doomarena-promptceptor 0.0.4.1

Promptceptor tool

Promptceptor

DoomArena Promptceptor (prompt interceptor) is a minimalistic tool for prompt engineering, red-teaming and debugging of AI agents.

Overview

Promptceptor works by monkey-patching common LLM API clients such as OpenAI and LiteLLM to track and store the prompt, parameters and completion of every LLM call in a simple folder structure on the disk. Streaming mode is supported.

The calls can then be modified and replayed for quick prototyping of prompt injection attacks and prompt-based defenses, with the option of changing the model and sampling parameters.

Supported clients

• OpenAI Chat Completions API: non-streaming and streaming.
• OpenAI Responses API: non-streaming and streaming # Function calls may not be supported yet
• LiteLLM: non-streaming and streaming

Quick start

1. Install this package

pip install doomarena-promptceptor

Or install it locally for development

pip install -e doomarena/promptceptor
pytest doomarena/promptceptor  # run the tests (may require some API keys)

2. Add a single line to your main script to monkey-patch calls to the LLM API of your choice

from doomarena.promptceptor.integrations.openai_chat import OpenAIChatPatcher
from openai import OpenAI
from pathlib import Path

# Add this in main thread / initialization / setup function
output_folder = OpenAIChatPatcher(log_dir=Path('logs')).patch_client() 

# All subsequent calls will be logged to output_folder
client = OpenAI()
print("\n▶ Running non-streaming chat completion...")
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {
            "role": "user",
            "content": "Write a one-sentence bedtime story about a unicorn."
        }
    ],
    stream=False
)

Check out the examples in ./src/doomarena/promptceptor/examples for more info on supported LLM API Clients.

3. Inspect the resulting folder structure, which should look something like this:

logs/2025-05-07-19-30-12
├── 0
│   ├── input.yaml
│   └── output.txt
└── 1
│   ├── input.yaml
│   └── output.txt
├── 2
│   ├── input.yaml
│   └── output.txt
...

Each call to the LLM API will result in a new subfolder (e.g. 0, 1, 2) containing the input call to the LLM input.yaml and the raw output output.txt. Multithreading and multiprocessing is supported but may result in gaps in the indices or several subfolders (not a big deal).

4. Modify and recompute.

If you're curious how a different input may have affected you can modify the prompt messages inside input.yaml, as well as the model (e.g. switch from gpt-4o to claude), temperature, and any other **kwargs exposed by the LLM API client.

Then, recompute the outputs with

promptceptor path/to/logs

Promptceptor will recompute the output if output.txt is missing or the input.yaml timestamp is newer (see --overwrite parameter for more details).

Examples

You can run and inspect examples of patching OpenAI and LiteLLM clients

# Export relevant API keys here
OPENAI_API_KEY=...
OPENROUTER_API_KEY=...

python -m doomarena.promptceptor.scripts.litellm_example
python -m doomarena.promptceptor.scripts.openai_example