# What AI Models Does AI-Infra-Guard Support? A Complete Guide to LLM Integration

> Discover the AI models supported by AI-Infra-Guard. Integrate major LLMs like GPT-4, Claude, Gemini, and any self-hosted model with an OpenAI-compatible API.

- Repository: [Tencent/AI-Infra-Guard](https://github.com/tencent/AI-Infra-Guard)
- Tags: deep-dive
- Published: 2026-08-26

---

**AI-Infra-Guard supports major commercial LLMs including OpenAI GPT-4, Anthropic Claude, Google Gemini, Alibaba/Kimi, DeepSeek, and Zhipu AI GLM, plus any self-hosted model with an OpenAI-compatible REST API endpoint.**

Tencent's AI-Infra-Guard (A.I.G) is an open-source AI infrastructure security scanning platform designed to integrate seamlessly with diverse large language models. The architecture treats any model accessible via a standard API endpoint as a first-class citizen, allowing security teams to leverage both cloud-based providers and private deployments for vulnerability detection tasks.

## Commercial LLM Providers Supported by AI-Infra-Guard

The platform ships with pre-configured support for the industry's leading LLM families. These integrations are defined in the source code and validated through the `aig-skill-scan` benchmark suite.

### OpenAI Models

According to [`common/utils/models/openai.go`](https://github.com/Tencent/AI-Infra-Guard/blob/main/common/utils/models/openai.go), AI-Infra-Guard natively supports OpenAI's model family including `gpt-4` and `gpt-3.5-turbo`. The implementation uses a standardized struct that expects `model`, `token`, and `base_url` parameters, making it compatible with both OpenAI's official API and compatible proxies.

### Anthropic Claude

The README's performance benchmark table lists `claude-opus-4.6` as a tested and validated model, achieving an F1 score of **0.9848** with high precision (0.9725) and recall (0.9974) in security scanning tasks.

### Google Gemini

`gemini-3.5-flash` is officially supported and benchmarked, showing the lowest false positive rate (**0.0120**) among tested models while maintaining strong precision at **0.9947**.

### Alibaba Cloud and Moonshot AI (Kimi)

The platform includes dedicated support for `kimi-2.6` (Moonshot AI) and related Alibaba Cloud models, achieving an F1 score of **0.9780** in security scanning evaluations.

### DeepSeek

`deepseek-v4-flash` is fully supported with demonstrated performance metrics showing an F1 score of **0.9740**, making it suitable for high-throughput security analysis workflows.

### Zhipu AI (GLM)

`glm-5.1` achieves performance parity with top-tier models, scoring **0.9836** F1 with particularly strong recall metrics (**0.9974**) for detecting AI infrastructure vulnerabilities.

## Self-Hosted and Custom AI Model Support

Beyond commercial providers, AI-Infra-Guard supports any self-hosted or custom model exposing an OpenAI-compatible REST API. This includes deployments via Ollama, vLLM, NVIDIA Triton, ComfyUI, and n8n.

The generic model schema lives in [`pkg/database/model.go`](https://github.com/Tencent/AI-Infra-Guard/blob/main/pkg/database/model.go), which defines the storage structure for user-defined configurations. The system loads these definitions dynamically from [`db/model.yaml`](https://github.com/Tencent/AI-Infra-Guard/blob/main/db/model.yaml) (handled in [`pkg/database/yaml_model.go`](https://github.com/Tencent/AI-Infra-Guard/blob/main/pkg/database/yaml_model.go)), allowing runtime integration of private models without code changes.

## How AI-Infra-Guard Configures AI Models

The platform uses a unified three-field schema to standardize LLM integration across all providers. As implemented in [`common/utils/models/openai.go`](https://github.com/Tencent/AI-Infra-Guard/blob/main/common/utils/models/openai.go), every model configuration requires:

```json
{
  "model": "your-model-name",
  "token": "your-api-key-or-token",
  "base_url": "https://your-endpoint/v1"
}

```

The `ModelStore` implementation in [`pkg/database/model.go`](https://github.com/Tencent/AI-Infra-Guard/blob/main/pkg/database/model.go) persists these entries using GORM, storing ID, name, token, base URL, and rate limits. At runtime, [`common/websocket/task_manager.go`](https://github.com/Tencent/AI-Infra-Guard/blob/main/common/websocket/task_manager.go) injects these parameters into task execution contexts, enabling dynamic model switching per scan.

## Model Performance Benchmarks

The following table from the repository README shows validated AI models and their performance characteristics in the `aig-skill-scan` component:

| # | Model                     | F1     | Precision | Recall | FPR |

|---|--------------------------|--------|-----------|--------|-----|
| 1 | Claude Opus 4.6         | **0.9848** | 0.9725    | **0.9974** | 0.0663 |
| 2 | GLM 5.1                 | 0.9836 | 0.9701    | **0.9974** | 0.0723 |
| 3 | Gemini 3.5 Flash        | 0.9792 | **0.9947**| 0.9641 | **0.0120** |
| 4 | Kimi 2.6                | 0.9780 | 0.9895    | 0.9667 | 0.0241 |
| 5 | DeepSeek v4 Flash       | 0.9740 | 0.9868    | 0.9615 | 0.0301 |

These benchmarks demonstrate that AI-Infra-Guard supports models ranging from **0.9740 to 0.9848 F1 scores**, ensuring high-quality security analysis regardless of the chosen provider.

## Running Infrastructure Scans with Specific AI Models

### Command-Line Interface

Execute a skill scan using a specific supported model via the CLI:

```bash
aig-skill-scan --repo ./my-skill \
                -m deepseek-v4-flash \
                --language en \
                -o result.json

```

### HTTP API Integration

Submit scanning tasks programmatically using the JSON payload format processed by [`common/websocket/task_manager.go`](https://github.com/Tencent/AI-Infra-Guard/blob/main/common/websocket/task_manager.go):

```json
{
  "type": "ai_infra_scan",
  "content": {
    "target": ["http://127.0.0.1:8000"],
    "model": {
      "model": "gpt-4",
      "token": "sk-xxxxxxxx",
      "base_url": "https://api.openai.com/v1"
    }
  }
}

```

This payload structure applies universally across all supported AI models, whether commercial or self-hosted.

## Summary

- AI-Infra-Guard supports **six major commercial LLM families**: OpenAI, Anthropic, Google, Alibaba/Kimi, DeepSeek, and Zhipu AI (GLM).
- The **generic API schema** in [`pkg/database/model.go`](https://github.com/Tencent/AI-Infra-Guard/blob/main/pkg/database/model.go) enables integration with any OpenAI-compatible endpoint, including Ollama, vLLM, and custom deployments.
- Model configurations require only **three parameters**: `model`, `token`, and `base_url`, stored persistently in [`db/model.yaml`](https://github.com/Tencent/AI-Infra-Guard/blob/main/db/model.yaml).
- Benchmarked models achieve **F1 scores between 0.9740 and 0.9848**, with Claude Opus 4.6 and GLM 5.1 showing the highest accuracy.
- The **task manager** ([`common/websocket/task_manager.go`](https://github.com/Tencent/AI-Infra-Guard/blob/main/common/websocket/task_manager.go)) dynamically injects model parameters into scanning workflows via CLI or HTTP API.

## Frequently Asked Questions

### Can I use AI-Infra-Guard with a local LLM running on my own server?

Yes. AI-Infra-Guard supports any model exposing an OpenAI-compatible REST API. Configure your self-hosted model (such as those running on Ollama, vLLM, or Triton) by specifying its `base_url` in the model configuration JSON. The system loads these definitions from [`db/model.yaml`](https://github.com/Tencent/AI-Infra-Guard/blob/main/db/model.yaml) at runtime.

### Does AI-Infra-Guard require separate API implementations for each LLM provider?

No. The platform uses a unified model interface defined in [`common/utils/models/openai.go`](https://github.com/Tencent/AI-Infra-Guard/blob/main/common/utils/models/openai.go). All providers use the same three-field schema (`model`, `token`, `base_url`), meaning you can switch between OpenAI, Anthropic, or custom models without changing your integration code.

### Which AI model provides the best accuracy for AI-Infra-Guard security scans?

According to the benchmark data in the README, **Claude Opus 4.6** achieves the highest F1 score (0.9848) and perfect recall (0.9974), making it the most accurate for detecting AI infrastructure vulnerabilities. However, **Gemini 3.5 Flash** offers the lowest false positive rate (0.0120) if precision is your primary concern.

### How do I add a new custom model to AI-Infra-Guard?

Add your model to the `ModelStore` by creating an entry in [`db/model.yaml`](https://github.com/Tencent/AI-Infra-Guard/blob/main/db/model.yaml) or via the database interface in [`pkg/database/model.go`](https://github.com/Tencent/AI-Infra-Guard/blob/main/pkg/database/model.go). Specify the `model` name, API `token`, and `base_url`. The system will automatically make the model available for use with `aig-skill-scan` commands and HTTP API tasks without requiring a restart.