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GCSA Insight: Critical Security Vulnerability Discovered in a Leading Robotics Manufacturer’s Product, with System-Level Privilege Compromise Verified on Physical Hardware As artificial intelligence and embodied AI technologies continue to advance, robots are evolving from conventional automated machines into intelligent systems capable of perception, decision-making, and autonomous operation. At the same time, cybersecurity risks are extending beyond software and data into the physical world. The security of robotic systems is no longer solely a matter of protecting data and devices; it can also directly affect operational safety, physical environments, and user safety. Recently, the security research team of the Global Cybersecurity Alliance (GCSA) discovered and verified a critical security vulnerability in a product developed by a leading robotics manufacturer. Through in-depth analysis and multiple rounds of testing on physical hardware, the team successfully breached the device’s system security boundaries and obtained the highest level of operating system privileges in a real-world device environment, demonstrating the vulnerability’s actual impact under specific testing conditions. This research encompassed the entire process, from identifying the security flaw and assessing its risks to validating its impact on physical hardware. The findings also highlight significant challenges facing the underlying security mechanisms of embodied AI systems. The research findings and supporting evidence have been formally submitted to the manufacturer. On November 13, 2026, the manufacturer responded by email, preliminarily confirming the existence of the vulnerability and stating that it had initiated internal remediation efforts and an assessment of the scope of affected products. It is important to emphasize that confirmation of a vulnerability does not mean the associated security risks have been eliminated. GCSA will continue to monitor the manufacturer’s assessment of the affected scope, remediation plans, and subsequent security validation, and will update its research findings as new information becomes available. For intelligent systems capable of physical interaction and movement, timely, effective, and verifiable security remediation is particularly important. In accordance with responsible vulnerability disclosure principles, GCSA is currently withholding sensitive information, including the manufacturer’s identity, specific product details, the vulnerability’s underlying mechanisms, and exploitation methods. GCSA is currently seeking guidance from relevant authorities and regulatory bodies regarding the associated security risks. Taking into account the manufacturer’s remediation progress and a comprehensive assessment of potential security implications, GCSA will carefully determine the appropriate arrangements for further disclosure. Subject to appropriate conditions and upon obtaining the consent of the relevant authorities and regulatory bodies, GCSA will publicly release a detailed security insight report, providing the industry with ongoing updates on the research findings and associated risk assessments. The findings of this research are based on specific testing environments and the evidence obtained to date. They do not imply that other product models or similar robotic systems are affected by the same security issue. This security research and validation on physical hardware represent one of GCSA’s initiatives in the field of embodied AI security. GCSA will continue to advance security research, coordinated vulnerability disclosure, and industry collaboration to promote stronger security response mechanisms across the artificial intelligence sector, enhance product security, and strengthen industry trust. 🔗 Read the full report: · Stay Connected with GCSA Follow GCSA's official social media channels for the latest news, product launches, research developments, and ecosystem updates. GCSA Official Telegram Channel: GCSA Official WeChat Account: #GCSAInsight# #GCSACybersecurity# #GCSASecVibe#

We’ve partnered with Fanatics Studios, the joint venture between @Fanatics and @OBB, for a multiyear collaboration that will bring Fanatics Studios’ creative and event production capabilities to NBA All-Star, including in-arena events, competitions and original content, starting with NBA All-Star 2027 in Phoenix.

.@Trondao, @Tether and @Chainalysis today announced the founding of the Digital Crime Task Force (DCTF), a new initiative designed to proactively identify and disrupt illicit activity involving Tether’s USD₮ stablecoin on the TRON blockchain. DCTF brings together three industry leaders — a blockchain network, a stablecoin issuer and a blockchain intelligence company — in a next-level collaboration built around proactive defense. Read more on @Trondao 👇

Apple CEO Tim Cook says neither a degree nor coding skills get you hired at Apple : "People that code, People that don't" . Even though he calls coding "the only global language that we all share." Instead, he screens for three traits: collaboration rooted in deep belief that "one plus one equals three," genuine curiosity about how things and people work, and creativity that lets people "see around the corner." The logic is that research tells you what customers want today, but only a person can sense what they'll need in three years. As Cook puts it, the goal is to "get ahead of the curve" and together these traits make a great team player.

今年和 Token2049 Singapore 的合作可以说是收获满满✨ 一边是 Foresight News @Foresight_News 以 Media Advocacy 的形式参与合作，目测是唯一一家 #Web3# 中文垂直媒体的 Logo 在 Token2049 官网上展示，我们会持续将大会的精彩内容带给中文社区！ 另一边，借着还在「牛学」阶段，牵头和几家海外高校区块链社团一起，以 Partner 的形式参与其中，主办方为每家社团提供限量学生门票，让更多对 #Web3# 感兴趣的同学有机会到现场体验一番！ 高校区块链社团的小伙伴们来自： 🏫悉尼大学区块链社团 UNCB @UNCBUSYD @kassymoon_ 🏫墨尔本 FinTech Student Alliance：来自墨大、Monash、RMIT 的同学 @Ancosoho @Zzx2117577 🏫Monash 马来西亚校区区块链社团 @mumbcs @MarioTaning 🏫Foresight News 中国大陆高校创作者社区 @Foresight_News 💛 Huge thanks to Natalie at @Wachsman_ and Máté at @token2049 for all your help and support in making these collaborations happen! 🙌 🩵 If you’re there, be sure to say hi to @Chloeppan from FN team!

Having some fun doing my own Grok Bot @bot ( Dots, Muse ) alternative with @tan_stack start, @pidotdev + @Cloudflare durable objects! 💅 - Multi-harness Support - Multi Decision Room Routing ( Jev, LLM Judge, Roundtable ) - Memory for Bots, Rooms & Global - Reactions 👍 - Daemon runs locally + web on cloudflare - File Support with R2, Room data & Bot Data exportable on CF durable. Will open-source next week hopefully, so give a follow & stay tunned. I was going to use some open-source alternatives or fork them, but most of them are damn sloppy, my ideia is to use this for myself since OpenClaw & Hermes lack group collaboration & a proper mobile / responsive design. Dots is not available in EU, Grok is "locked" to grok models but for me its still the best in terms of UX/UI overall so congrats to Cursor & Xai Team :p

Imagine seeing your work announced on the national stage by the world's most gifted speakers, like @SecRubio who introduced our new design for the U.S. Passport, in collaboration with the State Department. National Design Studio is growing!

Today we're introducing MagicPath directly in ChatGPT, in collaboration with OpenAI. MagicPath now lives in your sidebar. Access all your files and open your canvas right inside ChatGPT. It's never been easier to design and build.

这个开源项目系统整理了 35 家 AI 公司的 AI 工程师面试题，全部来自 “公开报告的面试经历” 来自 @outcome_school 团队 @pallavishekhar_ 开源发布，内容几乎涵盖了 OpenAI、Anthropic、DeepMind、xAI、DeepSeek、Kimi、GLM 等 AI Labs，Cursor、Cognition、ElevenLabs 等 AI Native 团队和 Nvidia、Microsoft、Amazon、Apple 等头部大厂。 覆盖的岗位头衔也非常多：AI Engineer、LLM Engineer、Gen AI Engineer、ML Engineer、Research Engineer、Applied Scientist、FDE、MLOps/LLMOps 工程师等。 开源地址： # 内容架构：一个精心设计的双层结构 第一层：跨公司通用题（Common Questions）。 作者把在多家公司反复出现的题目只列一次，标注"Asked at"哪些公司，按十大主题组织： 1. LLM 内部机制与架构 — attention 缩放因子、KV cache 内存公式推导、MQA/GQA/MLA、FlashAttention、BPE、RoPE/YaRN、Chinchilla scaling laws、MoE、解码采样策略、lost-in-the-middle、RMSNorm、SwiGLU 2. 推理、服务与 GPU 性能 — prefill vs decode、continuous batching、PagedAttention、投机解码、量化（FP16→FP4）、五种并行策略、TTFT/TPOT 指标、H100 上的 roofline 计算、vLLM/SGLang/TensorRT-LLM 选型、“如何把服务成本降 10 倍” 3. RAG 与检索 — 分块策略、BM25 vs 稠密检索、重排序器、HyDE、权限感知检索、ANN 索引、索引新鲜度、答案归因 4. Agent 与工具调用 — ReAct、MCP、工具 schema 设计、多智能体编排、Agent 记忆、循环终止条件、人类审批 5. 微调与对齐 — RLHF/DPO/GRPO/RLVR 全谱系、LoRA/QLoRA 数学、灾难性遗忘、“提示 vs RAG vs 微调”决策框架、蒸馏、reward hacking 6. 评估与可观测性 — LLM-as-judge 及其偏差、幻觉检测、基准污染、Agent 评估、回归门禁 7. 安全与负责任 AI — 提示注入（直接/间接）、OWASP LLM Top 10、护栏、Constitutional AI、红队 8. 多模态与语音 — VLM、语音 Agent 延迟预算、barge-in 打断处理、级联 vs 端到端语音、ASR/TTS 评估 9. AI 系统设计 — 十类高频设计题（千万级文档企业 RAG、代码助手、客服 Agent、Text-to-SQL、LLM 网关、数亿用户聊天服务等） 10. 编码题 — 从零实现 attention、KV cache、BPE、采样；LRU 缓存、令牌桶限流器、异步批处理器、SSE 流解析器、最小 Agent 循环 第二层：35 家公司的专属章节，分为五大梯队： 1. 前沿实验室（12 家）：Anthropic、OpenAI、Google DeepMind、Meta、xAI、Mistral、Cohere、DeepSeek、月之暗面（Kimi）、智谱（GLM）、阿里（Qwen）、Sarvam AI（印度） 2. 大厂 AI 组织：Microsoft、Amazon、Apple、NVIDIA、Tesla，以及一组消费级 ML 公司（Uber/Netflix/LinkedIn/Airbnb/Pinterest/Spotify） 3. AI 基础设施公司：Databricks、Groq、Together AI、Hugging Face、Scale AI、Perplexity 4. AI 原生产品公司：Cursor、Cognition（Devin）、Sierra、Harvey（法律）、Glean、 AI（机器人）、Waymo 5. 前向部署/企业 AI：Palantir # 题目分布透露的行业信号同样值得关注 1. 公司的差异化考察方向，和它的商业模式严丝合缝。 这是最能体现整理功力的地方： · DeepSeek、月之暗面、智谱、Qwen 的题目深度绑定自家论文——MLA、auxiliary-loss-free 负载均衡、Multi-Token Prediction、MuonClip、DualPipe、长上下文扩展、GLM 的 thinking 模式。面试这些公司等于面试它们的论文，还要求 PyTorch 从零实现 MoE 路由。 · Groq 的题全是 SRAM-only 架构下的 roofline 重推演：“没有 HBM，decode 的 roofline 论证哪里变了”、“确定性在 p99 层面到底买到什么”。 · Apple 清一色端侧：3B 模型在手机上跑、PTQ vs QAT、不采集用户内容的前提下用设备信号改进模型、30+ 语言区无法记录用户内容的评估方案。 · CharacterAI 是推理经济学：“我们的服务成本被 KV cache 而非权重主导，降一个数量级，代价是什么”。 · Harvey（法律）和 Abridge（医疗） 考的是领域约束下的工程：200 页信贷协议里第 140 页的条款依赖第 8 页的定义术语怎么检索、生成的病历中出现了患者没提过的药怎么当作安全事故处理、PHI 如何约束整个架构。 · Palantir 的招牌是 "decomposition" 轮：把“一家货运铁路公司每年因机车非计划停机损失数千万”分解成工程计划。 2. 编码轮的形态正在发生实质性变化。 文档里反复出现的一类题，与传统 LeetCode 明显不同： · “实现一个内存 KV 存储：先 SET/GET/DELETE，再加事务 BEGIN/COMMIT/ROLLBACK，包括嵌套事务”（OpenAI、xAI 都问） · “给你一个 LLM 推理引擎的调度器类，其中一个方法是空壳，没有规格没有文档。说说你头三十分钟干什么”（xAI） “重构这 120 行能跑但很乱的代码，不许破坏测试”（OpenAI） · “对 5 万个文档跑 LLM 调用，API 限 100 并发、偶发 429 和超时，把 Python 写出来”（Anthropic） 考察重心从算法记忆转向增量需求下的代码演进能力、并发正确性、真实工程约束下的取舍。 3. “AI 协作轮”作为新题型已经进入正式面试流程。 这是文档里最前沿的信号： · Anthropic 部分机器学习岗有 AI-collaboration 轮：现场给你 Claude，考察的是你如何指挥它和验证它的产出，而不是你自己写。 · Meta 2026 年的流程新增三阶段 AI 辅助编码轮（探索修复 → 实现新功能 → 扩展改进）。 · Cursor 的 onsite 是两天在真实 Cursor 代码库上做一个功能（或 8 小时远程版），明确考核你对 AI 工具的使用效率和自主 scoping 能力。 · Sierra 给你两小时和任意 AI 工具，看你选择做什么。 xAI 有四小时限时产品构建。 4. FDE 成为一级岗位类别。 Anthropic、OpenAI、Databricks、Scale、Together、Sierra、Harvey、ElevenLabs、Palantir 的章节里都有 "Applied and Forward-Deployed Scenarios" 专属题库——典型题目如“企业客户说 Claude 幻觉太多，你是驻场工程师，头 48 小时做什么”。这对应了 AI 公司向企业交付方式的转变：模型能力差距收窄后，落地能力成为差异化。 5. 硬核系统题的普及。 "H100 上 70B 模型 batch size 1 的 roofline 计算"、"估算 70B 模型的 GPU 显存（权重 + KV cache + 激活 + 碎片）"、"p99 延迟在部署后翻倍但模型没变，走一遍诊断”——这类题横跨 NVIDIA、Together、OpenAI、Perplexity 等多家，说明推理性能的量化直觉已成为 AI 工程师的通用素养，而非基础设施工程师的专属。