7 Matching Annotations
  1. Last 7 days
    1. in 89% of the 198 manually reviewed vulnerability reports, our expert contractors agreed with Claude's severity assessment exactly, and 98% of the assessments were within one severity level. If these results hold consistently for our remaining findings, we would have over a thousand more critical severity vulnerabilities and thousands more high severity vulnerabilities.

      89%的严重性评估精确一致是一个重要的校准信号:它意味着Mythos不仅能找到漏洞,还能准确理解其安全影响。这个校准水平与经验丰富的人类安全研究员相当甚至更优。基于这个比率外推的「上千个关键严重性漏洞」虽然是估计值,但有统计基础——这是迄今为止关于AI大规模漏洞发现能力最有力的量化声明。

  2. May 2026
    1. This attack achieved a high success rate against state-of-the-art models, including Claude Opus 4.7.

      大多数人认为最新的AI模型已经足够先进可以抵抗基本的注入攻击,但作者证明即使是像Claude Opus 4.7这样的前沿模型也无法抵御简单的间接提示注入,这挑战了人们对先进AI模型安全性的过高期望。

    1. The most urgent finding this week comes from researchers who demonstrated that the very mechanism enabling agents to use tools - function calling - can be hijacked with alarming reliability.

      这一发现揭示了AI代理工具调用接口的安全漏洞,为构建安全的AI代理系统提出了新的挑战。

  3. Apr 2026
    1. just a handful of obviously fake articles could cause Gemini, ChatGPT, and Copilot to inform users about an imaginary disease with a ridiculous name.

      令人惊讶的是:仅凭少量明显虚假的文章就能导致主流AI模型传播虚构疾病信息。这揭示了AI训练数据容易被污染的脆弱性,也暗示了未来可能需要类似'低背景钢'的纯净数据源来确保AI输出的可靠性。

    1. select known-vulnerable dependency versions 50% more often than humans.

      这一统计洞察颠覆了“AI写代码更安全”的迷思。AI代理在优化代码功能性时,往往以牺牲安全性为代价,倾向于选择存在已知漏洞的旧版本依赖。这反映出当前AI模型在训练时对安全维度的忽视,也警示我们在AI辅助开发流程中必须强制引入自动化的安全卡点。