---
sidebar_label: Video Inputs
title: Video Jailbreaking Strategy
description: Evaluate video-capable AI models against adversarial video inputs with embedded text designed to bypass safety filters
---

# Video Jailbreaking

The Video strategy converts prompt text into a video with text overlay and then encodes that video as a base64 string. This allows for testing how AI systems handle video-encoded text, which may potentially bypass text-based content filters or lead to different behaviors than when processing plain text.

## Why Use This Strategy

This strategy helps security researchers and AI developers:

1. **Test model capabilities**: Assess if models can extract and process text from base64-encoded videos
2. **Evaluate security measures**: Determine if video-encoded text can bypass content filters that typically scan plain text
3. **Assess multi-modal behavior**: Identify differences in how models respond to the same content in different formats
4. **Discover inconsistencies**: Reveal potential vulnerabilities by comparing text-based and video-based processing pathways

## How It Works

The strategy performs the following operations:

1. Takes the original text from your test case
2. Creates a simple video with the text displayed on a white background for 5 seconds
3. Converts the video to a base64 string
4. Replaces the original text in your test case with the base64-encoded video

The resulting test case contains the same semantic content as the original but in a different format that may be processed differently by AI systems.

## Example Transformation

For instance, a harmful prompt that might normally be filtered is converted into a video with the text overlaid, then encoded as base64. The encoded video would start like:

```
AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1wNDEAAAAIZnJlZQAAAu1tZGF0...
```

## Implementation

Use it in your promptfooconfig.yaml like this:

```yaml title="promptfooconfig.yaml"
# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
description: 'Multi-modal redteam'
targets:
  - bedrock:amazon.nova-lite-v1:0
prompts:
  - file://path/to/your/prompt.json
redteam:
  plugins:
    - ...
  strategies:
    - video
    - id: basic
      config:
        enabled: false
```

:::note
You should only disable all other strategies when using the video strategy. You can do this by setting `enabled: false` for the basic strategy and removing other strategies from the strategies array.
:::

Your prompt.json file should look like this:

```json title="prompt.json"
[
  {
    "role": "system",
    "content": "You are a helpful assistant"
  },
  {
    "role": "user",
    "content": [
      {
        "video": {
          "format": "mp4",
          "source": { "bytes": "{{video}}" }
        }
      }
    ]
  }
]
```

:::note
You should update the prompt.json to match the prompt format of your LLM provider. Base64 videos are encoded as MP4 format.
:::

:::note
The `{{video}}` syntax in the examples is a Nunjucks template variable. When promptfoo processes your prompt, it replaces `{{video}}` with the base64-encoded video data.
:::

## Requirements

This strategy requires FFmpeg to be installed on your system:

**On macOS:**

```bash
brew install ffmpeg
```

**On Ubuntu/Debian:**

```bash
apt-get install ffmpeg
```

**On Windows:**
Download from [ffmpeg.org](https://ffmpeg.org/download.html) or use package managers like Chocolatey:

```bash
choco install ffmpeg
```

## Technical Details

- **Video Format**: The strategy creates MP4 videos with H.264 encoding
- **Duration**: Videos are 5 seconds long by default
- **Resolution**: 640x480 pixels
- **Text Rendering**: The text is centered on a white background using a standard font
- **Processing**: All video creation is done locally using FFmpeg

:::warning
This strategy requires more processing resources than other encoding strategies due to video generation. It may take longer to run, especially on large test sets.
:::

## Importance

This strategy is worth implementing because:

1. It tests the robustness of content filtering mechanisms against video formats
2. It evaluates the model's ability to handle and extract information from video data
3. It can reveal inconsistencies in how models handle the same content presented in different formats
4. Video modalities may have different thresholds or processing pipelines for harmful content
5. It complements image and audio strategies to provide comprehensive multi-modal testing

## Related Concepts

- [Audio Jailbreaking](/docs/red-team/strategies/audio) - Similar approach using speech audio
- [Image Jailbreaking](/docs/red-team/strategies/image) - Similar approach using images
- [Multi-Modal Red Teaming Guide](/docs/guides/multimodal-red-team) - Comprehensive guide for testing multi-modal models
- [Types of LLM vulnerabilities](/docs/red-team/llm-vulnerability-types/) - Full vulnerability and plugin directory with category mapping
- [Red Team Strategies](/docs/red-team/strategies/) - Full strategy catalog