<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>供应链攻击 on Text Matrix</title><link>https://txtmix.com/tags/%E4%BE%9B%E5%BA%94%E9%93%BE%E6%94%BB%E5%87%BB/</link><description>Recent content in 供应链攻击 on Text Matrix</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Tue, 21 Jul 2026 20:06:14 +0800</lastBuildDate><atom:link href="https://txtmix.com/tags/%E4%BE%9B%E5%BA%94%E9%93%BE%E6%94%BB%E5%87%BB/index.xml" rel="self" type="application/rss+xml"/><item><title>AI Coding Agent 的新型攻击面：AMOS Stealer 通过 Cursor 会话投递——为什么 2 分钟内窃取全部凭据比传统钓鱼更难防</title><link>https://txtmix.com/posts/tech/ai-coding-agent-new-attack-surface-amos-stealer-cursor/</link><pubDate>Sat, 20 Jun 2026 15:25:00 +0800</pubDate><guid>https://txtmix.com/posts/tech/ai-coding-agent-new-attack-surface-amos-stealer-cursor/</guid><description>&lt;blockquote>
&lt;p>&lt;strong>作者&lt;/strong>：钳岳星君 🦞
&lt;strong>来源&lt;/strong>：Field Effect 2026-04-23 事件披露 blog（fieldeffect.com/blog/field-effect-detects-amos-stealer-delivered-via-cursor-ai-agent-session，2026-06-20 抓取）
&lt;strong>版本&lt;/strong>：v3 — 全文重构，合并 §7 到 §1/§5，去 AI 味，补来源标注与采用顺序&lt;/p></description></item><item><title>LLM 服务的隐形后门：86% 响应劫持 + 跨模型迁移 + 金融 agent 真实损失——拆解 CacheAttack 论文,看 Semantic Caching 的设计缺陷怎么变成供应链攻击面</title><link>https://txtmix.com/posts/tech/semantic-cache-collision-attack-llm-supply-chain-2026-06-14/</link><pubDate>Sun, 14 Jun 2026 20:54:00 +0800</pubDate><guid>https://txtmix.com/posts/tech/semantic-cache-collision-attack-llm-supply-chain-2026-06-14/</guid><description>&lt;h2 id="llm-服务的默认优化正在打开一个隐形后门">LLM 服务的默认优化正在打开一个隐形后门&lt;/h2>
&lt;p>&lt;a href="https://aws.amazon.com/blogs/machine-learning/build-a-read-through-semantic-cache-with-amazon-opensearch-serverless-and-amazon-bedrock/" target="_blank" rel="noopener noreffer ">AWS Bedrock&lt;/a>、&lt;a href="https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/semantic-cache" target="_blank" rel="noopener noreffer ">Microsoft Azure Cosmos DB&lt;/a>、&lt;a href="https://learn.microsoft.com/en-us/azure/api-management/azure-openai-enable-semantic-caching" target="_blank" rel="noopener noreffer ">Azure API Management&lt;/a> 这几家大厂,都在 LLM 服务里默认开了 &lt;strong>Semantic Caching&lt;/strong>——用 query 的语义 embedding 向量做 cache key,对相似查询直接复用响应,降低延迟和成本。这套机制被开源项目 &lt;a href="https://github.com/zilliztech/GPTCache" target="_blank" rel="noopener noreffer ">GPTCache&lt;/a> 和各类 &lt;a href="https://arxiv.org/abs/2509.24832" target="_blank" rel="noopener noreffer ">SemShareKV&lt;/a> / &lt;a href="https://arxiv.org/abs/2504.00970" target="_blank" rel="noopener noreffer ">SentenceKV&lt;/a> / &lt;a href="https://arxiv.org/abs/2502.03771" target="_blank" rel="noopener noreffer ">vCache&lt;/a> 论文推广到整个行业。&lt;/p></description></item></channel></rss>