Mitigant

io.github.mitigant由 GitHub 账号 mitigant 发布v0.1.0更新于 Oct 25, 2026

在AWS、Azure和GCP上运行真实的、可自动恢复的云攻击,以测试你的防御能力。

AI 翻译由 AI 模型根据下方的原文描述翻译,可能有误。

原文描述

Run real, auto-recovered cloud attacks on AWS, Azure and GCP to test your defenses.

已验证Streamable HTTP可网页运行云端与基础设施

概览

Mitigant 的托管 MCP 服务(mcp.mitigant.io);需要 API 密钥;Streamable HTTP;10 个工具。

暂无 AI 概览:以下信息来自注册中心条目和服务本身。

端点
mcp.mitigant.io
最近检查

安装

在 SourceWeft 中

  1. 打开 控制台中的 Mitigant,将其添加到工作区。
  2. 连接 apiToken 凭据。它们会被私密存储,绝不会在公开页面上展示。
  3. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.mitigant%2Fmitigant-mcp/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "mitigant": {
      "type": "http",
      "url": "https://mcp.mitigant.io/mcp",
      "headers": {
        "apiToken": "<YOUR_API_KEY>"
      }
    }
  }
}

README

该仓库没有 README。

工具

10

list_attacks

Browse Mitigant's attack actions -- atomic tests that each emulate one attacker technique -- that you can launch on their own against AWS, Azure or GCP. Currently 111 attack actions (AWS 49, Azure 36, GCP 26). Returns each action's attackName (show this to the user) and attackType (an opaque id -- pass this, not attackName, to get_attack_details or launch_attack), what it does, its impact, the MITRE ATT&CK technique it maps to, and its orchestration type (PROVISIONED or INLINE -- see orchestration_filter for what these mean). Optionally filter by cloud service (S3, IAM, ...), category, or orchestration type. Some attack actions run only as steps inside a scenario and appear via list_scenarios / get_scenario_details, not here. Start here to find an attack action to run.

输入参数cloud_service必填service_filtercategory_filterorchestration_filter

get_attack_details

Full description, impact, MITRE mapping, and orchestration type (PROVISIONED or INLINE -- PROVISIONED attacks create and destroy their own target rather than targeting your existing infrastructure (they may still read or use existing resources along the way), so they're the lower-risk, production-viable choice; INLINE attacks find and attack an existing resource in the account) for one attack, by the action key from list_attacks. Result includes attackName (show this to the user) alongside the attackType you passed in (an opaque id, not for display).

输入参数action_key必填cloud_service必填

launch_attack

Run an attack against a cloud account connected to Mitigant and return the result once it finishes. This performs real actions on real cloud infrastructure -- it is not a simulation. Confirm with the user before calling: if their request could match more than one attack in list_attacks (similar names, or an underspecified description like "the security group attack"), show them the matching candidates with their names and descriptions and have them pick one -- don't guess which attack they meant.

输入参数regionaccount_no必填action_key必填cloud_service必填

list_cloud_accounts

List the cloud accounts available for attack emulation -- any account not listed here cannot be attacked, so check this before guessing an account_no for launch_attack. An empty result means no accounts are onboarded for attack emulation (for that cloud, or at all), not a failure -- the account may still exist in Mitigant for other purposes (e.g. posture scanning) without being eligible here. Each result's accountNo is already the value launch_attack expects; accountName is for display to the user. defaultRegion is the region attacks against that account run in by default (and where their evidence is collected); it can be overridden per attack request, but unless you do, actions target that region. State it when telling the user where an attack will run.

输入参数cloud_service

list_scenarios

List Mitigant's attack scenarios -- multi-step chains that combine several attack actions run in sequence, so treat a scenario as having the combined blast radius of every step. Currently 45 scenarios (AWS 26, Azure 12, GCP 7). Returns each scenario's scenarioName (show this to the user) and scenarioKey (an opaque id -- pass this, not scenarioName, to get_scenario_details or launch_scenario), plus description, impact, and the step-by-step composition (each attack action's name, service, MITRE mapping, and impact). Use this to find a multi-step chain to run; use list_attacks instead for a single attack action.

输入参数cloud_service必填

get_scenario_details

Full details for one attack scenario -- description, impact, and the step-by-step composition, by the scenario key from list_scenarios. A scenario runs several attacks in sequence; this returns every step, not just the first. Result includes scenarioName (show this to the user) alongside the scenarioKey you passed in (an opaque id, not for display).

输入参数scenario_key必填cloud_service必填

launch_scenario

Run a multi-step attack chain against a cloud account connected to Mitigant and return the result once every step finishes. This performs real actions on real cloud infrastructure, as several attacks in sequence -- it is not a simulation, and its blast radius is the combined blast radius of every step. Confirm with the user before calling: if their request could match more than one scenario in list_scenarios, show them the matching candidates with their names and descriptions and have them pick one -- don't guess which scenario they meant.

输入参数regionaccount_no必填scenario_key必填cloud_service必填

get_attack_status

Get the current report for a previously launched attack, including its clean-up state. Returns the attack report plus a 'cleanup' summary with cleanUpComplete and a plain-language statement. Only tell the user the account is back to its pre-attack state when cleanup.cleanUpComplete is true -- while anything is pending or failed, clean-up is not done, so report it as in progress and check again rather than implying all-clear. A pending clean-up is usually the DELIBERATE inspection window, not a stall: read cleanup.statement and relay it, and poll no faster than that -- a few minutes -- rather than reporting a fault.

输入参数attack_id必填

clean_up_attack

Clean up a previously launched attack, restoring the cloud resources it changed back to their pre-attack state. Clean-up is normally AUTOMATIC after a short, deliberate inspection window (so the team can verify the attack and collect evidence first); calling this kicks it off immediately rather than waiting out that window. It is NOT a fix for a stall, and it ends that window early -- removing the attack's artifacts before the team may have finished inspecting them -- so only use it when the user explicitly wants the account restored now. This triggers clean-up and returns immediately -- clean-up runs in the background, so a success here means the request was accepted, not that clean-up is done. Confirm the account is actually restored by polling get_attack_status until cleanup.cleanUpComplete is true; do not tell the user it is restored based on this call alone.

输入参数attack_id必填cloud_service必填

get_attack_evidence

Get the evidence Mitigant collected for a previously launched attack -- what the account's own logging (CloudTrail, GuardDuty) recorded about it. Returns an 'evidence' summary (counts, sources, and a plain-language statement) plus the raw records, each with an evidenceUrl that opens the matching console entry. Use it to show the user what their defenses saw, especially during the clean-up inspection window. Important: an empty result shortly after an attack is normal -- logging delivery lags by minutes -- and an attack whose purpose is to disable logging is expected to leave none, so never report an empty result as the attack leaving no trace; re-check instead. Read evidence.statement and relay it.

输入参数attack_id必填

版本历史

1
  1. v0.1.0最新Oct 25, 2026

评分

已满足适用检查项的 75%

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