FEAT: add support for multimodal data from HarmBench

doc/api.rst

@@ -126,6 +126,7 @@ API Reference
     fetch_examples
     fetch_forbidden_questions_dataset
     fetch_harmbench_dataset
+    fetch_harmbench_multimodal_dataset_async
     fetch_librAI_do_not_answer_dataset
     fetch_llm_latent_adversarial_training_harmful_dataset
     fetch_jbb_behaviors_by_harm_category

pyrit/datasets/__init__.py

@@ -10,6 +10,7 @@
 from pyrit.datasets.dataset_helper import fetch_examples
 from pyrit.datasets.forbidden_questions_dataset import fetch_forbidden_questions_dataset
 from pyrit.datasets.harmbench_dataset import fetch_harmbench_dataset
+from pyrit.datasets.harmbench_multimodal_dataset import fetch_harmbench_multimodal_dataset_async
 from pyrit.datasets.librAI_do_not_answer_dataset import fetch_librAI_do_not_answer_dataset
 from pyrit.datasets.llm_latent_adversarial_training_harmful_dataset import (
     fetch_llm_latent_adversarial_training_harmful_dataset,
@@ -47,6 +48,7 @@
     "fetch_examples",
     "fetch_forbidden_questions_dataset",
     "fetch_harmbench_dataset",
+    "fetch_harmbench_multimodal_dataset_async",
     "fetch_librAI_do_not_answer_dataset",
     "fetch_llm_latent_adversarial_training_harmful_dataset",
     "fetch_many_shot_jailbreaking_dataset",

pyrit/datasets/harmbench_multimodal_dataset.py

@@ -0,0 +1,200 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import logging
+import uuid
+from enum import Enum
+from pathlib import Path
+from typing import List, Literal, Optional
+
+from pyrit.common.net_utility import make_request_and_raise_if_error_async
+from pyrit.datasets.dataset_helper import fetch_examples
+from pyrit.models import SeedPromptDataset, data_serializer_factory
+from pyrit.models.seed_prompt import SeedPrompt
+
+logger = logging.getLogger(__name__)
+
+
+class SemanticCategory(Enum):
+    CYBERCRIME_INTRUSION = "cybercrime_intrusion"  # n=54
+    ILLEGAL = "illegal"  # 36
+    HARMFUL = "harmful"  # 9
+    CHEMICAL_BIOLOGICAL = "chemical_biological"  # 4
+    HARASSMENT_BULLYING = "harassment_bullying"  # 4
+    MISINFORMATION_DISINFORMATION = "misinformation_disinformation"  # 3
+
+
+async def fetch_harmbench_multimodal_dataset_async(
+    *,
+    source: str = (
+        "https://raw.githubusercontent.com/centerforaisafety/HarmBench/c0423b9/data/behavior_datasets/"
+        "harmbench_behaviors_multimodal_all.csv"
+    ),
+    source_type: Literal["public_url", "file"] = "public_url",
+    cache: bool = True,
+    data_home: Optional[Path] = None,
+    categories: Optional[List[SemanticCategory]] = None,
+) -> SeedPromptDataset:
+    """
+    Fetch HarmBench multimodal examples and create a SeedPromptDataset.
+
+    The HarmBench multimodal dataset contains 110 harmful behaviors.
+    Each example consists of an image ("image_path") and a behavior string referencing the image ("text").
+    The text and image prompts that belong to the same example are linked using the same ``prompt_group_id``.
+    You can extract the grouped prompts using the ``group_seed_prompts_by_prompt_group_id`` method.
+
+    Note: The first call may be slow as images need to be downloaded from the remote repository.
+    Subsequent calls will be faster since images are cached locally and won't need to be re-downloaded.
+
+    Args:
+        source (str): The source from which to fetch examples. Defaults to the HarmBench repository.
+        source_type (Literal["public_url", "file"]): The type of source. Defaults to 'public_url'.
+        cache (bool): Whether to cache the fetched examples. Defaults to True.
+        data_home (Optional[Path]): Directory to store cached data. Defaults to None.
+        categories (Optional[List[SemanticCategory]]): List of semantic categories
+            to filter examples. If None, all categories are included (default).
+
+    Returns:
+        SeedPromptDataset: A SeedPromptDataset containing the multimodal examples.
+
+    Raises:
+        ValueError: If any of the specified categories are invalid.
+
+    Note:
+        For more information related to the HarmBench project and the original dataset, visit:
+        https://www.harmbench.org/ \n
+        Paper: https://arxiv.org/abs/2402.04249 \n
+        Authors:
+            Mantas Mazeika & Long Phan & Xuwang Yin & Andy Zou & Zifan Wang & Norman Mu & Elham Sakhaee
+            & Nathaniel Li & Steven Basart & Bo Li & David Forsyth & Dan Hendrycks
+    """
+    if categories is not None:
+        valid_categories = {category.value for category in SemanticCategory}
+        invalid_categories = (
+            set(cat.value if isinstance(cat, SemanticCategory) else cat for cat in categories) - valid_categories
+        )
+        if invalid_categories:
+            raise ValueError(f"Invalid semantic categories: {', '.join(invalid_categories)}")
+
+    required_keys = {"Behavior", "BehaviorID", "FunctionalCategory", "SemanticCategory", "ImageFileName"}
+    examples = fetch_examples(source, source_type, cache, data_home)
+    prompts = []
+    failed_image_count = 0
+
+    for example in examples:
+        missing_keys = required_keys - example.keys()
+        if missing_keys:
+            raise ValueError(f"Missing keys in example: {', '.join(missing_keys)}")
+
+        if example["FunctionalCategory"] != "multimodal":
+            continue
+
+        semantic_category = example["SemanticCategory"]
+
+        if categories is not None:
+            category_values = {cat.value for cat in categories}
+            if semantic_category not in category_values:
+                continue
+
+        behavior_text = example["Behavior"]
+        behavior_id = example["BehaviorID"]
+        image_filename = example["ImageFileName"]
+        image_description = example.get("ImageDescription", "")
+        redacted_description = example.get("RedactedImageDescription", "")
+
+        # A unique group ID to link the text and image prompts since they are part of the same example
+        group_id = uuid.uuid4()
+
+        # Note: All images in the HarmBench dataset are stored as .png files, even if the ImageFileName
+        # field specifies a different extension (.jpg or .jpeg). Hence we always use .png extension here.
+        image_url = (
+            "https://raw.githubusercontent.com/centerforaisafety/HarmBench/c0423b9/data/multimodal_behavior_images/"
+            f"{image_filename.rsplit('.', 1)[0]}.png"
+        )
+
+        try:
+            # Only include examples where image fetch is successful
+            local_image_path = await _fetch_and_save_image_async(image_url, behavior_id)
+
+            image_prompt = SeedPrompt(
+                value=local_image_path,
+                data_type="image_path",
+                name=f"HarmBench Multimodal Image - {behavior_id}",
+                dataset_name="HarmBench Multimodal Examples",
+                harm_categories=[semantic_category],
+                description=f"An image prompt from the HarmBench multimodal dataset, BehaviorID: {behavior_id}",
+                source=source,
+                prompt_group_id=group_id,
+                sequence=0,
+                metadata={
+                    "behavior_id": behavior_id,
+                    "image_description": image_description,
+                    "redacted_image_description": redacted_description,
+                    "original_image_url": image_url,
+                },
+            )
+            prompts.append(image_prompt)
+        except Exception as e:
+            failed_image_count += 1
+            logger.warning(f"Failed to fetch image for behavior {behavior_id}: {e}. Skipping this example.")
+        else:
+            text_prompt = SeedPrompt(
+                value=behavior_text,
+                data_type="text",
+                name=f"HarmBench Multimodal Text - {behavior_id}",
+                dataset_name="HarmBench Multimodal Examples",
+                harm_categories=[semantic_category],
+                description=(f"A text prompt from the HarmBench multimodal dataset, BehaviorID: {behavior_id}"),
+                source=source,
+                prompt_group_id=group_id,
+                sequence=0,
+                metadata={
+                    "behavior_id": behavior_id,
+                },
+                authors=[
+                    "Mantas Mazeika",
+                    "Long Phan",
+                    "Xuwang Yin",
+                    "Andy Zou",
+                    "Zifan Wang",
+                    "Norman Mu",
+                    "Elham Sakhaee",
+                    "Nathaniel Li",
+                    "Steven Basart",
+                    "Bo Li",
+                    "David Forsyth",
+                    "Dan Hendrycks",
+                ],
+                groups=[
+                    "University of Illinois Urbana-Champaign",
+                    "Center for AI Safety",
+                    "Carnegie Mellon University",
+                    "UC Berkeley",
+                    "Microsoft",
+                ],
+            )
+            prompts.append(text_prompt)
+
+    if failed_image_count > 0:
+        logger.warning(f"Total skipped examples: {failed_image_count} (image fetch failures)")
+
+    seed_prompt_dataset = SeedPromptDataset(prompts=prompts)
+    return seed_prompt_dataset
+
+
+async def _fetch_and_save_image_async(image_url: str, behavior_id: str) -> str:
+    filename = f"harmbench_{behavior_id}.png"
+    serializer = data_serializer_factory(category="seed-prompt-entries", data_type="image_path", extension="png")
+
+    # Return existing path if image already exists for this BehaviorID
+    serializer.value = str(serializer._memory.results_path + serializer.data_sub_directory + f"/{filename}")
+    try:
+        if await serializer._memory.results_storage_io.path_exists(serializer.value):
+            return serializer.value
+    except Exception as e:
+        logger.warning(f"Failed to check whether image for {behavior_id} already exists: {e}")
+
+    response = await make_request_and_raise_if_error_async(endpoint_uri=image_url, method="GET")
+    await serializer.save_data(data=response.content, output_filename=filename.replace(".png", ""))
+
+    return str(serializer.value)

tests/integration/datasets/test_fetch_datasets.py

@@ -13,6 +13,7 @@
     fetch_equitymedqa_dataset_unique_values,
     fetch_forbidden_questions_dataset,
     fetch_harmbench_dataset,
+    fetch_harmbench_multimodal_dataset_async,
     fetch_jbb_behaviors_by_harm_category,
     fetch_jbb_behaviors_by_jbb_category,
     fetch_jbb_behaviors_dataset,
@@ -72,6 +73,21 @@ def test_fetch_datasets(fetch_function, is_seed_prompt_dataset):
         assert len(data.prompts) > 0
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "fetch_function, number_of_prompts",
+    [
+        (fetch_harmbench_multimodal_dataset_async, 110 * 2),
+    ],
+)
+async def test_fetch_multimodal_datasets(fetch_function, number_of_prompts):
+    data = await fetch_function()
+
+    assert data is not None
+    assert isinstance(data, SeedPromptDataset)
+    assert len(data.prompts) == number_of_prompts
+
+
 @pytest.mark.integration
 def test_fetch_jbb_behaviors_by_harm_category():
     """Integration test for filtering by harm category with real data."""