CrowdStrike SafeMind: When the Best Offense Builds the Best DefenseSep 17, 2026
CrowdStrike SafeMind: When the Best Offense Builds the Best DefenseSep 17, 2026
今日共收录 1784 条安全情报,包含 814 个 CVE,581 篇安全通告,以及 100 篇研究论文。
← 返回归档列表CrowdStrike SafeMind: When the Best Offense Builds the Best DefenseSep 17, 2026
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The Pakistan-aligned threat group tracked as Transparent Tribe (aka APT36 and Earth Karkaddan) has been attributed to a
Cybersecurity researchers have discovered a cluster of 13 npm packages that have been found to deliver a previously undo
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Microsoft fixed vulnerabilities across Azure and AI-branded products, with privilege escalation flaws accounting for the
本文是一篇面向智能电网(Smart Grid)网络安全的综述性研究论文,作者来自加泰罗尼亚地区高校与研究机构,投稿分类为 eess.SY(系统工程)。文章的核心出发点在于强调信息技术(IT)与运营技术(OT)环境之间的关键差异:IT 侧关注
该论文聚焦 Model Context Protocol (MCP) 使能的大模型工具调用链路中的「隐式信任」问题。MCP 允许 LLM 调用外部工具,但每次工具交互都会通过多个输入通道(工具描述、工具执行结果、采样消息 sampling
背景与问题:近年来,恶意 npm 包检测工具开始借助 LLM 对源代码的语义理解能力,在大规模场景下识别包的恶意意图,并在 Shai-Hulud 等近期供应链攻击事件中体现出实用价值。然而,LLM 的上下文窗口有限,攻击者正是利用这一点进行
在按 token 计费的 LLM 服务中,输出越长用户付费越多,这为不诚信的服务提供方创造了经济动机:在不明显损害任务可用性的前提下,暗中操纵生成过程以膨胀输出 token 数量。论文将此类行为定义为“提供方侧 Token 膨胀攻击”(Pr
该论文研究消费级本地大模型(local LLM)部署中的提示词机密性问题。业界普遍认为在本机运行推理比云端更私密,因为提示词不离开设备;作者对此提出质疑,并论证「本地推理」本身并不足以保证提示词保密,还取决于服务软件在推理前、推理中和推理后
大型语言模型正被集成到恶意软件分诊流程中, 作为推理组件汇总静态证据并给出面向分析师的判定结论。本文指出, 这种推理能力本身会引入新的攻击面, 并提出 ALIBI——一种针对前沿 LLM 恶意软件分析器的“语义掩护故事”攻击。ALIBI 不
该论文是一篇以测量与基准为核心的实证研究,聚焦工具增强型 LLM Agent 的一个结构性盲区:模型会调用根本不存在的工具,或传入任何 schema 都未声明的参数,即"工具幻觉"。作者指出,现有的两类防御——工具选择(从候选中挑对工具)与