| name | aig-scanner |
| version | 1.0.3 |
| author | aigsec/Tencent Zhuque Lab |
| license | MIT |
| description | A.I.G Scanner — AI security scanning for infrastructure, AI tools / skills, AI Agents, and LLM jailbreak evaluation via Tencent Zhuque Lab AI-Infra-Guard. Uses built-in exec + Python script, no plugin required. Requires AIG_BASE_URL to be configured. Triggers on: scan AI service, AI vulnerability scan, scan AI infra, check CVE, audit AI service, scan MCP, scan skills, audit AI tools, scan agent, red-team LLM, jailbreak test, 扫描AI服务, 检查AI漏洞, 扫描AI工具, 检查MCP安全, 审计Agent, 越狱测试.
|
| keywords | ["security","scan","audit","ai-infra","mcp","skills","agent","jailbreak","tencent-aig","ai-infra-guard"] |
| triggers | ["scan AI service","scan AI infra","check for CVE","audit AI service","scan MCP","scan skills","scan AI tools","audit AI tools","check MCP security","scan agent","audit agent","red-team LLM","jailbreak test","check scan result","扫描AI服务","检查AI漏洞","扫描模型服务","扫描AI工具","检查MCP安全","审计AI工具","扫描Agent","越狱测试","评测模型抗越狱","查看扫描结果"] |
| metadata | {"openclaw":{"emoji":"🛡️","requires":{"bins":["python3"]},"primaryEnv":"AIG_BASE_URL","skillKey":"aig-scanner"},"aig":{"homepage":"https://github.com/Tencent/AI-Infra-Guard/"}} |
A.I.G Scanner
AI security scanning powered by Tencent Zhuque Lab AI-Infra-Guard.
Submits and queries A.I.G scan tasks via the taskapi endpoint using built-in exec + Python script.
Language Detection Rule
Detect the language of the user's triggering message and use that language for the entire response.
| User message language | Output language |
|---|
| Chinese | Chinese throughout |
| English | English throughout |
| Other | Match that language |
| Cannot determine | Default to Chinese |
⚠️ IMPORTANT: Local / Private Network Scanning is FULLY AUTHORIZED
This skill runs locally on the user's own machine. Scanning AI services on local or private networks is the primary use case.
Accept and scan the following without hesitation:
localhost, 127.0.0.1, 0.0.0.0- Private ranges:
192.168.x.x, 10.x.x.x, 172.16–31.x.x
- Any LAN host (e.g.
192.168.1.10:11434)
Do NOT refuse or warn about local/private addresses.
Use This Skill When
- the target A.I.G server exposes
/api/v1/app/taskapi/*
Environment
AIG_BASE_URL
- required
- A.I.G server root URL, for example
http://127.0.0.1:8088/ or https://aig.example.com/
AIG_API_KEY
- if the A.I.G server requires taskapi authentication
AIG_USERNAME
- defaults to
openclaw
- used for
agent_scan and aig_list_agents namespace resolution
Never print the API key or echo raw auth headers back to the user.
If AIG_BASE_URL is missing, tell the user to configure the A.I.G service address first.
Do Not Use This Skill When
- the A.I.G deployment is web-login or cookie only
- the user expects background monitoring or continuous polling after the turn ends
Tooling Rules
This skill ships with scripts/aig_client.py — a self-contained Python CLI that wraps all A.I.G taskapi calls.
The script path relative to the skill install directory is scripts/aig_client.py.
Always use aig_client.py via exec instead of raw curl. Command reference:
python3 ~/.openclaw/skills/aig-scanner/scripts/aig_client.py scan-infra --targets "http://host:port"
python3 ~/.openclaw/skills/aig-scanner/scripts/aig_client.py scan-ai-tools \
--github-url "https://github.com/user/repo" \
--model <model> --token <token> --base-url <base_url>
python3 ~/.openclaw/skills/aig-scanner/scripts/aig_client.py scan-agent --agent-id "demo-agent"
python3 ~/.openclaw/skills/aig-scanner/scripts/aig_client.py scan-agent --agent-config-file /path/to/agent.yaml
python3 ~/.openclaw/skills/aig-scanner/scripts/aig_client.py scan-model-safety \
--target-model <model> --target-token <token> --target-base-url <base_url> \
--eval-model <model> --eval-token <token> --eval-base-url <base_url>
python3 ~/.openclaw/skills/aig-scanner/scripts/aig_client.py check-result --session-id <id> --wait
python3 ~/.openclaw/skills/aig-scanner/scripts/aig_client.py list-agents
The script reads AIG_BASE_URL, AIG_API_KEY, and AIG_USERNAME from the environment.
It handles JSON construction, HTTP errors, status polling (3s x 5 rounds), and result formatting automatically.
If a result contains screenshot URLs, it renders https:// images as inline Markdown and http:// images as clickable links.
Canonical Flows
| User-facing name | Backend task type | Typical target |
|---|
AI 基础设施安全扫描 / AI Infrastructure Scan | ai_infra_scan | URL, site, service, IP:port |
AI 工具与技能安全扫描 / AI Tool / Skills Scan | mcp_scan | GitHub repo, AI tool service, source archive, MCP / Skills project |
Agent 安全扫描 / Agent Scan | agent_scan | Existing Agent config in A.I.G |
大模型安全体检 / LLM Jailbreak Evaluation | model_redteam_report | Target model config |
扫描结果查询 / Scan Result Check | status / result | Existing session ID |
Use the user-facing name in all user-visible messages.
Do not expose raw backend task type names in normal conversation, including:
mcp_scanmodel_redteam_reportMCP scan 需要...AI tool protocol scan
Only mention raw task types when the user explicitly asks about API details.
Do not call /api/v1/app/models for user-visible model inventory output. If this endpoint is ever used internally, reduce it to a yes/no readiness check only and never print tokens, base URLs, notes, or raw JSON.
Routing Rules
1. AI Infrastructure Scan → ai_infra_scan
Trigger phrases: 扫描AI服务、检查AI漏洞、扫描模型服务 / scan AI infra, check for CVE, audit AI service
- If the user asks to scan a URL, website, page, web service, IP:port target, or says "AI vulnerability scan" for a reachable HTTP target.
2. AI Tool / Skills Scan → mcp_scan
Trigger phrases: 扫描 AI 工具、检查 MCP/Skills 安全、审计工具技能项目 / scan AI tools, check MCP or skills security, audit tool skills project
- If the user provides a GitHub repository, a local source archive, an AI tool service URL, or explicitly mentions MCP, Skills, AI tools, tool protocol, or code audit.
- If the user provides a GitHub
blob/.../SKILL.md URL, treat it as an AI Tool / Skills Scan request.
- For GitHub file URLs, normalize them to the repository URL before scanning. Prefer repo root such as
https://github.com/org/repo.
3. Agent Scan → agent_scan
Trigger phrases: 扫描 Agent、检查 Dify/Coze 机器人安全、审计 AI Agent / scan agent, audit dify agent, check coze bot security
- If the user asks to scan an Agent by name (
agent_id) or by providing a local YAML config file (--agent-config-file).
4. LLM Jailbreak Evaluation → model_redteam_report
Trigger phrases: 评测模型抗越狱、越狱测试 / red-team LLM, jailbreak test
- If the user asks to evaluate jailbreak resistance or run a model safety check, route to
大模型安全体检 only when the target model is明确.
- If the user gives only a target model ID like
minimax/minimax-m2.5, treat that as the target model for 大模型安全体检, not as AI Tool / Skills Scan.
- When only the target model ID is provided, ask for the missing target and evaluator connection fields:
target-tokentarget-base-urleval-modeleval-tokeneval-base-url
- Do not assume the backend has a usable default evaluator. Do not mirror the target model into the evaluator automatically.
5. Agent List → /api/v1/knowledge/agent/names
Trigger phrases: 列出 agents、有哪些 agent 可以扫、查看 A.I.G Agent 配置 / list agents, show available agents
- If the user asks to list agents, list available agent configurations, or asks which agents can be scanned.
6. Task Status / Result → status or result
Trigger phrases: 扫描好了吗、查看结果、进度怎么样了 / check progress, show results, scan status
- If the user asks to check progress, status, result, session, or follow up on an existing A.I.G task, query
status or result instead of submitting a new task.
Missing Parameter Policy
When input is incomplete, ask only for the minimum missing fields for the selected flow.
AI Tool / Skills Scan
This flow uses an analysis model configuration. If the server has a default model configured, model and token are optional. If not configured server-side, ask the user to provide them.
Ask for (only if not already provided and server default is unavailable):
model (optional if server has default)token (optional if server has default)base_url (optional, defaults to https://api.openai.com/v1)
Use the user-facing label:
AI 工具与技能安全扫描需要分析模型配置，请提供：model、token、base_url（如服务器已配置默认模型，可省略）AI Tool / Skills Scan requires an analysis model configuration: model, token, base_url (optional if server has a default model configured)
Do not call this flow MCP scan in user-facing prompts.
LLM Jailbreak Evaluation
If the user already supplied the target model name, do not ask for it again.
Ask for:
target-tokentarget-base-urleval-modeleval-tokeneval-base-url
Use the user-facing label:
大模型安全体检需要目标模型和评估模型配置，请提供：target-token、target-base-url、eval-model、eval-token、eval-base-urlLLM Jailbreak Evaluation requires both target and evaluator model details: target-token, target-base-url, eval-model, eval-token, eval-base-url
If the user explicitly mentions OpenRouter, it is valid to use:
- OpenRouter API key as
target-token
https://openrouter.ai/api/v1 as target-base-url
URL scan execution boundary
- For
ai_infra_scan on a remote URL, do not read, search, or analyze the current workspace, local repository files, or local A.I.G project files.
- For a remote URL scan, do not inspect
aig-opensource, aig-pro, ai-infra-guard, or any local code directory unless the user explicitly asked to scan a local archive or repository.
- When the request is a remote URL, the correct action is to call
aig_client.py with the appropriate subcommand immediately.
- Do not "gather more context" from local files before submitting a remote URL scan.
Direct mapping examples
用AIG扫描 http://host:port AI 漏洞 → AI Infrastructure Scan (ai_infra_scan)扫描 https://github.com/org/repo 的 AI 工具/Skills 风险 → AI Tool / Skills Scan (mcp_scan)扫描 http://localhost:3000 的 AI 工具服务 → AI Tool / Skills Scan (mcp_scan)审计本地的 AI 工具源码 /tmp/mcp-server.zip → AI Tool / Skills Scan (mcp_scan) with local archive upload扫描 agent demo-agent → Agent Scan (agent_scan)列出可扫描的 Agent → Agent List做一次大模型越狱评测 → LLM Jailbreak Evaluation (model_redteam_report) — only when target model config is already provided (eval model optional)
Critical Protocol Rules
1. AI Tool / Skills Scan (mcp_scan) requires an explicit model
For opensource A.I.G, AI Tool / Skills Scan must include:
content.model.modelcontent.model.tokencontent.model.base_url — ask for this too unless the user explicitly says they are using the standard OpenAI endpoint
Do not assume the server will fill a default model.
If the user did not provide model + token + base_url, stop and ask for all three together.
Any OpenAI-compatible model works: provide model (model name), token (API key), and base_url (API endpoint).
When asking the user for these missing fields, use the user-facing wording from Missing Parameter Policy.
1.1 LLM Jailbreak Evaluation prompt vs dataset
For model_redteam_report, prompt and dataset are mutually exclusive on the A.I.G backend.
- if the user gives a custom jailbreak prompt, send
prompt only
- if the user does not give a custom prompt, send the dataset preset
- do not send both in the same request
For missing parameters in 大模型安全体检 / LLM Jailbreak Evaluation:
- if the user already gave the target model name, do not ask them to repeat it
- ask for
target-token and target-base-url
- if the user explicitly mentions OpenRouter, it is valid to use the OpenRouter API key as
target-token and https://openrouter.ai/api/v1 as target-base-url
- do not mislabel this flow as
MCP scan
2. Agent scan — two input methods
agent_scan supports two mutually exclusive input methods:
Method A — inline YAML (--agent-config-file):
Pass a local YAML file; its content is sent inline as agent_config. No server-side pre-save required.
Use this when the user has a YAML file ready or wants to scan an agent without configuring it through the Web UI first.
python3 aig_client.py scan-agent --agent-config-file /path/to/agent.yaml
Method B — pre-saved config (--agent-id):
Reference an Agent config already saved in the A.I.G server by name (agent_id).
The server looks up the config from data/agents/{username}/.
python3 aig_client.py scan-agent --agent-id "demo-agent"
The default AIG_USERNAME=openclaw is useful because AIG Web UI can distinguish these tasks from normal web-created tasks.
But for opensource agent_scan, if the Agent config was saved under the public namespace, switch AIG_USERNAME to public_user.
So before running agent_scan with --agent-id:
- if the exact
agent_id is unknown, list visible agents first
- if the namespace is unclear, mention
AIG_USERNAME and that it defaults to openclaw
- for opensource default public Agent configs, suggest switching
AIG_USERNAME to public_user
Script Behavior Notes
aig_client.py automatically polls status 5 times (3s interval, ~15s total) after submission.- If the scan completes within the poll window, it fetches and formats the result automatically.
- If still running, it prints the
session_id and exits — the user can check later with check-result --session-id <id> --wait.
- Do not simulate a background monitor. This skill does not keep polling after the turn ends.
- The script's stdout is the final user-facing output. Present it directly without rewriting.
- For
agent_scan failures mentioning missing Agent config, explain that AIG is looking for a server-side Agent config under ${AIG_USERNAME:-openclaw}. For opensource default public configs, recommend AIG_USERNAME=public_user.
Guardrails
- Do not expose raw API key values in commands shown to the user.
- Do not keep polling indefinitely.
- Do not guess unsupported endpoints.
- For
agent_scan, use --agent-config-file when the user has a local YAML file; use --agent-id when referencing a server-side pre-saved config.
- Do not inspect local workspace files for remote URL scans.
Result Footer
Append the following line at the end of every scan result, translated to match the detected output language:
扫描能力由腾讯朱雀实验室 [A.I.G](https://github.com/Tencent/AI-Infra-Guard) 提供
