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Probe data

The probe_data package loads attack datasets, optionally transforms prompts, and can generate image or audio payloads for multi-modal specs.

Datasets

agentic_security.probe_data.data:

  • load_dataset_generic — load a CSV URL or Hugging Face dataset into a ProbeDataset
  • load_local_csv / load_local_csv_files — datasets from local CSV files
  • prepare_prompts — select and transform registry datasets for a scan

agentic_security.probe_data.models.ProbeDataset is the in-memory dataset type. Image datasets wrap that as ImageProbeDataset.

Transforms

agentic_security.probe_data.stenography_fn provides encoding helpers such as rot13, base64_encode, and mirror_words. See stenography.

Image and audio

  • generate_image / generate_image_dataset in probe_data.image_generator
  • generate_audioform in probe_data.audio_generator
from agentic_security.probe_data.audio_generator import generate_audioform

audio_bytes = generate_audioform("Hello, world!")

Prompt selection

probe_data.modules.rl_model implements PromptSelectionInterface with RandomPromptSelector, CloudRLPromptSelector, and QLearningPromptSelector. update_rewards returns None. Boolean arguments are Python True / False.

from agentic_security.probe_data.modules.rl_model import QLearningPromptSelector

selector = QLearningPromptSelector(["What is AI?", "Explain machine learning"])
next_prompt = selector.select_next_prompt("What is AI?", passed_guard=True)
selector.update_rewards("What is AI?", next_prompt, reward=1.0, passed_guard=True)

See RL model for the selector options.