[BREAKING] FEAT add TAP to content harms scenario

Makefile

@@ -18,6 +18,7 @@ mypy:
 
 docs-build:
 	uv run jb build -W -v ./doc
+	cp -r assets doc/_build/assets
 	uv run ./build_scripts/generate_rss.py
 
 # Because of import time, "auto" seemed to actually go slower than just using 4 processes

doc/code/scenarios/0_scenarios.ipynb

@@ -220,7 +220,7 @@
       "Available Scenarios:\n",
       "================================================================================\n",
       "\u001b[1m\u001b[36m\n",
-      "  airt.content_harms\u001b[0m\n",
+      "  content_harms\u001b[0m\n",
       "    Class: ContentHarms\n",
       "    Description:\n",
       "      Content Harms Scenario implementation for PyRIT. This scenario contains\n",
@@ -235,7 +235,7 @@
       "      airt_hate, airt_fairness, airt_violence, airt_sexual, airt_harassment,\n",
       "      airt_misinformation, airt_leakage\n",
       "\u001b[1m\u001b[36m\n",
-      "  airt.cyber\u001b[0m\n",
+      "  cyber\u001b[0m\n",
       "    Class: Cyber\n",
       "    Description:\n",
       "      Cyber scenario implementation for PyRIT. This scenario tests how willing\n",
@@ -250,7 +250,67 @@
       "    Default Datasets (1, max 4 per dataset):\n",
       "      airt_malware\n",
       "\u001b[1m\u001b[36m\n",
-      "  airt.scam\u001b[0m\n",
+      "  jailbreak\u001b[0m\n",
+      "    Class: Jailbreak\n",
+      "    Description:\n",
+      "      Jailbreak scenario implementation for PyRIT. This scenario tests how\n",
+      "      vulnerable models are to jailbreak attacks by applying various\n",
+      "      single-turn jailbreak templates to a set of test prompts. The responses\n",
+      "      are scored to determine if the jailbreak was successful.\n",
+      "    Aggregate Strategies:\n",
+      "      - all\n",
+      "    Available Strategies (1):\n",
+      "      pyrit\n",
+      "    Default Strategy: all\n",
+      "    Default Datasets (1, max 4 per dataset):\n",
+      "      airt_harms\n",
+      "\u001b[1m\u001b[36m\n",
+      "  leakage\u001b[0m\n",
+      "    Class: Leakage\n",
+      "    Description:\n",
+      "      Leakage scenario implementation for PyRIT. This scenario tests how\n",
+      "      susceptible models are to leaking training data, PII, intellectual\n",
+      "      property, or other confidential information. The LeakageScenario class\n",
+      "      contains different attack variations designed to extract sensitive\n",
+      "      information from models.\n",
+      "    Aggregate Strategies:\n",
+      "      - all, single_turn, multi_turn, ip, sensitive_data\n",
+      "    Available Strategies (4):\n",
+      "      first_letter, image, role_play, crescendo\n",
+      "    Default Strategy: all\n",
+      "    Default Datasets (1, max 4 per dataset):\n",
+      "      airt_leakage\n",
+      "\u001b[1m\u001b[36m\n",
+      "  psychosocial\u001b[0m\n",
+      "    Class: Psychosocial\n",
+      "    Description:\n",
+      "      Psychosocial Harms Scenario implementation for PyRIT. This scenario\n",
+      "      contains various psychosocial harm-based checks that evaluate model\n",
+      "      behavior for responses that may harm the well-being or mental health of\n",
+      "      users. Examples include mistreating users in a mental health crisis or\n",
+      "      aiding them in self-harm. All datasets in this scenario share the\n",
+      "      unified 'psychosocial' harm category, but each dataset (e.g.,\n",
+      "      imminent_crisis, dependency) can have unique: - Multiturn attack\n",
+      "      strategies - Scoring rubrics - Seed objectives The scenario tests\n",
+      "      whether models: - Recognize mental health crises and respond\n",
+      "      appropriately - Avoid facilitating self-harm or harmful behaviors -\n",
+      "      Provide crisis resources and encourage professional help Example usage\n",
+      "      with custom configurations: # Define custom configurations per subharm\n",
+      "      category custom_configs = { \"airt_imminent_crisis\": SubharmConfig(\n",
+      "      crescendo_system_prompt_path=\"path/to/custom_escalation.yaml\",\n",
+      "      scoring_rubric_path=\"path/to/custom_rubric.yaml\", ), } scenario =\n",
+      "      Psychosocial(subharm_configs=custom_configs) await\n",
+      "      scenario.initialize_async( objective_target=target_llm,\n",
+      "      scenario_strategies=[PsychosocialStrategy.MULTI_TURN], )\n",
+      "    Aggregate Strategies:\n",
+      "      - all\n",
+      "    Available Strategies (2):\n",
+      "      imminent_crisis, licensed_therapist\n",
+      "    Default Strategy: all\n",
+      "    Default Datasets (1, max 4 per dataset):\n",
+      "      airt_imminent_crisis\n",
+      "\u001b[1m\u001b[36m\n",
+      "  scam\u001b[0m\n",
       "    Class: Scam\n",
       "    Description:\n",
       "      Scam scenario evaluates an endpoint's ability to generate scam-related\n",
@@ -264,11 +324,19 @@
       "    Default Datasets (1, max 4 per dataset):\n",
       "      airt_scams\n",
       "\u001b[1m\u001b[36m\n",
-      "  foundry.foundry\u001b[0m\n",
-      "    Class: FoundryScenario\n",
+      "  red_team_agent\u001b[0m\n",
+      "    Class: RedTeamAgent\n",
       "    Description:\n",
-      "      Deprecated alias for Foundry. This class is deprecated and will be\n",
-      "      removed in version 0.13.0. Use `Foundry` instead.\n",
+      "      RedTeamAgent is a preconfigured scenario that automatically generates\n",
+      "      multiple AtomicAttack instances based on the specified attack\n",
+      "      strategies. It supports both single-turn attacks (with various\n",
+      "      converters) and multi-turn attacks (Crescendo, RedTeaming), making it\n",
+      "      easy to quickly test a target against multiple attack vectors. The\n",
+      "      scenario can expand difficulty levels (EASY, MODERATE, DIFFICULT) into\n",
+      "      their constituent attack strategies, or you can specify individual\n",
+      "      strategies directly. This scenario is designed for use with the Foundry\n",
+      "      AI Red Teaming Agent library, providing a consistent PyRIT contract for\n",
+      "      their integration.\n",
       "    Aggregate Strategies:\n",
       "      - all, easy, moderate, difficult\n",
       "    Available Strategies (25):\n",
@@ -280,7 +348,7 @@
       "    Default Datasets (1, max 4 per dataset):\n",
       "      harmbench\n",
       "\u001b[1m\u001b[36m\n",
-      "  garak.encoding\u001b[0m\n",
+      "  encoding\u001b[0m\n",
       "    Class: Encoding\n",
       "    Description:\n",
       "      Encoding Scenario implementation for PyRIT. This scenario tests how\n",
@@ -305,7 +373,7 @@
       "\n",
       "================================================================================\n",
       "\n",
-      "Total scenarios: 5\n"
+      "Total scenarios: 8\n"
      ]
     },
     {

pyrit/scenario/core/atomic_attack.py

@@ -110,7 +110,6 @@ def __init__(
         self._seed_groups = seed_groups
         self._adversarial_chat = adversarial_chat
         self._objective_scorer = objective_scorer
-        self._objective_scorer = objective_scorer
         self._memory_labels = memory_labels or {}
         self._attack_execute_params = attack_execute_params
 

pyrit/scenario/core/scenario.py

@@ -53,7 +53,7 @@ class Scenario(ABC):
     def __init__(
         self,
         *,
-        name: str,
+        name: str = "",
         version: int,
         strategy_class: type[ScenarioStrategy],
         objective_scorer: Scorer,
@@ -104,7 +104,7 @@ def __init__(
         self._objective_scorer = objective_scorer
         self._objective_scorer_identifier = objective_scorer.get_identifier()
 
-        self._name = name
+        self._name = name if name else type(self).__name__
         self._memory = CentralMemory.get_memory_instance()
         self._atomic_attacks: list[AtomicAttack] = []
         self._scenario_result_id: Optional[str] = str(scenario_result_id) if scenario_result_id else None

pyrit/scenario/core/scenario_strategy.py

@@ -13,8 +13,10 @@
 
 from __future__ import annotations
 
-from enum import Enum
-from typing import TYPE_CHECKING, TypeVar
+from enum import Enum, EnumMeta
+from typing import TYPE_CHECKING, Any, TypeVar
+
+from pyrit.common.deprecation import print_deprecation_message
 
 if TYPE_CHECKING:
     from collections.abc import Sequence
@@ -23,7 +25,34 @@
 T = TypeVar("T", bound="ScenarioStrategy")
 
 
-class ScenarioStrategy(Enum):
+class _DeprecatedEnumMeta(EnumMeta):
+    """
+    Custom Enum metaclass that supports deprecated member aliases.
+
+    Subclasses of ScenarioStrategy can define deprecated member name mappings
+    by setting ``__deprecated_members__`` on the class after definition.
+    Each entry maps the old name to a ``(new_name, removed_in)`` tuple::
+
+        MyStrategy.__deprecated_members__ = {"OLD_NAME": ("NewName", "0.13.0")}
+
+    Accessing ``MyStrategy.OLD_NAME`` will emit a DeprecationWarning and return
+    the same enum member as ``MyStrategy.NewName``.
+    """
+
+    def __getattr__(cls, name: str) -> Any:
+        deprecated = cls.__dict__.get("__deprecated_members__")
+        if deprecated and name in deprecated:
+            new_name, removed_in = deprecated[name]
+            print_deprecation_message(
+                old_item=f"{cls.__name__}.{name}",
+                new_item=f"{cls.__name__}.{new_name}",
+                removed_in=removed_in,
+            )
+            return cls[new_name]
+        raise AttributeError(name)
+
+
+class ScenarioStrategy(Enum, metaclass=_DeprecatedEnumMeta):
     """
     Base class for attack strategies with tag-based categorization and aggregation.
 

pyrit/scenario/scenarios/airt/__init__.py

@@ -9,19 +9,21 @@
 )
 from pyrit.scenario.scenarios.airt.cyber import Cyber, CyberStrategy
 from pyrit.scenario.scenarios.airt.jailbreak import Jailbreak, JailbreakStrategy
-from pyrit.scenario.scenarios.airt.leakage_scenario import LeakageScenario, LeakageStrategy
-from pyrit.scenario.scenarios.airt.psychosocial_scenario import PsychosocialScenario, PsychosocialStrategy
+from pyrit.scenario.scenarios.airt.leakage import Leakage, LeakageScenario, LeakageStrategy
+from pyrit.scenario.scenarios.airt.psychosocial import Psychosocial, PsychosocialScenario, PsychosocialStrategy
 from pyrit.scenario.scenarios.airt.scam import Scam, ScamStrategy
 
 __all__ = [
     "ContentHarms",
     "ContentHarmsStrategy",
+    "Psychosocial",
     "PsychosocialScenario",
     "PsychosocialStrategy",
     "Cyber",
     "CyberStrategy",
     "Jailbreak",
     "JailbreakStrategy",
+    "Leakage",
     "LeakageScenario",
     "LeakageStrategy",
     "Scam",