Cyber Defense Summit 2026Register today to reserve your spot
Cyber Defense Summit 2026Register today to reserve your spot
今日共收录 754 条安全情报,包含 65 个 CVE,375 篇安全通告,以及 61 篇研究论文。
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Cyber Defense Summit 2026Crafted to equip elite security professionals with the strategies, tools, and insights needed t
Security TalksJoin our security experts in this ongoing series as they explore the latest AI innovations across our secu
New Abuse of the ClickOnce Technology, Part 2: Stop Threat Actors from Clicking Once and Staying Forever
New Abuse of the ClickOnce Technology, Part 1: The Inner Workings of ClickOnce Application Deployment
Browser Security: Zero-Days Are Only Part of the Problem
A joint operation involving Google has disrupted NetNut, a residential proxy network that gave access to millions of com
Cybersecurity researchers have discovered a previously undocumented modular malware framework codenamed Avalon that's di
Researchers identified what they believe is the first documented case of a ransomware operation, JadePuffer, conducted e
A U.S. government entity paid about $1 million to keep stolen files from being leaked, according to a new case study by
The North Korean threat actors linked to the Contagious Interview campaign have been observed publishing 108 unique pack
本文研究了LLM编码智能体(coding agent)在依赖第三方技能市场时面临的安全威胁。第三方技能以软件供应链形式引入攻击面:恶意技能可窃取凭证、泄露源码或安装后门。现有防御主要依赖静态扫描器(模式匹配或LLM-as-judge分析),
该论文提出了一种名为 kNNGuard 的无需训练的护栏机制,用于检测大型语言模型(LLM)中的不安全、离题或对抗性提示。现有护栏主要依赖微调构建分类器,但通常泛化能力差且推理延迟高。kNNGuard 利用现成 LLM 的激活空间,仅需 5
这篇论文研究了在API访问受到严格限制的情况下,如何推断大语言模型(LLM)的底层架构属性。当前大多数商业LLM供应商不再公开模型架构细节,且API只返回每个解码token的单一logit值,不再提供logit偏置功能。作者提出了Night
该论文提出了Antaeus,一个用于检测仓库级逻辑漏洞的框架,通过将LLM推理锚定在仓库级代码上下文中来克服现有方法的局限性。逻辑漏洞的识别需要推断应用特定的安全不变量和隐式假设,而现有基于LLM的检测器在内存安全等漏洞上表现良好,但难以处
本文提出 SessionBound,一种将企业 AI 代理的任务审批转化为有预算、可审计的数据库会话的框架。随着 AI 代理在企业内部分析、审计、合规检查和运营调查中的广泛应用,传统的授权机制面临挑战:经理或数据所有者可能批准一个业务任务,
该论文研究了大型语言模型(LLM)在安全对齐后内部表示的安全方向问题。作者首先证实了之前的工作:对齐的LLM在提示侧(prompt-side)的残差流中编码了“有害性”(harmfulness)和“拒绝”(refusal)两个可分离的方向。
该论文揭示了大型语言模型(LLM)在支持函数调用的状态ful环境中存在的一种结构性安全漏洞。在此类系统中,开发者定义的函数schema、结构化参数以及不可信的工具输出被混入同一个共享模型上下文,导致可信控制逻辑与不可信数据间的边界模糊,攻击
该论文是一篇关于生成式人工智能(Generative AI)和联邦学习(Federated Learning)在入侵检测系统(IDS)中应用的综述。入侵检测系统是现代网络环境(如网络物理系统、物联网、企业网络和分布式网络)中监控流量、识别恶
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