In Bouncy Castle for Java before 1.86, the Messaging Layer Security (MLS, RFC 9420)...
CVE-2026-71885
In Bouncy Castle for Java before 1.86, the Messaging Layer Security (MLS, RFC 9420) implementation did not bind an X.509
今日共收录 931 条安全情报,包含 128 个 CVE,457 篇安全通告,以及 95 篇研究论文。
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In Bouncy Castle for Java before 1.86, the Messaging Layer Security (MLS, RFC 9420) implementation did not bind an X.509
CVE-2026-71891
In Bouncy Castle for Java before 1.86, BLS12_381BasicScheme.keyValidate, and so BLSPublicKeyParameters and every BasicSc
CVE-2026-71887
In Bouncy Castle for Java before 1.86, the high-level OpenPGP API accepted a data signature made by a signing subkey who
CVE-2026-71886
In Bouncy Castle for Java before 1.86, the high-level OpenPGP certificate API accepted a third-party certification or tr
CVE-2026-87115
The VikAppointments Services Booking Calendar plugin for WordPress is vulnerable to arbitrary file deletion due to insuf
CVE-2026-103421
The WPMobile.App – Android and iOS App Builder plugin for WordPress is vulnerable to Stored Cross-Site Scripting via the
CVE-2026-92974
The Photo Gallery by 10Web – Mobile-Friendly Image Gallery plugin for WordPress is vulnerable to Reflected Cross-Site Sc
CVE-2026-88783
The Kubio AI Page Builder WordPress plugin before 2.9.3 does not limit its widening of the allowed HTML elements to the
CVE-2026-91078
The TillKit WordPress plugin before 1.0.5 does not require the hard-coded, publicly known PIN of the privileged POS acco
CVE-2026-88782
The Kubio AI Page Builder WordPress plugin before 2.9.3 does not validate the URI scheme of a user-supplied value before
CVE-2026-97644
The Groundhogg — CRM, Newsletters, and Marketing Automation plugin for WordPress is vulnerable to Privilege Escalation v
CVE-2026-100152
The The All in One SEO – AI SEO Plugin to Boost SEO Rankings & Traffic (Schema, Local SEO, Sitemap & SEO Insights) plugi
CVE-2026-73802
### Summary act_runner appends workflow-controlled `jobs..container.options` directly to the Docker HostConfig for the
该论文关注量化大语言模型(qLLM)在边缘设备上的安全风险。量化虽带来低延迟、低能耗的部署优势,但会削弱模型的对齐防护能力,使 qLLM 更易被越狱提示绕过。作者提出 MOMAT(Mixture of Multiple Atlases),一
该论文关注多模态检索增强生成(MRAG)引入的新型安全风险。MRAG 通过将外部、最新的多模态知识注入多模态大模型(MLLM)的生成过程,有效降低幻觉、提升事实性,并具备成本优势,但同时把原本封闭的私有数据存储(datastore)暴露为新
随着 ACP、A2A 等 Agent 交互协议的普及,基于大模型的智能体正从单体工具调用走向多智能体协作,由此引入了新的安全威胁:在 A2A 场景下,来自远端 peer 的任务会被本地 Agent 默认视为合法请求,这为间接提示注入提供了天
该论文研究 LLM 工具调用型智能体(tool-calling agents)在结构化工具调用场景中的一种隐私风险行为,作者将其命名为“主动式越权(proactive over-authorization)”,即智能体在完成用户请求时,主动
该论文研究的是参数高效微调(PEFT)场景下的后门防御问题。随着大语言模型(LLM)与 LoRA 等轻量微调方法的广泛采用,攻击者可以通过污染微调数据或发布被植入后门的适配器(adapter),使模型在特定触发条件下产生恶意输出,而基座模型
该论文研究大型语言模型(LLM)后门防御问题。被植入后门的 LLM 在正常输入上表现正常,但在隐藏触发器下会产生攻击者指定的输出。现有防御覆盖训练前、训练中、训练后和推理时四个阶段,底层策略可归为两类:一是抑制后门学习,例如过滤投毒数据或在
该论文研究的是大语言模型(LLM)微调过程中的隐私泄漏问题。此前的多项工作已经表明,微调不仅能让模型适配下游任务,还会“唤醒”那些通过直接查询已经无法获取的私有信息——即所谓的微调恢复攻击(fine-tuning recovery atta