DOC: Add scenario doc pages for all 7 undocumented scenarios

doc/code/scenarios/2_content_harms.py

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+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.19.1
+#   kernelspec:
+#     display_name: pyrit-dev
+#     language: python
+#     name: pyrit-dev
+# ---
+
+# %% [markdown]
+# # 2. Content Harms Scenario
+#
+# The `ContentHarms` scenario tests whether a target model can be induced to generate harmful content across
+# seven harm categories: hate, fairness, violence, sexual, harassment, misinformation, and leakage. It combines
+# single-turn attacks (PromptSending, RolePlay) with multi-turn techniques (ManyShot, TAP) to provide broad
+# coverage of content safety risks.
+#
+# ## Available Strategies
+#
+# Each strategy targets a specific harm category with its own dataset:
+#
+# | Strategy | CLI Value | Description |
+# |----------|-----------|-------------|
+# | ALL | `all` | Aggregate — runs all 7 harm categories |
+# | Hate | `hate` | Tests for hateful content generation |
+# | Fairness | `fairness` | Tests for unfair or biased content |
+# | Violence | `violence` | Tests for violent content generation |
+# | Sexual | `sexual` | Tests for sexual content generation |
+# | Harassment | `harassment` | Tests for harassing content generation |
+# | Misinformation | `misinformation` | Tests for misinformation generation |
+# | Leakage | `leakage` | Tests for data leakage in content |
+#
+# ## Setup
+
+# %%
+from pyrit.prompt_target import OpenAIChatTarget
+from pyrit.scenario import DatasetConfiguration
+from pyrit.scenario.printer.console_printer import ConsoleScenarioResultPrinter
+from pyrit.scenario.scenarios.airt import ContentHarms, ContentHarmsStrategy
+from pyrit.setup import IN_MEMORY, initialize_pyrit_async
+from pyrit.setup.initializers import LoadDefaultDatasets
+
+await initialize_pyrit_async(memory_db_type=IN_MEMORY, initializers=[LoadDefaultDatasets()])  # type: ignore
+
+objective_target = OpenAIChatTarget()
+printer = ConsoleScenarioResultPrinter()
+
+# %% [markdown]
+# ## Running via CLI
+#
+# The simplest way to run this scenario is with `pyrit_scan`. To test a single harm category quickly:
+#
+# ```bash
+# pyrit_scan airt.content_harms \
+#   --initializers target load_default_datasets \
+#   --target openai_chat \
+#   --strategies hate \
+#   --max-dataset-size 1
+# ```
+#
+# To run all harm categories:
+#
+# ```bash
+# pyrit_scan airt.content_harms \
+#   --initializers target load_default_datasets \
+#   --target openai_chat \
+#   --max-dataset-size 2
+# ```
+#
+# ## Programmatic Usage
+#
+# For more control, you can configure and run the scenario programmatically. Here we run only the `hate`
+# strategy with a minimal dataset. Note that each strategy runs **four** attack types (PromptSending,
+# RolePlay, ManyShot, TAP) plus a baseline, so even a single strategy produces multiple atomic attacks.
+
+# %%
+dataset_config = DatasetConfiguration(dataset_names=["airt_hate"], max_dataset_size=1)
+
+scenario = ContentHarms()
+await scenario.initialize_async(  # type: ignore
+    objective_target=objective_target,
+    scenario_strategies=[ContentHarmsStrategy.Hate],
+    dataset_config=dataset_config,
+)
+
+print(f"Scenario: {scenario.name}")
+print(f"Atomic attacks: {scenario.atomic_attack_count}")
+
+# %%
+scenario_result = await scenario.run_async()  # type: ignore
+
+# %% [markdown]
+# ## Interpreting Results
+#
+# The `ScenarioResult` contains aggregated outcomes from all atomic attacks. Use the printer to see a
+# summary of success rates and strategy effectiveness.
+
+# %%
+await printer.print_summary_async(scenario_result)  # type: ignore