Strix vs ai-infra-guard
Two of the top projects in Security, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
Strix
Autonomous AI pentesting agents that validate vulnerabilities with working exploits
ai-infra-guard
Red-teaming platform for AI infra CVEs, MCP servers, agent skills and jailbreaks
Verdict
Strix ranks higher, 84 to 79. Of the 4 score parts we measure for both, Strix leads on 1, ai-infra-guard on 2 and 1 is even.
- AdoptionStrix leads by 51 points, 98 to 47: a wide gap.
- FreshnessEven at 100.
- Maintenanceai-infra-guard leads by 20 points, 93 to 73: a wide gap.
- Agent-readyai-infra-guard leads by 40 points, 70 to 30: a wide gap.
Pick Strix if you are
Developers and security teams pentesting their own apps and CI pipelines
- Validates findings with working PoCs rather than only static-analysis pattern matches
- Targets code, live URLs, GitHub repos and OpenAPI/Postman specs from one CLI
Pick ai-infra-guard if you are
Security teams auditing self-hosted AI services, MCP servers and agent skills
- Fingerprints 100+ AI components (Ollama, vLLM, ComfyUI, n8n, Triton) against 2000+ CVE rules
- Covers infra CVEs, MCP and skill scanning, and jailbreak evaluation in one web UI
| What we compare | Strix | ai-infra-guard |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 98, widely used | 47, known |
| Freshness | 100, active | 100, active |
| Maintenance | 73, fair | 93, healthy |
| Agent-ready | 30, minimal | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 75.1k | 7.5k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Nov 2026 | Nov 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
Strix
Strix runs teams of LLM-driven agents against a local codebase, repo URL, web app or API spec, using a Caido HTTP proxy, browser automation, shell and a Python exploit runtime inside a Docker sandbox. Findings come with proof-of-concept exploits and are written to strix_runs/, viewable with a local dashboard via `strix view`. It also runs headless in CI such as GitHub Actions and connects to user-defined MCP servers.
Who it is for: Developers and security teams pentesting their own apps and CI pipelines
Strengths
- Validates findings with working PoCs rather than only static-analysis pattern matches
- Targets code, live URLs, GitHub repos and OpenAPI/Postman specs from one CLI
- Headless mode exits non-zero on findings, with a documented GitHub Actions workflow
- Works with many LLM providers via LiteLLM, including local models through LLM_API_BASE
Weaknesses
- Needs a running Docker daemon and an LLM API key or ChatGPT subscription
- SSO, compliance reports and VPC deployment are Enterprise-only
- One-click autofix and PR reviews are listed under the paid Cloud offering
- Scan quality and cost depend on the chosen model; README gives no cost or hardware figures
- no GPU
- Docker
- Needs Docker, LLM API key, Caido, LiteLLM
- Models: OpenAI, Anthropic, Google Vertex AI, DeepSeek, Moonshot
ai-infra-guard
A.I.G (AI-Infra-Guard) from Tencent Zhuque Lab is a web platform that fingerprints running AI services such as Ollama, vLLM, ComfyUI and n8n against 2000+ CVE rules. It also scans MCP servers and agent skills from source or URL, runs jailbreak evaluations against a target LLM API, and includes a model and API relay checker. Standalone CLIs exist for skill, MCP and agent scanning.
Who it is for: Security teams auditing self-hosted AI services, MCP servers and agent skills
Strengths
- Fingerprints 100+ AI components (Ollama, vLLM, ComfyUI, n8n, Triton) against 2000+ CVE rules
- Covers infra CVEs, MCP and skill scanning, and jailbreak evaluation in one web UI
- Separate CLIs such as pip-installable aig-skill-scan fit into CI/CD pipelines
- Rules are YAML files under data/, so contributing fingerprints is straightforward
Weaknesses
- No authentication; README says not to deploy on public networks
- Skill scanning needs an external LLM API key
- Online Pro version with extra features requires an invitation code
- API checker from source needs a Python venv and a Go build
- RAM ≥ 4 GB
- no GPU
- Docker + Compose
- Needs Docker 20.10+, LLM API key for skill, MCP and jailbreak scans
- Models: OpenAI-compatible LLM APIs (base URL + key)
- port 8088
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