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Aegis

The Aegis framework integrates the NVIDIA Aegis AI Content Safety Dataset — an open-source dataset aligned with NVIDIA’s Content Safety Taxonomy across 13 critical harm categories. Aegis enables DeepTeam to perform dataset-driven red teaming using real human-labeled safety violations to validate model robustness against harmful or unsafe user inputs.

Overview​

Aegis focuses on real-world unsafe content from public conversations. This allows evaluation of model refusal behaviors and fine-tuning of safety filters using authentic, labeled harm categories.

from deepteam.frameworks import Aegis
from deepteam import red_team
from somewhere import your_model_callback

aegis = Aegis(num_attacks=10)

risk_assessment = red_team(
    model_callback=your_model_callback,
    framework=aegis,
)

print(risk_assessment)

The Aegis framework accepts FOUR optional parameters:

  • [Optional] num_attacks: Number of harmful test cases to sample from the dataset. Defaulted to 15.
  • [Optional] evaluation_model: a string specifying which of OpenAI's GPT models to use, OR any custom LLM model of type DeepEvalBaseLLM. Defaulted to 'gpt-4o'.
  • [Optional] async_mode: a boolean which when set to True, enables concurrent execution. Defaulted to True.
  • [Optional] verbose_mode: a boolean which when set to True, prints intermediate steps used to evaluate model responses. Defaulted to False.

How It Works​

  1. Loads the Aegis dataset from Hugging Face.
  2. Filters unsafe samples labeled under NVIDIA’s harm taxonomy.
  3. Randomly samples num_attacks test cases.
  4. Evaluates how well the model identifies, refuses, or mitigates these harmful prompts.

Categories & Filtering​

Aegis covers 13 harm domains, all supported by DeepTeam filtering.

Example categories:

  • sexual_content
  • violence
  • hate_speech
  • self_harm
  • misinformation
  • privacy_violation
  • child_exploitation
  • drugs
  • terrorism

References​

NVIDIA Aegis AI Content Safety Dataset (Hugging Face)