AI-Infra-Guard
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A full-stack AI Red Teaming platform securing AI ecosystems via Agent Scan, Skills Scan, MCPMCPModel Context Protocol, a standard for connecting AI applications to external tools and data sources. A compatible server exposes capabilities an assistant can use through a common interface. scan, AI Infra scan and LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code. jailbreak evaluation.
ai-infra-guard mcp --save-history true AI-Infra-Guard --localscan
Summary
AI-Infra-Guard is an open-source AI red teaming tool, run from the command line, that scans AI infrastructure for CVE vulnerabilities, MCP server risks, agent skill security issues, and LLM jailbreak robustness. It targets AI security teams and red teamers rather than general application developers, covering fingerprinting of components like Qdrant, Chroma, and Weaviate, detection rules for MCP threats such as tool poisoning and command injectioncommand injectionAn attack that makes an application execute operating-system commands supplied through untrusted input. It often happens when input is passed unsafely to a shell., and jailbreak methods including Many-Shot, PAIR, GOAT, and ActorAttack. Its Agent-Scan and MCP-Scan modules run standalone or integrated into the wider platform, and Skill-Scan audits agent skills with SARIFSARIFStatic Analysis Results Interchange Format, a standard for sharing code-analysis findings, including locations and rule details. It lets scanners report results to a common review interface. and Markdown reporting. Backed by 41 contributors and over 1,300 commits in the past year, with a release 22 days ago, it is under active development.
A full-stack AI Red Teaming platform securing AI ecosystems via Agent Scan, Skills Scan, MCP scan, AI Infra scan and LLM jailbreak evaluation.
How AI-Infra-Guard helps
Which AI infrastructure components in my stack have known CVEs?
fingerprints deployed services such as Qdrant, Chroma, and Weaviate along with model APIs, then matches them against a maintained vulnerability rule set and reports which versions are affected
Which of my MCP servers are exposed to tool poisoning, credential exfiltration, or command injection?
runs its MCP detection rules against configured servers and flags the specific threat pattern each one matches
Can my deployed model resist known jailbreak techniques?
drives the model through multi-turn attack methods such as Many-Shot, PAIR, GOAT, and ActorAttack and reports which prompts broke its guardrails
Which of my agent skills introduce security issues before they run in production?
audits a skill project's code and reports findings as SARIF or Markdown, naming the file and issue for each
Are my autonomous agents vulnerable to loss-of-control or unsafe handoffs between agents?
runs them through deterministic benchmarks built for these scenarios and reports where control or trust boundaries failed
Which of my agents are exposed to supply-chain, cascading-failure, or unexpected-code-execution risks?
checks agent configurations against OWASP-aligned detection skills and names the risk category each finding falls under
Languages
Command line
No command matches.
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Values are placeholders taken from each option’s declared default. Nothing is executed here — the output shown is a recording of a run that already happened.
Score card
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Shipping velocity releases a month, last 12 months
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Documentation quality release-notes rubric, 0–100
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Breaking-change discipline how rarely a release breaks users, and notice given
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Maintenance risk quiet time against its own cadence, maintainers
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Innovation & surface new capabilities, API and MCP surface
- Community health
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Contributor trend distinct committers, this year against last
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Maintainer concentration bus factor now against a year ago
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Issue backlog trend issues closed per issue opened, 12 months
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Responsiveness median time to a maintainer’s first reply
Nine scores for AI-Infra-Guard, each built from the releases, notes and API history this site tracks — and, on Researcher and Business, where it stands among every measured tool in its category and how that has moved.
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Blank means never measured; nothing here is a filled-in zero.
Peer standings are included with Researcher and Business. See how this tool ranks against its competitors, using data we don't publish anywhere else.
See plans →Each score is 0–100 on a published rubric, derived from data this site already shows. A dimension we cannot measure says so — it is never scored 0 and never enters a rank.
How this is scored
- Shipping velocity — releases a month over the last 12 months, on a log scale where 16 a month scores 100. Needs 3 releases in the window.
- Documentation quality — the release-notes rubric published on the analytics page, unchanged.
- Breaking-change discipline — the share of releases carrying a breaking change (none scores 100, half or more scores 0). Only projects that label breaking changes are measured: a clean record cannot be told from an unlabelled one.
- Maintenance risk — quiet time measured against the tool's own release cadence (quiet longer than 90 days and three typical gaps scores 0), plus the number of active maintainers and whether contributors are growing, where known. Higher is lower risk.
- Innovation & surface — new capabilities in the feature ledger this year, API and MCP operations added, and the share of commits that are features: the mean of whichever we hold.
- Community health (open-source tools only) — each is a direction, not a size: contributors this year against last, the bus factor now against a year ago, and issues closed per issue opened, where steady scores 50, doubled 100 and halved 0; plus the median time to a maintainer's first reply (an hour scores about 100, a month or more 0). A commercial card has no repository and carries none of the four.
- Documented delivery (commercial tools) — when a vendor's documented new capabilities have slowed against its own baseline, that counts against the overall standing as heavily as two low dimensions. Steady delivery, or too little history to judge, leaves the standing alone; it is never a sixth score.
- Open-source tools are scored on repository signals; commercial tools on their published changelog. Ranks compare tools within one category and one profile and need at least five measured peers; the overall standing needs three ranked dimensions. Best-in-breed or consolidator is a shape, not a quality, and is never scored.
Release-note detail over time
See how much detail AI-Infra-Guard puts in its release notes, quarter by quarter, and compare it with tracked tools in the same category. This history uses up to ten retained, reviewed notes per tool; sparse or blank older quarters may reflect that limit.
Checking release-note history…
Coverage
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What AI-Infra-Guard connects to, runs on and detects, read from its release notes and docs. So far: 2 new integrations, 1 new detection.
3 changes, tracked since Oct 15, 2026.
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Oct 152026
new Memory/RAG poisoning detection skill
Add memory/RAG poisoning detection skill (PR #429, a25426cb)
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Oct 152026
new Atlas Cloud provider preset for Agent-Scan
Add Atlas Cloud provider preset (PR #706, 6a14a56a)
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Oct 152026
new API Route provider preset for Agent-Scan
Add API Route provider preset (PR #697, c752770e)
Cloud platforms & services 2
Nothing first seen in this window. That doesn't mean AI-Infra-Guard shipped nothing, only that we found no record of it.
How we count
Read from 2 of 40 release notes and docs updates we hold for AI-Infra-Guard. "First seen" is the earliest note that names something; "last" is the latest. Our history may start partway through AI-Infra-Guard's life, so an old integration can show a recent first-seen date.
Each change counts once: "added 40 checks" is one line, so the numbers measure how often coverage moves, not how big the catalogue is or how well it detects.
Releases
40 entries
No entries match these filters.
- October 20264
- September 20263
- August 20262
- July 20262
- June 20265
- May 20262
- April 20263
- March 20263
- February 20261
- January 20261
- December 20252
- October 20251
- September 20251
- August 20251
- June 20252
- April 20251
- March 20251
- February 20254
- January 20251
Project health
Issue, contributor and maintainer activity in Tencent/AI-Infra-Guard, 2023-10 – 2026-09, as of 2026-09-25. Twelve-month figures first; the trend lines run the whole window.
- Contributors 51 +37 vs prior 12 months
- Maintainers 8 +8 joined · −1 left, last 12 months
- Bus factor 3 committers behind half the commits
- Community-filed 92% of issues opened, last 12 months
- Median time to close 2.1 days first maintainer reply 3.9 hours
- Open backlog 15 of 142 issues ever filed
- Issues opened / month 8 · 0 communitymaintainers
- Open backlog 15
- Contributors / month 15
- Commits / month 44
- Stars 6,438
- bugs 16%
- features 29%
- questions 10%
- docs 3%
- other 42%
Counts from the project's public repository and git history; no individuals are identified. Maintainers are the committers covering 80% of the last year's commits. how we measure this
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