tencent/ai-infra-guard — explained in plain English
Analysis updated 2026-07-03
Scan your AI model server setup for known security vulnerabilities before deploying to production.
Test an AI agent and its connected tools for security weaknesses your team might have missed.
Run systematic jailbreak tests against an AI model to measure how resistant it is to producing restricted outputs.
| tencent/ai-infra-guard | belval/textrecognitiondatagenerator | fo40225/tensorflow-windows-wheel | |
|---|---|---|---|
| Stars | 3,671 | 3,671 | 3,673 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | easy | easy |
| Complexity | 3/5 | 2/5 | 1/5 |
| Audience | ops devops | data | data |
Figures from each repo's GitHub metadata at analysis time.
No built-in authentication, deploy only on a private network, never on a public-facing server.
AI-Infra-Guard is a security testing platform built by Tencent's Zhuque Lab that helps teams find weaknesses in AI systems before attackers do. The idea is that as companies deploy AI models, AI agents, and related infrastructure, those systems introduce new kinds of security risks that traditional scanning tools were not built to catch. The platform bundles several scanning modes into one place. One module scans AI infrastructure components for known vulnerabilities, covering over 58 tools like model servers, agent frameworks, and databases. Another module tests AI agents and their tool integrations, called MCP servers, for security issues. A third module runs jailbreak evaluations, which means it systematically tries to get AI models to produce outputs they are supposed to refuse, then measures how often that succeeds. To run it yourself, you start it with Docker and access a web interface at port 8088. A one-line install script is also provided for convenience. The README notes the platform has no built-in authentication, so it is meant for internal or private network use only, not for public-facing deployments. The project was presented at Black Hat Europe 2025 and is actively updated, with changelog entries showing weekly or biweekly releases that expand the vulnerability database and add coverage for new AI components.
A security scanner from Tencent that finds weaknesses in AI systems, checks over 58 AI tools and servers for known vulnerabilities, tests AI agents and their integrations, and measures how often AI models can be tricked into breaking their own rules.
Mainly Python. The stack also includes Python, Docker.
License not mentioned in the explanation.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly ops devops.
This repo across BitVibe Labs
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