike Expands Identity Leadership with OpenID and IDPro

历史摘要 · 未重新核验 · CrowdStrike

威胁情报2026-06-22 发布INFO

cloud architecture assessment

cloud architecture assessment

历史摘要 · 未重新核验 · Mandiant

威胁情报2026-06-22 发布INFO

ransomware and multifaceted extortion defense

ransomware and multifaceted extortion defense

历史摘要 · 未重新核验 · Mandiant

威胁情报2026-06-22 发布INFO

Google Cloud Next 2026Catch-up sessions on the keynotes and select sessions are now available on demand.Explore content on-demand

Google Cloud Next 2026Catch-up sessions on the keynotes and select sessions are now available on demand.Explore content

历史摘要 · 未重新核验 · Mandiant

学术论文2026-06-22 发布HIGH

SQUIP: Exploiting the Scheduler Queue Contention Side Channel.

该论文首次提出了一种针对现代超标量CPU中调度队列(Scheduler Queue)的侧信道攻击——SQUIP攻击。调度队列负责决定指令按何种顺序分配到各个执行单元,以往未被作为侧信道研究,因为Intel CPU仅有一个调度队列,其竞争现象

历史摘要 · 未重新核验 · IEEE S&P

学术论文2026-06-22 发布INFO

ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning.

该论文提出了一种名为ORL-AUDITOR的新型数据集审计机制,专门针对离线深度强化学习(offline DRL)场景。离线DRL利用预先收集的数据集训练模型,广泛应用于自动驾驶等安全关键领域。随着高质量数据集成为AI核心资产,许多机构公开

历史摘要 · 未重新核验 · NDSS

学术论文2026-06-22 发布MEDIUM

Extract: A PHP Foot-Gun Case Study.

该论文以PHP语言中的类型体系为研究对象,通过挖掘特定API(如extract函数)的潜在陷阱,系统分析了PHP类型系统在使用类似extract这类函数时可能引发的安全漏洞。作者通过构造一系列案例,展示了攻击者如何利用类型混淆与变量覆盖机制

历史摘要 · 未重新核验 · USENIX Security

学术论文2026-06-22 发布INFO

DeepRed: A Deep Learning-Powered Command and Control Framework for Multi-Stage Red Teaming Against ML-based Network Intrusion Detection Systems.

该论文提出了一种名为DeepRed的深度学习驱动的命令与控制(C2)框架,用于针对基于机器学习的网络入侵检测系统(ML-NIDS)进行多阶段红队测试。研究背景指出,尽管机器学习能够提升NIDS的检测能力,但对抗性机器学习研究揭示了ML模型的

历史摘要 · 未重新核验 · USENIX Security