深度学习 · 2026-09-23 Deep Dive · 2026-09-23

当恶意软件开始自己开会投票:CLOSEDQUORUM 与 AI 原生网络攻防的第一天

When Malware Starts Holding Its Own Votes: CLOSEDQUORUM and Day One of AI-Native Cyber Warfare

2026 年 9 月 22 日,同一天里发生了两件互为镜像的事:Cisco Talos 披露了 CLOSEDQUORUM——第一个把"要不要偷密码、要不要注入进程、要不要横向移动"这类战术决策,完全交给四个大模型投票决定的 Windows 恶意软件;几乎同时,Palo Alto Networks 上线了由 Claude 与 GPT 前沿模型驱动的"持续前沿 AI 防御"服务。这不是又一篇"AI 会不会威胁网络安全"的思辨文章,而是攻防双方**已经**把决策权交给模型之后,世界长什么样的第一手记录。

On September 22, 2026, two mirror-image events happened on the same day: Cisco Talos disclosed CLOSEDQUORUM — the first Windows malware to hand tactical decisions like "steal credentials or inject a process or move laterally" entirely to a vote among four large language models — and, almost simultaneously, Palo Alto Networks launched a "Continuous Frontier AI Defense" service built on Claude and GPT frontier models. This isn't another think-piece asking whether AI *might* threaten cybersecurity. It's a first-hand record of what the world looks like once both attackers and defenders have already ceded decision-making to models.

事件日期:2026-09-22Event date:2026-09-22 调研日期:2026-09-23Research date:2026-09-23 阅读时长:约 22 分钟Reading time:~20 min

01 · 30 秒版本

01 · The 30-Second Version

🎯开篇速览Overview

过去谈"AI 恶意软件",通常指攻击者用 ChatGPT 写钓鱼邮件、生成免杀代码——人还是那个按回车键的人,AI 只是更快的工具。CLOSEDQUORUM 不一样:它侵入目标电脑后,不再联系任何人类操盘手,而是把"接下来干什么"这个问题,同时发给 DeepSeek、Qwen、Mistral、Google Gemini 四个大模型,让它们像董事会投票一样各投一票,票数最多的动作(偷密码 / 注入进程 / 建立持久化 / 横向移动)就被执行。这在 Cisco Talos 的报告里被称为"首个被公开记录、将这一模式应用于战术级指挥控制(C2)的 Windows 植入体"。

Until now, "AI malware" mostly meant an attacker using ChatGPT to draft a phishing email or generate evasive code — a human was still the one pressing enter, and AI was just a faster tool. CLOSEDQUORUM is different: once it compromises a machine, it stops contacting any human operator and instead sends the question "what should I do next?" simultaneously to four LLMs — DeepSeek, Qwen, Mistral, and Google Gemini — which vote like a board of directors. Whichever action gets the most votes (steal credentials / inject a process / persist / move laterally) is the one that runs. Cisco Talos calls it "the first publicly documented Windows implant to apply this model to tactical command and control (C2)."

与此同时,Talos 也开源了配套的狩猎工具 CAIRN(Cognitive Artifact Intelligence Research Network),专门用来在海量样本里找出"沾了 AI 的恶意软件"。而在完全独立的另一条线上,Palo Alto Networks 的 Unit 42 团队在同一天发布了"持续前沿 AI 防御"(Continuous Frontier AI Defense)服务,底层跑的是 Anthropic 的 Claude Mythos 5 和 OpenAI 的 GPT-5.6-Cyber,用来对企业做 7×24 小时的自动化渗透测试与漏洞验证。这篇文档就是要把这三件"同一天发生的事"讲透:它们分别是什么、怎么运作、可信到什么程度、普通安全团队该做什么。

At the same time, Talos open-sourced a companion hunting tool called CAIRN (Cognitive Artifact Intelligence Research Network), built specifically to find "AI-touched malware" in large sample sets. On a completely separate track, Palo Alto Networks' Unit 42 team launched "Continuous Frontier AI Defense" the very same day, running on Anthropic's Claude Mythos 5 and OpenAI's GPT-5.6-Cyber to run round-the-clock automated penetration testing and exploit validation for enterprises. This document unpacks all three same-day events: what each one is, how it actually works, how much of it to believe, and what an ordinary security team should do about it.

图 1:两种"C2"的根本区别——左边是人类通过服务器下指令,右边是四个大模型投票替代了人类和服务器。图源:根据 Cisco Talos 原文描述整理绘制。

Fig. 1: The fundamental difference between two kinds of "C2" — on the left, a human issues commands through a server; on the right, four LLMs vote in place of both the human and the server. Diagram compiled from the Cisco Talos writeup.

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先划重点:这不是"AI 学会了黑客技术"的故事,而是"攻击者把决策权外包给了云端 API"的故事。CLOSEDQUORUM 本身的攻击手法(偷密码、注入进程)都是老套路,真正新的地方只有一处——谁来决定下一步做什么。

The one thing to remember: this is not a story about "AI learning to hack." It's a story about an attacker outsourcing the decision loop to a cloud API. CLOSEDQUORUM's actual techniques — stealing credentials, injecting processes — are old tricks. The only genuinely new part is who decides what happens next.

02 · 背景与动机

02 · Background & Motivation

🕰️背景:一场加速的军备竞赛Background: An Accelerating Arms Race

要理解 CLOSEDQUORUM 为什么值得单独写一篇文档,得先看清它出现之前,这条路已经被走了多远。过去 13 个月里,"AI 参与网络攻击"这件事经历了三级跳,每一级都把 AI 往决策链的更深处推了一步。

To understand why CLOSEDQUORUM deserves its own deep dive, it helps to see how far this road had already been walked before it appeared. Over the past 13 months, "AI's role in cyberattacks" has advanced in three distinct jumps, each pushing AI one layer deeper into the decision chain.

图 2:AI 卷入攻防两端的时间线。三个"第一次"依次登场,再到攻防同日破局。图源:综合 ESET welivesecurity、Anthropic 披露报告、DarkReading 2026 预测文章与 Cisco Talos/Palo Alto Networks 官方公告整理。

Fig. 2: Timeline of AI's growing role on both sides of the fight, culminating in the same-day attack/defense breakthrough. Diagram compiled from ESET's welivesecurity, Anthropic's disclosure report, DarkReading's 2026 predictions piece, and the official Cisco Talos / Palo Alto Networks announcements.

2025-08:PromptLock —— AI 第一次"现场编写"恶意代码

2025-08: PromptLock — AI Writes Malicious Code On the Spot, For the First Time

ESET 研究人员发现了业内公认的首个 AI 驱动勒索软件 PromptLock。它用 Golang 编写,内置调用本地 Ollama 服务、驱动 OpenAI 开源的 gpt-oss-20b 模型,现场生成用于遍历文件、窃取数据、加密文件的 Lua 脚本并直接执行。但 ESET 明确说明,PromptLock 从未在真实攻击中被观察到,更像是一个概念验证(PoC)——后来还有学术研究者联系 ESET,称自己的实验样本与该恶意软件高度相似,进一步印证了"研究原型"的判断。

ESET researchers discovered what's widely regarded as the first AI-powered ransomware, PromptLock. Written in Go, it calls a local Ollama service running OpenAI's open-weight gpt-oss-20b model to generate on the fly the Lua scripts it uses to enumerate files, exfiltrate data, and encrypt — then executes them directly. But ESET was explicit that PromptLock was never observed in a real attack; it looked more like a proof-of-concept, a judgment later reinforced when academic researchers contacted ESET saying their own experimental prototype closely resembled the sample.

2025-11:GTG-1002 —— AI 第一次被曝"主导"一场真实的国家级间谍活动

2025-11: GTG-1002 — AI's First Reported Role "Driving" a Real Nation-State Espionage Campaign

Anthropic 自己披露并阻断了一起被追踪为 GTG-1002 的网络间谍活动:一个人类操盘团队利用 Claude 的 Agent 能力,让模型自主执行了整场入侵中约 80%~90% 的战术操作(侦察、漏洞利用、横向移动等),人类仅负责目标选定和少数关键决策审批。这被普遍认为是首个被公开记录的、大规模由 AI 编排执行的网络攻击——但要注意,目标选择权、战役级决策权仍在人类手中,AI 承担的是"战术执行"而非"战略决策"。

Anthropic itself disclosed and disrupted a cyber-espionage campaign tracked as GTG-1002: a human operator team used Claude's agentic capabilities to have the model autonomously carry out roughly 80-90% of the tactical operations in an intrusion — reconnaissance, exploitation, lateral movement — with humans limited to target selection and a handful of key approval gates. This is widely regarded as the first publicly documented, large-scale AI-orchestrated cyberattack — though it's worth noting target selection and campaign-level decisions still sat with humans; AI handled tactical execution, not strategic decision-making.

2025-12:分析师的预言几乎精确命中

2025-12: An Analyst's Prediction Landed Almost Exactly

市场研究机构 Omdia 的首席分析师 Tyler Shields 在 2026 年网络安全预测中写道,2026 年可能出现"一种具备自我学习和自我保存意识的智能体式网络蠕虫",它会"根据攻击时遭遇的防御手段,彻底改变战术、流程与技术",而不只是简单变形免杀。他同时指出,攻击者不像防御者那样受制于"AI 出错会不会搞垮自己环境"的顾虑,因此 2026 年"攻击者至少会领先一步"。九个月后,CLOSEDQUORUM 的出现——虽然规模和成熟度远不及预言中的"蠕虫"——精确对上了这条预测的方向。

Omdia principal analyst Tyler Shields wrote in his 2026 cybersecurity predictions that the year could bring "a self-learning and self-preservation aware agentic cyber worm" capable of "completely [changing] tactics, processes, techniques... based on the cyber defenses that it runs while attacking targets" — not just simple polymorphism. He also noted that unlike defenders, who must vet AI tools carefully before deployment, "attackers are not limited by fear of their offensive AI technologies causing problems," meaning 2026 would likely see "attackers at least one step ahead." Nine months later, CLOSEDQUORUM — far smaller and less mature than the "worm" he imagined — landed squarely in the direction he predicted.

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看懂这条脉络的关键:PromptLock 解决的是"AI 帮我写代码";GTG-1002 解决的是"AI 帮我执行战术";CLOSEDQUORUM 解决的是"AI 帮我做决策"。三者叠加起来,才是完整的"AI 原生自主网络攻击"拼图——而 CLOSEDQUORUM 补上的正是最后、也是最关键的一块。

The thread that ties this together: PromptLock solved "AI writes my code for me." GTG-1002 solved "AI executes my tactics for me." CLOSEDQUORUM solves "AI makes my decisions for me." Together they form the full picture of "AI-native autonomous cyberattack" — and CLOSEDQUORUM fills in the last, and most consequential, piece.

03 · 机制拆解

03 · Mechanism Breakdown

⚙️机制拆解:委员会如何投票Mechanism: How the Quorum Votes

CLOSEDQUORUM 是一个用 Go 语言编写、体积约 16.4MB 的 64 位 Windows 可执行文件。Talos 通过静态分析还原出了它内部完整的决策循环,核心是一个叫 ModelOrchestrator 的编排器。下面按它真实的执行顺序,逐步拆解这套"投票委员会"是怎么运作的。

CLOSEDQUORUM is a Go-written, roughly 16.4MB, 64-bit Windows executable. Through static analysis, Talos reconstructed its full internal decision loop, centered on an orchestrator function called ModelOrchestrator. Here's the "voting committee" walked through in the order it actually executes.

① 采集主机上下文① Gather host context
植入体先读取主机名、操作系统版本、当前进程是否有管理员权限等信息,拼成一段 TARGET:%s 格式的文本,作为后面每次提问模型时的"案情简介"。

The implant first reads the hostname, OS version, and whether the current process has admin rights, formatting them into a TARGET:%s string that becomes the "case brief" attached to every prompt sent to the models.
② 构造系统提示词,限定"能说的话"② Build a system prompt that constrains the models to one of four words
从二进制中提取出的系统提示词写着:"你是一名高级恶意软件策略师,只能给出可执行的决策。"(You are an advanced malware strategist. Provide ONLY executable decisions.)同时用结构化输出(强制模型只能返回固定 JSON 格式)把答案锁定在四个选项里:steal(偷)、inject(注入)、persist(扎根)、move(横移)。

The system prompt extracted from the binary reads: "You are an advanced malware strategist. Provide ONLY executable decisions." It also uses structured output (forcing the model to return a fixed JSON schema) to lock the answer to exactly four options: steal, inject, persist, or move.
③ 依次询问四个云端模型③ Query four cloud models, one after another
queryLLM() 函数依次向 DeepSeek(api.deepseek.com)、Qwen、Mistral(api.mistral.ai)、Google Gemini 发起 API 调用,每个模型独立给出一个动作建议——彼此之间不共享对方的答案,是并行投票而非讨论出的共识。

queryLLM() calls DeepSeek (api.deepseek.com), Qwen, Mistral (api.mistral.ai), and Google Gemini in sequence via their APIs. Each model returns its own action suggestion independently — they don't see each other's answers, so this is parallel voting, not a negotiated consensus.
④ 多数票决胜,平票由 DeepSeek 拍板④ Plurality wins; DeepSeek breaks ties
interModelDiscussion() 函数把四票汇总进一个 map[string]int 计数器里,得票最多的动作胜出;如果出现平票,则按 DeepSeek → Qwen → Mistral → Gemini 的固定优先级,由 DeepSeek 的投票确定性地决出胜负——不是随机数,是写死在代码里的规则。

interModelDiscussion() tallies the four votes into a map[string]int counter; whichever action gets the most votes wins. On a tie, priority falls to DeepSeek, then Qwen, then Mistral, then Gemini — DeepSeek's vote deterministically breaks the tie. It's not random; it's a hard-coded rule in the binary.
⑤ 执行胜出动作⑤ Execute the winning action
根据胜出的动作,植入体分别执行:用 MiniDumpWriteDump 转储 LSASS 内存偷凭据、偷 Chrome/Edge/Firefox 浏览器密码和 MetaMask/Exodus 等加密钱包;用 APC 注入或进程镂空(process hollowing)做代码注入;通过注册表 Run 键、计划任务、WMI 事件订阅建立持久化;并尝试抑制 ETW(Windows 事件跟踪)日志来规避检测。

Depending on the winning action, the implant does one of: dumping LSASS memory via MiniDumpWriteDump to steal credentials, and stealing saved passwords from Chrome/Edge/Firefox and crypto wallets like MetaMask and Exodus; injecting code via APC injection or process hollowing; establishing persistence through Registry Run keys, scheduled tasks, or WMI event subscriptions; and attempting to suppress ETW (Event Tracing for Windows) logging to evade detection.
⑥ 加密打包,经 Discord 回传⑥ Encrypt, chunk, and exfiltrate via Discord
窃取到的数据用 AES-256-GCM 加密(密钥由当前日期派生),Base64 编码后切成 1,900 字节一段,通过一个攻击者控制的 Discord Webhook 分段发出;同一个频道也会实时收到模型们的决策结果和"理由"文本,相当于攻击者在 Discord 里围观一场 AI 董事会的实况直播。

Stolen data is encrypted with AES-256-GCM (key derived from the current date), Base64-encoded, chunked into 1,900-byte segments, and sent out through an attacker-controlled Discord webhook. The same channel also receives, in real time, each model's decision and its stated "reasoning" — the attacker effectively watches an AI boardroom livestream on Discord.

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一个体现代码质量的细节:如果四个模型的 API 全部调用失败,植入体不会默认执行某个危险动作,而是把结果归类为未映射的 consensus,让循环休眠后重试——这说明设计者预料到了云端 API 不稳定的情况,做了保守的失败处理,而不是"failure = 乱来"。这一点在下文"踩坑与审慎"里还会用到。

A detail worth noting about code quality: if all four model API calls fail, the implant does not default to executing some dangerous action. It instead falls through to an unmapped consensus case, sleeps, and retries — a conservative failure path, suggesting the author anticipated flaky cloud APIs rather than letting "failure" mean "do something reckless." We'll come back to this in the caveats section.

💬 换个说法讲一遍:这到底像什么?

💬 Rephrased: what is this actually like?

想象一个刚闯进你家的窃贼,他不再打电话问同伙"接下来干嘛",而是掏出四部手机同时发短信问四个不同的军师:"是先翻保险箱、还是先撬门锁、还是先蹲下来等你回家?"四条回复里选票数最多的那条照做,平票就听第一个军师的。这四位军师彼此不认识、互不商量,只是碰巧被同时问到了同一个问题。

Picture a burglar who's just broken into your house. Instead of calling an accomplice to ask "what now," he pulls out four phones and texts four different advisors at once: "should I hit the safe, pick the lock, or wait for you to come home?" He does whatever the majority of the four replies say, and if it's a tie, he defers to the first advisor. The four advisors don't know each other and never confer — they just happen to get asked the same question at the same moment.

04 · 术语速查

04 · Quick Glossary

📖术语表Glossary

下面这些术语在全文中会反复出现,英文原词保留(便于检索原始资料),首次出现时已随文给出解释,这里再集中列一份速查表。

These terms recur throughout the document. The English terms are kept as-is (for easy cross-referencing with primary sources); each is explained inline on first use, and collected here for quick lookup.

C2 / Command & Control

"指挥控制",指攻击者用来远程遥控已入侵设备的通信通道。传统 C2 需要一台服务器和一个人在另一头下命令;CLOSEDQUORUM 把"下命令的那一头"换成了模型投票。

The communication channel an attacker uses to remotely control a compromised machine. Traditionally this means a server with a human issuing commands on the other end; CLOSEDQUORUM replaces that human end with a model vote.

Plurality voting(多数票 / 相对多数制)

不要求过半数,只要票数比其他选项都多就算赢。CLOSEDQUORUM 四票里只要有两票选同一个动作、且没有其他动作票数更高,就算这个动作胜出。

A vote doesn't need to be a majority (more than half) — it just needs more votes than any other option. Two out of CLOSEDQUORUM's four votes agreeing (with no option beating it) is enough to win.

LSASS dump(LSASS 内存转储)

LSASS 是 Windows 里管理用户登录凭据的系统进程,把它的内存整个转储出来,就能从中提取出登录过这台电脑的账号密码哈希——是 Windows 世界里最经典的凭据窃取手法之一。

LSASS is the Windows process that manages user login credentials. Dumping its entire memory lets an attacker extract password hashes for every account that has logged into the machine — one of the most classic credential-theft techniques on Windows.

Process injection / process hollowing(进程注入 / 进程镂空)

把恶意代码塞进一个正常、受信任的进程里运行,让恶意行为"借壳"合法进程的身份,躲避安全软件按进程名做的检测。

Injecting malicious code into a normal, trusted process so the malicious behavior runs under a legitimate process's identity, evading security tools that detect by process name.

ETW / Event Tracing for Windows

Windows 系统内置的事件日志与遥测机制,是很多安全软件监测异常行为的数据来源;恶意软件"抑制 ETW"就是想办法让自己的动作不被记录下来。

Windows' built-in event logging and telemetry mechanism, a key data source many security tools use to detect anomalous behavior. Malware that "suppresses ETW" is trying to keep its actions off the record.

Cognitive artifact(认知痕迹)

CAIRN 提出的核心概念:恶意软件只要调用了 AI,就会在二进制里留下一些"痕迹"——提示词模板、API Key 前缀、模型服务商的接口地址、越狱关键词等,这些不用运行程序就能静态提取出来。

CAIRN's core concept: any malware that calls an AI model leaves behind traces in the binary — prompt templates, API key prefixes, provider endpoint URLs, jailbreak keywords — all extractable through static analysis without ever running the sample.

YARA rule(YARA 规则)

一种描述"文件里出现哪些特征字符串/模式就判定为某类恶意软件"的规则语言,是安全研究里最基础的静态匹配工具之一。

A pattern-matching rule language used to say "if a file contains these strings/patterns, classify it as X" — one of the most basic static-analysis tools in security research.

Zero Data Retention(ZDR,零数据留存)

Unit 42 前沿防御服务的架构承诺:客户数据不会被留存,也不会被用来训练面向公众的模型——这是企业愿意把渗透测试数据喂给第三方 AI 服务的前提条件。

An architectural commitment from Unit 42's frontier defense service: customer data is never retained and never used to train public models — a prerequisite for enterprises willing to feed their pentest data to a third-party AI service.

05 · 实操指南

05 · Hands-On Guide

🔍怎么用:CAIRN 狩猎工具How To: The CAIRN Toolkit

Talos 的逻辑是:与其等更多 CLOSEDQUORUM 出现在真实攻击里才能反应,不如现在就在全网样本库里"顺藤摸瓜",把所有带 AI 痕迹的可疑样本先筛出来。CAIRN(全称 Cognitive Artifact Intelligence Research Network)就是为此而生的开源工具,开放在 github.com/Cisco-Talos/CAIRN。它的关键设计是只读取 VirusTotal 的元数据,不下载、不运行样本本身,这样才能在大规模样本库上快速、安全地跑起来。

Talos's logic: rather than waiting for more CLOSEDQUORUM-style samples to show up in real attacks before reacting, hunt now — sweep the entire public sample corpus for AI-related traces first. CAIRN (Cognitive Artifact Intelligence Research Network) is the open-source tool built for exactly this, hosted at github.com/Cisco-Talos/CAIRN. Its key design choice is that it reads only VirusTotal metadata and never downloads or executes the sample itself — which is what lets it run fast and safely at scale.

三级分类体系:从"沾边"到"实锤"

The Three-Tier System: From "Touches AI" to "Confirmed Family"

层级	含义	规则数	例子
T1	单个"认知痕迹",召回率高但误报也多——比如出现了某个 AI 服务商的 API 端点	9	API endpoint、Key 前缀、提示词残留
T2	两个以上痕迹以"有操作意义的组合"同时出现,比如既有提示词又有具体的攻击任务指令	8	AI 诱饵提示词 + 智能体化攻击任务
T3	锚定到已确认的恶意软件家族,是最有把握的判定	9	TEAMPCP、HONESTCUE 等家族

Tier	Meaning	Rules	Example
T1	A single "cognitive artifact" — high recall but noisier, e.g. an AI provider's API endpoint appears	9	API endpoint, key prefix, prompt residue
T2	Two or more artifacts co-occurring in an operationally meaningful way, e.g. a prompt template plus a concrete attack-task instruction	8	AI decoy prompt + agentic offensive tasking
T3	Anchored to a confirmed malware family — the highest-confidence verdict	9	TEAMPCP, HONESTCUE families

CAIRN 还把已知的 AI 恶意软件归纳成 11 种"原型"(archetype),方便按套路归类新样本,比如:A1 LLM 驱动的载荷现场生成(PromptLock、HONESTCUE 属于此类)、A4 LLM 直接被当作任务指挥通道(CLOSEDQUORUM 属于此类)、A5 针对 LLM 基础设施供应链的攻击(如后门代理窃取 API Key)、A6 专门窃取 LLM 凭据的窃密木马(PROMPTSTEAL、QUIETVAULT、LAMEHUG)。

CAIRN also groups known AI malware into 11 "archetypes" to make classifying new samples faster — for example: A1, LLM-directed payload generation on the fly (PromptLock and HONESTCUE fall here); A4, LLM used directly as a tasking/command channel (CLOSEDQUORUM); A5, attacks on the LLM infrastructure supply chain, such as a backdoored proxy harvesting API keys; and A6, credential stealers specifically targeting LLM API keys (PROMPTSTEAL, QUIETVAULT, LAMEHUG).

最小上手流程

Minimal Getting-Started Flow

需要 Python 3.11+ 以及一个 VirusTotal Intelligence API Key(必需),PromptIntel API Key 为可选项。安装与基础用法(摘自官方 README):

Requires Python 3.11+ and a VirusTotal Intelligence API key (mandatory); a PromptIntel API key is optional. Install and basic usage (from the official README):

# 安装
git clone https://github.com/fetterm4n/CAIRN.git
cd CAIRN
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
cp .env.example .env    # 填入 VT API Key

# 校验规则与查看可用采集渠道
cairn validate-rules    # 报告 26 条规则
cairn filters            # 列出采集渠道(共 27 个)

# 快速拉取一批候选样本并查看命中统计
cairn pull --filter python-ai-scripts --limit 25
cairn summary

# 深入分析某个具体样本(按 SHA256)
cairn refresh --sha256 <hash> --behaviours
cairn near <sha256> --top 10   # 语义最近邻

# 导出报告
cairn report   # CSV + Markdown

# install
git clone https://github.com/fetterm4n/CAIRN.git
cd CAIRN
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
cp .env.example .env    # fill in your VT API key

# validate rules and list acquisition channels
cairn validate-rules    # reports 26 rules
cairn filters            # lists channels (27 total)

# pull a batch of candidates and view hit stats
cairn pull --filter python-ai-scripts --limit 25
cairn summary

# drill into one sample by SHA256
cairn refresh --sha256 <hash> --behaviours
cairn near <sha256> --top 10   # semantic nearest neighbors

# export report
cairn report   # CSV + Markdown

代码来源:命令与流程摘自 CAIRN 官方 GitHub README(Cisco-Talos/CAIRN),未做改动。安全团队典型用法是:先用 cairn pull 按渠道批量拉取候选样本,用 cairn summary 看规则命中分布,对 T1/T2 命中的样本做人工复核(官方明确说明 T1/T2 命中需要逆向工程验证才能下最终结论),再用 cairn cluster/cairn near 的语义聚类找出同一波攻击的其他变种。

Source: commands and flow are taken verbatim from CAIRN's official GitHub README (Cisco-Talos/CAIRN). A typical security-team workflow: pull candidate batches by channel with cairn pull, review rule-hit distribution with cairn summary, manually validate T1/T2 hits (the docs are explicit that T1/T2 hits require reverse-engineering validation before a final verdict), then use cairn cluster/cairn near semantic clustering to find other variants from the same campaign.

⚠️

官方标注的已知限制:像 PyInstaller、Tauri 这类打包框架本身就会在二进制里留下大量 AI 相关字符串(即使程序压根没调用 AI),会造成较高的误报率;CAIRN 的规则只能基于"可观测的元数据"匹配,匹配不到深层实现细节,因此它被官方定位为"传统逆向分析的补充,而非替代品"。

Officially documented limitations: packaging frameworks like PyInstaller and Tauri leave behind plenty of AI-related strings regardless of whether the program actually calls AI, causing a higher false-positive rate; CAIRN's rules can only match against observable metadata, not deep implementation detail, which is why the maintainers position it as "complementary to traditional reverse engineering, not a replacement."

06 · 防御侧

06 · The Defense Side

🛡️同日应战:Unit 42 前沿防御Same-Day Response: Unit 42 Frontier Defense

如果说 CLOSEDQUORUM 是"进攻方把决策权交给模型委员会",那么同一天由 Palo Alto Networks 旗下 Unit 42 团队发布的 Continuous Frontier AI Defense(持续前沿 AI 防御,以下简称 CFAD)就是"防守方也把大量工作交给模型编排"的对应动作。它不是巧合式的同日撞车,而是行业里"攻防两端同步转向 AI 原生"的一个缩影。

If CLOSEDQUORUM represents the offense handing decision-making to a committee of models, Unit 42's Continuous Frontier AI Defense (CFAD), launched by Palo Alto Networks the very same day, is the defense's mirror move — handing a large share of the work to orchestrated models too. This isn't a coincidental same-day collision; it's a snapshot of the whole industry pivoting to AI-native operations on both sides at once.

它是什么:多模型编排的持续渗透测试

What It Is: Continuous, Multi-Model-Orchestrated Penetration Testing

CFAD 是一个按年订阅的常驻服务,持续对企业的 Web 应用、API、云基础设施、代码仓库、网络资产做漏洞发现;它不满足于"扫出一个漏洞"就完事,还会做端到端的攻击路径模拟来验证这个漏洞是否真的可被利用,最后给出按优先级排序的修复建议、代码级修复指导,以及在 CVE 公开披露前就能用上的"虚拟补丁"建议。数据保护上承诺零数据留存(ZDR)架构,客户数据不会被用来训练面向公众的模型。

CFAD is an always-on, annual-subscription service that continuously scans a company's web apps, APIs, cloud infrastructure, code repos, and network assets for vulnerabilities. It doesn't stop at "found a bug" — it runs end-to-end attack-path simulations to prove the vulnerability is actually exploitable, then delivers prioritized fixes, code-level remediation guidance, and "virtual patch" recommendations that can be applied even before a CVE is publicly disclosed. On data protection, it commits to a Zero Data Retention (ZDR) architecture — customer data is never used to train public models.

底层跑的是谁:一个关键论据支撑了"为什么要多模型"

What's Under the Hood — and the Key Argument for Why "Multi-Model" Matters

CFAD 的底层模型包括 Anthropic 的 Claude Mythos 5(官方称其为"受限能力模型",gated capability model)、OpenAI 的 GPT-5.6-Cyber,以及若干开放权重模型,由一套专有的多模型编排架构(multi-model harness)把任务路由给最合适的模型。Palo Alto Networks 在公告里给出了一个很直白的理由:"在复杂环境里,没有任何单一 AI 模型能发现超过 40% 的漏洞",而排名靠前的几个网络安全模型之间的重合率不到 10%——换句话说,只用一个模型,你会漏掉大部分本可以被发现的问题。

CFAD runs on Anthropic's Claude Mythos 5 (described officially as a "gated capability model"), OpenAI's GPT-5.6-Cyber, and several open-weight models, routed through a proprietary multi-model orchestration harness that sends each task to whichever model suits it best. Palo Alto Networks' announcement gives a blunt reason why: "no single AI model catches more than 40% of vulnerabilities in a complex environment," and the overlap between the top-performing cyber models is under 10%. In other words, relying on just one model means missing most of what could otherwise be found.

图 3:Unit 42 CFAD 的处理管线——多个模型分工协作,而不是单一模型包打天下。图源:根据 Palo Alto Networks 官方发布内容整理绘制。

Fig. 3: Unit 42 CFAD's processing pipeline — multiple models split the work, rather than one model trying to do it all. Diagram compiled from Palo Alto Networks' official announcement.

🔗 类比

🔗 Analogy

如果说 CLOSEDQUORUM 是"让四个模型投票决定攻击的下一步",Unit 42 CFAD 更像是"让不同专长的模型分头负责不同战场,再把各自的发现汇总核实"——一个是投票表决,一个是分工协作,同样是多模型,解决的问题结构完全不同。

If CLOSEDQUORUM is "four models voting on the attack's next move," Unit 42 CFAD is closer to "different specialist models each covering their own battlefield, with findings pooled and cross-checked afterward." One is a vote; the other is division of labor. Both are multi-model, but they're solving structurally different problems.

07 · 对比与选型

07 · Comparison & Positioning

⚖️对比选型:它在坐标系里的位置Comparison: Where It Sits

进攻侧:三代 AI 恶意软件的自主程度光谱

Offense Side: The Autonomy Spectrum Across Three Generations of AI Malware

把 PromptLock、GTG-1002、CLOSEDQUORUM 放在同一张表里看,能看清"AI 自主性"到底是沿着哪个维度在推进——不是"更聪明",而是"更少依赖人类做决定"。

Lining up PromptLock, GTG-1002, and CLOSEDQUORUM shows exactly which dimension "AI autonomy" has been advancing along — not "smarter," but "less dependent on a human to make the call."

案例	时间	AI 承担的角色	人类的角色	是否实战部署
PromptLock	2025-08	现场生成攻击代码(本地单模型)	仍需人类触发、部署、决策	未观察到,判定为 PoC
GTG-1002	2025-11	自主执行约 80~90% 的战术操作(单一 Agent)	选定目标、审批关键节点	是,真实间谍活动,后被 Anthropic 阻断
CLOSEDQUORUM	2026-09	对"下一步做什么"进行投票决策(四模型)	仅负责前期编译投放,执行阶段完全不在环	未观察到,样本含占位凭据,判定为早期原型

Case	Date	AI's role	Human's role	Deployed in the wild?
PromptLock	2025-08	Generates attack code on the fly (single local model)	Still triggers, deploys, and decides	Not observed; judged a PoC
GTG-1002	2025-11	Autonomously executes ~80-90% of tactical operations (single agent)	Selects targets, approves key checkpoints	Yes — real espionage campaign, later disrupted by Anthropic
CLOSEDQUORUM	2026-09	Votes on "what to do next" (four models)	Only builds/deploys upfront; entirely out of the loop during execution	Not observed; sample has placeholder creds, judged an early prototype

值得注意的是,GTG-1002 目前仍是三者中唯一被确认真实发生过、造成实际影响的案例——它的"自主程度"反而不如 CLOSEDQUORUM 那么彻底(人类还保留目标选择权),但破坏力是真实的。这提醒我们:评估一个 AI 恶意软件案例时,"自主程度有多高"和"现实威胁有多大"是两条不完全重合的坐标轴,不能因为 CLOSEDQUORUM 的架构更"炫"就认为它比 GTG-1002 更危险。

It's worth noting that GTG-1002 remains the only one of the three confirmed to have actually happened and caused real impact — even though its autonomy is technically less complete than CLOSEDQUORUM's (humans still picked the targets), its damage was real. That's a reminder that "how autonomous is it" and "how dangerous is it right now" are two axes that don't fully overlap — CLOSEDQUORUM's architecture being more novel doesn't automatically make it more dangerous than GTG-1002 today.

防御侧:CFAD 与另外两个主流 AI 安全平台的定位差异

Defense Side: How CFAD Differs From Two Other Mainstream AI Security Platforms

Unit 42 CFAD 不是这个赛道上唯一的选手。CrowdStrike 的 Charlotte AI 和 Microsoft 的 Security Copilot 都在做"智能体式安全",但三者切入点并不相同——理解这个差异,能帮企业判断自己更需要哪一类工具。

Unit 42 CFAD isn't the only player in this space. CrowdStrike's Charlotte AI and Microsoft's Security Copilot both build "agentic security" tools, but the three take different angles of attack — understanding the difference helps a company figure out which one it actually needs.

产品	核心定位	官方披露的能力要点
Unit 42 CFAD	持续攻击面验证与渗透测试	用多模型编排发现并验证可被利用的漏洞,给出优先级修复建议;强调"没有单一模型能覆盖全部漏洞类型"
CrowdStrike Charlotte AI	SOC(安全运营中心)日常运营的智能体分析师	原生构建在 Falcon 平台上,多智能体架构;官方称能以 98%+ 准确率自动分诊告警,减少约 70% 的人工工作量、将响应时间缩短约 90%;允许团队"自选底层模型"接入自建智能体
Microsoft Security Copilot	跨 Defender / Entra / Intune / Purview 等产品线的智能体自动化	内嵌智能体分别处理钓鱼邮件分诊、漏洞修复等具体任务;官方描述为"专用安全语言模型",未公开具体底层模型名称

Product	Core positioning	Officially disclosed capabilities
Unit 42 CFAD	Continuous attack-surface validation and penetration testing	Uses multi-model orchestration to find and validate exploitable vulnerabilities, then prioritizes fixes; emphasizes that "no single model covers every vulnerability class"
CrowdStrike Charlotte AI	Agentic analyst for day-to-day SOC operations	Built natively on the Falcon platform with a multi-agent architecture; claims >98% accuracy auto-triaging detections, ~70% reduction in manual effort, ~90% faster response times; lets teams build custom agents on "the model of your choice"
Microsoft Security Copilot	Agentic automation embedded across Defender / Entra / Intune / Purview	Embedded agents handle specific tasks like phishing triage and vulnerability remediation; described officially as a "specialized security language model," with no specific underlying model named publicly

三行对比均来自各厂商官方产品页面或官方博客(见 footer 来源清单),未使用第三方评测数字。

All three rows are sourced from each vendor's own product page or official blog (see the source list in the footer); no third-party benchmark figures were used.

08 · 数据说话

08 · By the Numbers

📊实战数据Real-World Numbers

CLOSEDQUORUM 目前没有真实部署数据(样本本身就是未激活的占位模板),但 Unit 42 CFAD 已经披露了一批来自内部使用和 100 多个客户实际交付的数字,可以作为"AI 原生防御到底有没有用"的参考:

CLOSEDQUORUM has no real-world deployment data yet (the sample itself is an inactive placeholder template), but Unit 42 CFAD has published a set of figures from its own internal use and 100+ real customer engagements — useful evidence for whether "AI-native defense" actually delivers:

100+

客户部署案例

customer engagements

3.2×

高危/严重漏洞发现量(对比传统测试)

more high/critical vulns found vs. legacy testing

51%

平均修复时间(MTTR)缩短

reduction in mean time to remediate

2/3

已验证漏洞在第三方应用中没有对应 CVE

validated exposures in third-party apps had no known CVE

内部部署三周内达成"相当于一年多传统渗透测试的成果量"。
100+ 客户实际交付案例中,37% 的已识别风险被评为高危或严重级别。
成功识别出无需受害者任何操作即可触发的账户接管攻击链(chained attack sequence)。

Internal deployment achieved "over a year's worth of traditional penetration testing results in just three weeks."
Across 100+ real customer engagements, 37% of identified exposures were rated high or critical severity.
Successfully identified chained attack sequences that enable account takeover without any action from the victim.

数据来源:Palo Alto Networks / Unit 42 官方发布内容(见 footer)。这些是厂商自己披露的数字,尚无独立第三方审计确认,阅读时应作为"厂商声称的效果"而非"中立评测结果"看待。

Source: Palo Alto Networks / Unit 42's official announcement (see footer). These are vendor-disclosed figures with no independent third-party audit yet, and should be read as "results the vendor claims," not "neutral benchmark results."

09 · 保持清醒

09 · Staying Level-Headed

🧯踩坑与审慎:别急着恐慌Caveats: Don't Panic Yet

这类"首个 XX"的安全新闻,历史上很容易被过度渲染。在把 CLOSEDQUORUM 当成"下一代网络武器"之前,有几件 Talos 自己在原文里就写明的事,必须放在最显眼的位置。

"First-ever" security stories like this one have a long history of getting overblown. Before treating CLOSEDQUORUM as "the next generation of cyberweapons," a few things Talos states plainly in its own writeup deserve top billing.

🚫

1. 没有观察到真实在野部署。Talos 明确写道,他们分析的是开发构建版本,公开流传的样本内置的是占位符 API Key(dummy_api_key)和虚拟 Webhook 地址,根本无法真正运行——这更像是一个"能力验证模板",要被真正用于攻击,还需要攻击者自行编译、填入真实凭据。

1. No confirmed in-the-wild deployment. Talos states plainly that what it analyzed were development builds, and the publicly circulating sample ships with a placeholder API key (dummy_api_key) and a dummy webhook address — it simply cannot run as-is. It reads more like a "capability-proof template" that an attacker would need to recompile with real credentials before it could be used in a real attack.

🔬

2. 只做了静态分析,没有完整跑通一次端到端执行。Talos 原文写得很直接:"我们没有观察到这套架构的完整端到端执行过程"。也就是说,四模型投票在真实攻防环境下是否真的稳定可用、模型会不会给出荒谬或前后矛盾的决策,目前没有实测数据支撑,只有代码层面的静态推断。

2. Only static analysis was done — no full end-to-end run was observed. Talos's own text is direct: "we did not observe a complete end-to-end execution of the architecture." In other words, whether the four-model vote actually holds up reliably in a live attack environment, or whether models might produce absurd or contradictory decisions, is currently unsupported by any runtime evidence — only static, code-level inference.

🌐

3. 架构本身依赖商业云 API,这既是特点也是弱点。无论是 CLOSEDQUORUM 的四模型投票,还是 Unit 42 CFAD 的多模型编排,都要求持续调用第三方 LLM 服务的接口。这意味着限速、服务中断、API 返回格式不符合预期,都可能直接打断整条决策链——对攻击者而言这是"单点故障",对防御者反而是一个潜在的检测面(比如监控异常的 LLM API 调用流量)。

3. The whole architecture leans on commercial cloud APIs — a feature and a weakness at once. Whether it's CLOSEDQUORUM's four-model vote or Unit 42 CFAD's multi-model orchestration, both require continuous calls to third-party LLM endpoints. That means rate limiting, service outages, or malformed API responses can directly break the decision chain. For an attacker this is a single point of failure; for a defender it's actually a potential detection surface — for instance, by monitoring for anomalous LLM API traffic.

📜

4. 历史模式值得参照:PromptLock 走过同样的路。2025 年 8 月被吹捧为"首个 AI 勒索软件"的 PromptLock,一年多后仍然从未在真实攻击中现身。CLOSEDQUORUM 目前所处的阶段,和 PromptLock 当年被发现时高度相似——都是被安全厂商在研究中主动发现、都缺乏真实受害者、都被厂商自己定性为"早期/有限的例子"。这不代表威胁不存在,而是提醒读者:发现一个概念验证,和这个概念验证被大规模武器化,中间还有相当长的距离。

4. A historical pattern worth watching: PromptLock walked this exact road. PromptLock, hailed in August 2025 as "the first AI-powered ransomware," has still never appeared in a real attack more than a year later. CLOSEDQUORUM today sits in a strikingly similar spot to where PromptLock was when it was discovered — found proactively by a security vendor's own research, no confirmed victims, and characterized by the vendor itself as an "early/limited example." That doesn't mean the threat isn't real; it's a reminder that there's a meaningful gap between discovering a proof-of-concept and that proof-of-concept becoming a weaponized, widely-used tool.

"CLOSEDQUORUM 是一个早期且有限的例子,但它让一个正在浮现的威胁模型变得具体可见……我们现在有一个开放的窗口期,可以研究这场转变,目的是在自主化操作变得更强大之前,先开发出所需的检测手段、控制措施和响应策略。" —— Ryan Fetterman,Cisco Talos 安全研究员,2026-09-22

"CLOSEDQUORUM is an early and limited example, but it makes an emerging threat model concrete... We have an open window to study this transition, with the aim of developing the detections, controls, and response strategies needed before autonomous operations become more capable." — Ryan Fetterman, Cisco Talos security researcher, 2026-09-22

换句话说,Talos 自己给出的定性,与其说是"警报已经拉响",不如说是"窗口期正在开启,现在动手研究还来得及"。这也是这篇文档把研究基调定为"审慎关注"而非"紧急预警"的原因。

In other words, Talos's own framing is less "the alarm is already ringing" and more "a window is opening, and there's still time to act if you start now." That's why this document's tone is "watch carefully" rather than "sound the emergency alarm."

10 · 场景落地

10 · Practical Scenarios

🧭什么场景该关心这件事Who Should Care, and Why

👤 如果你是企业安全团队 / 蓝队

👤 If You're an Enterprise Security / Blue Team

是什么

CLOSEDQUORUM 的检测特征(YARA 规则、IOC)已经随 Talos 报告公开,可以直接导入现有检测体系;CAIRN 则可以用来周期性扫描你所在行业相关的样本库,提前发现类似手法的新变种。

CLOSEDQUORUM's detection signatures (YARA rules, IOCs) were published alongside the Talos report and can be imported directly into existing detection stacks; CAIRN can be run periodically against samples relevant to your industry to catch new variants of the same technique early.

为什么

即便这个具体样本还没在野外出现,它公开的架构思路(多模型投票决策)几乎必然会被其他攻击者模仿改造——现在补齐检测能力,成本远低于事后应急响应。

Even though this specific sample hasn't shown up in the wild, its publicly documented architecture (multi-model voting for decisions) will almost certainly be copied and adapted by other attackers. Building detection capability now is far cheaper than incident response after the fact.

怎么做

在网络出口监控层面,对 api.deepseek.com、api.mistral.ai 等 LLM 服务商域名与 Discord Webhook 域名的异常调用模式建立基线告警,尤其关注非开发/非业务机器上出现的此类流量。

At the network egress layer, build baseline alerting for unusual call patterns to LLM provider domains like api.deepseek.com and api.mistral.ai, as well as Discord webhook domains — especially traffic like this coming from machines that aren't dev boxes or business systems.

🏢 如果你是在评估要不要买 AI 驱动的安全服务

🏢 If You're Evaluating Whether to Buy an AI-Driven Security Service

是什么

Unit 42 CFAD 代表的是"持续验证型"服务,和一次性渗透测试、以及 Charlotte AI / Security Copilot 这类"日常运营型"智能体产品定位不同,采购前要先想清楚自己缺的是哪一类能力。

Unit 42 CFAD represents a "continuous validation" service, positioned differently from a one-off pentest engagement, and different again from "day-to-day operations" agentic products like Charlotte AI or Security Copilot. Before buying, be clear on which capability gap you're actually trying to fill.

为什么

厂商披露的效果数字(如 CFAD 的 3.2 倍漏洞发现量、51% MTTR 缩短)都来自厂商自身或其客户案例,尚无独立第三方复核,采购决策不应只看这些数字,还要结合自己环境的 PoC 试用结果。

Vendor-disclosed metrics (CFAD's 3.2x more vulnerabilities found, 51% MTTR reduction) come from the vendor itself or its own customer cases, with no independent third-party review yet. Purchase decisions shouldn't rest on these numbers alone — run your own PoC in your environment first.

怎么做

重点追问数据保护条款(是否零数据留存/ZDR)、底层模型是否可指定或排除、以及"验证可利用性"这一步具体如何工作——这三点是区分"真的做了验证"和"只是又一个漏洞扫描器套壳"的关键。

Push specifically on data-protection terms (zero data retention or not), whether you can specify or exclude certain underlying models, and exactly how the "prove exploitability" step actually works — these three questions separate genuine validation from "just another vulnerability scanner with an AI wrapper."

🧑‍💻 如果你只是对这个趋势感兴趣,想跟上大方向

🧑‍💻 If You're Just Trying to Follow the Bigger Trend

是什么

记住一条主线就够了:AI 在攻防两端的渗透,正在从"辅助人类做事更快"(2025 年初的常态),经过"辅助人类做战术执行"(GTG-1002),走向"辅助人类做决策,甚至替代人类做决策"(CLOSEDQUORUM、CFAD)。

One throughline is enough to remember: AI's penetration into both offense and defense is moving from "helping humans do things faster" (the norm in early 2025), through "helping humans execute tactics" (GTG-1002), toward "helping humans decide — or even deciding in their place" (CLOSEDQUORUM, CFAD).

为什么

这条主线比任何一个具体案例的"是否被夸大"更重要——即使 CLOSEDQUORUM 本身最终被证明只是一个昙花一现的原型,决策权向 AI 转移的大方向也不会因此逆转。

This throughline matters more than whether any single case turns out to be overhyped — even if CLOSEDQUORUM itself ends up being a flash-in-the-pan prototype, the broader shift of decision-making toward AI won't reverse because of it.

11 · 延伸阅读

11 · Further Reading

📚学习资源清单Further Reading

Cisco Talos 官方博客:《The Closed Quorum: Inside the first reported autonomous AI C2 implant》—— 技术细节最全的一手来源。
Cisco Talos 官方博客:《Introducing CAIRN: Frontier tracking for AI-integrated malware》—— CAIRN 工具的设计理念与方法论。
GitHub:Cisco-Talos/CAIRN —— 工具源码、安装文档、规则库。
Palo Alto Networks 官方博客:《Introducing Unit 42 Continuous Frontier AI Defense》—— CFAD 服务详情。
Anthropic 披露报告:《Disrupting the first reported AI-orchestrated cyber espionage campaign》(GTG-1002)—— 理解"AI 执行战术"阶段的一手材料。
ESET welivesecurity:《First known AI-powered ransomware uncovered by ESET Research》(PromptLock)—— 理解"AI 现场编写攻击代码"阶段的一手材料。

Cisco Talos official blog: "The Closed Quorum: Inside the first reported autonomous AI C2 implant" — the most complete primary technical source.
Cisco Talos official blog: "Introducing CAIRN: Frontier tracking for AI-integrated malware" — CAIRN's design philosophy and methodology.
GitHub: Cisco-Talos/CAIRN — tool source code, install docs, rule library.
Palo Alto Networks official blog: "Introducing Unit 42 Continuous Frontier AI Defense" — full CFAD service details.
Anthropic disclosure report: "Disrupting the first reported AI-orchestrated cyber espionage campaign" (GTG-1002) — primary material on the "AI executes tactics" stage.
ESET welivesecurity: "First known AI-powered ransomware uncovered by ESET Research" (PromptLock) — primary material on the "AI writes attack code live" stage.