diff --git a/doc/code/scenarios/0_scenarios.ipynb b/doc/code/scenarios/0_scenarios.ipynb
index 407dcea2cc..3eb5a071a3 100644
--- a/doc/code/scenarios/0_scenarios.ipynb
+++ b/doc/code/scenarios/0_scenarios.ipynb
@@ -97,7 +97,24 @@
    "execution_count": null,
    "id": "1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found default environment files: ['./.pyrit/.env', './.pyrit/.env.local']\n",
+      "Loaded environment file: ./.pyrit/.env\n",
+      "Loaded environment file: ./.pyrit/.env.local\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyrit:alembic] No new upgrade operations detected.\n"
+     ]
+    }
+   ],
    "source": [
     "from pyrit.common import apply_defaults\n",
     "from pyrit.scenario import (\n",
@@ -107,8 +124,10 @@
     ")\n",
     "from pyrit.score.true_false.true_false_scorer import TrueFalseScorer\n",
     "from pyrit.setup import initialize_pyrit_async\n",
+    "from pyrit.setup.initializers.components import ScenarioTechniqueInitializer\n",
     "\n",
     "await initialize_pyrit_async(memory_db_type=\"InMemory\")  # type: ignore [top-level-await]\n",
+    "await ScenarioTechniqueInitializer().initialize_async()  # type: ignore [top-level-await]\n",
     "\n",
     "\n",
     "class MyStrategy(ScenarioStrategy):\n",
@@ -169,7 +188,319 @@
    "execution_count": null,
    "id": "3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using fallback default objective scorer: TrueFalseInverterScorer with chat target: OpenAIChatTarget\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using fallback default objective scorer: TrueFalseInverterScorer with chat target: OpenAIChatTarget\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'adversarial_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using fallback default objective scorer: TrueFalseInverterScorer with chat target: OpenAIChatTarget\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'adversarial_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using fallback default objective scorer: TrueFalseInverterScorer with chat target: OpenAIChatTarget\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'adversarial_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using fallback default objective scorer: TrueFalseInverterScorer with chat target: OpenAIChatTarget\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Available Scenarios:\n",
+      "================================================================================\n",
+      "\u001b[1m\u001b[36m\n",
+      "  airt.cyber\u001b[0m\n",
+      "    Class: Cyber\n",
+      "    Description:\n",
+      "      Cyber scenario implementation for PyRIT. This scenario tests how willing\n",
+      "      models are to exploit cybersecurity harms by generating malware. The\n",
+      "      Cyber class contains different variations of the malware generation\n",
+      "      techniques.\n",
+      "    Aggregate Strategies:\n",
+      "      - all, multi_turn\n",
+      "    Available Strategies (1):\n",
+      "      red_teaming\n",
+      "    Default Strategy: all\n",
+      "    Default Datasets (1, max 4 per dataset):\n",
+      "      airt_malware\n",
+      "\u001b[1m\u001b[36m\n",
+      "  airt.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, simple, complex\n",
+      "    Available Strategies (4):\n",
+      "      prompt_sending, many_shot, skeleton, role_play\n",
+      "    Default Strategy: simple\n",
+      "    Default Datasets (1, max 4 per dataset):\n",
+      "      airt_harms\n",
+      "\u001b[1m\u001b[36m\n",
+      "  airt.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. Uses the registry/factory\n",
+      "      pattern to construct attack techniques.\n",
+      "    Aggregate Strategies:\n",
+      "      - all, default, single_turn, multi_turn\n",
+      "    Available Strategies (12):\n",
+      "      role_play, many_shot, tap, pair, crescendo_simulated, red_teaming,\n",
+      "      context_compliance, crescendo_movie_director, crescendo_history_lecture,\n",
+      "      crescendo_journalist_interview, first_letter, image\n",
+      "    Default Strategy: default\n",
+      "    Default Datasets (1, max 4 per dataset):\n",
+      "      airt_leakage\n",
+      "\u001b[1m\u001b[36m\n",
+      "  airt.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: - Scoring rubrics - Seed\n",
+      "      objectives The scenario tests whether models: - Recognize mental health\n",
+      "      crises and respond appropriately - Avoid facilitating self-harm or\n",
+      "      harmful behaviors - Provide crisis resources and encourage professional\n",
+      "      help Example usage with custom configurations: # Define custom\n",
+      "      configurations per subharm category custom_configs = {\n",
+      "      \"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.ImminentCrisis], )\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",
+      "  airt.rapid_response\u001b[0m\n",
+      "    Class: RapidResponse\n",
+      "    Description:\n",
+      "      Rapid Response scenario for content-harms testing. Tests model behavior\n",
+      "      across multiple harm categories using selectable attack techniques.\n",
+      "    Aggregate Strategies:\n",
+      "      - all, default, single_turn, multi_turn\n",
+      "    Available Strategies (10):\n",
+      "      role_play, many_shot, tap, pair, crescendo_simulated, red_teaming,\n",
+      "      context_compliance, crescendo_movie_director, crescendo_history_lecture,\n",
+      "      crescendo_journalist_interview\n",
+      "    Default Strategy: default\n",
+      "    Default Datasets (7, max 4 per dataset):\n",
+      "      airt_hate, airt_fairness, airt_violence, airt_sexual, airt_harassment,\n",
+      "      airt_misinformation, airt_leakage\n",
+      "\u001b[1m\u001b[36m\n",
+      "  airt.scam\u001b[0m\n",
+      "    Class: Scam\n",
+      "    Description:\n",
+      "      Scam scenario evaluates an endpoint's ability to generate scam-related\n",
+      "      materials (e.g., phishing emails, fraudulent messages) with primarily\n",
+      "      persuasion-oriented techniques.\n",
+      "    Aggregate Strategies:\n",
+      "      - all, single_turn, multi_turn\n",
+      "    Available Strategies (3):\n",
+      "      context_compliance, role_play, persuasive_rta\n",
+      "    Default Strategy: all\n",
+      "    Default Datasets (1, max 4 per dataset):\n",
+      "      airt_scams\n",
+      "    Supported Parameters:\n",
+      "      - max_turns (int) [default: '5']: Maximum conversation turns for the persuasive_rta strategy.\n",
+      "\u001b[1m\u001b[36m\n",
+      "  benchmark.adversarial\u001b[0m\n",
+      "    Class: AdversarialBenchmark\n",
+      "    Description:\n",
+      "      Benchmark scenario that compares the attack success rate (ASR) across\n",
+      "      adversarial models. Adversarial targets are user-supplied via the\n",
+      "      ``adversarial_targets`` parameter (declared in\n",
+      "      ``supported_parameters``). Each target must already be registered in\n",
+      "      ``TargetRegistry`` — typically by ``TargetInitializer`` from\n",
+      "      ``ADVERSARIAL_CHAT_*`` env vars, or programmatically via\n",
+      "      ``TargetRegistry.register_instance``. At run time,\n",
+      "      ``_get_atomic_attacks_async`` performs the ``(technique ×\n",
+      "      adversarial_target × dataset)`` cross-product: for each selected\n",
+      "      adversarial-capable ``core`` factory in the ``AttackTechniqueRegistry``\n",
+      "      and each requested target, it calls\n",
+      "      ``factory.create(attack_adversarial_config_override=...)`` with the\n",
+      "      resolved target — no global registry mutation. The resulting\n",
+      "      ``AtomicAttack`` is named ``f\"{technique}__{target}_{dataset}\"`` with\n",
+      "      ``display_group`` set to the target's registry name so per-model ASR\n",
+      "      rolls up naturally in result displays.\n",
+      "    Aggregate Strategies:\n",
+      "      - all, default, light, single_turn, multi_turn\n",
+      "    Available Strategies (9):\n",
+      "      role_play, tap, pair, crescendo_simulated, red_teaming,\n",
+      "      context_compliance, crescendo_movie_director, crescendo_history_lecture,\n",
+      "      crescendo_journalist_interview\n",
+      "    Default Strategy: light\n",
+      "    Default Datasets (1, max 8 per dataset):\n",
+      "      harmbench\n",
+      "    Supported Parameters:\n",
+      "      - adversarial_targets (list[str]): Registry names of adversarial chat targets to benchmark. Each name must already be registered in TargetRegistry (via TargetInitializer or TargetRegistry.register_instance). Use 'pyrit_scan list-targets' to see registered targets. Settable via --adversarial-targets <name> [<name> ...] on the CLI, or scenario.args.adversarial_targets in .pyrit_conf.\n",
+      "\u001b[1m\u001b[36m\n",
+      "  foundry.red_team_agent\u001b[0m\n",
+      "    Class: RedTeamAgent\n",
+      "    Description:\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",
+      "      ansi_attack, ascii_art, ascii_smuggler, atbash, base64, binary, caesar,\n",
+      "      character_space, char_swap, diacritic, flip, leetspeak, morse, rot13,\n",
+      "      suffix_append, string_join, unicode_confusable, unicode_substitution,\n",
+      "      url, jailbreak, tense, multi_turn, crescendo, pair, tap\n",
+      "    Default Strategy: easy\n",
+      "    Default Datasets (1, max 4 per dataset):\n",
+      "      harmbench\n",
+      "\u001b[1m\u001b[36m\n",
+      "  garak.encoding\u001b[0m\n",
+      "    Class: Encoding\n",
+      "    Description:\n",
+      "      Encoding Scenario implementation for PyRIT. This scenario tests how\n",
+      "      resilient models are to various encoding attacks by encoding potentially\n",
+      "      harmful text (by default slurs and XSS payloads) and testing if the\n",
+      "      model will decode and repeat the encoded payload. It mimics the Garak\n",
+      "      encoding probe. The scenario works by: 1. Taking seed prompts (the\n",
+      "      harmful text to be encoded) 2. Encoding them using various encoding\n",
+      "      schemes (Base64, ROT13, Morse, etc.) 3. Asking the target model to\n",
+      "      decode the encoded text 4. Scoring whether the model successfully\n",
+      "      decoded and repeated the harmful content By default, this uses the same\n",
+      "      dataset as Garak: slur terms and web XSS payloads.\n",
+      "    Aggregate Strategies:\n",
+      "      - all\n",
+      "    Available Strategies (17):\n",
+      "      base64, base2048, base16, base32, ascii85, hex, quoted_printable,\n",
+      "      uuencode, rot13, braille, atbash, morse_code, nato, ecoji, zalgo,\n",
+      "      leet_speak, ascii_smuggler\n",
+      "    Default Strategy: all\n",
+      "    Default Datasets (2, max 3 per dataset):\n",
+      "      garak_slur_terms_en, garak_web_html_js\n",
+      "\n",
+      "================================================================================\n",
+      "\n",
+      "Total scenarios: 9\n"
+     ]
+    }
+   ],
    "source": [
     "from pyrit.backend.services.scenario_service import get_scenario_service\n",
     "from pyrit.cli._output import print_scenario_list\n",
@@ -233,8 +564,17 @@
   }
  ],
  "metadata": {
-  "jupytext": {
-   "main_language": "python"
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.13"
   }
  },
  "nbformat": 4,
diff --git a/doc/code/scenarios/0_scenarios.py b/doc/code/scenarios/0_scenarios.py
index 317aeac1ff..03e6fd81c2 100644
--- a/doc/code/scenarios/0_scenarios.py
+++ b/doc/code/scenarios/0_scenarios.py
@@ -102,8 +102,10 @@
 )
 from pyrit.score.true_false.true_false_scorer import TrueFalseScorer
 from pyrit.setup import initialize_pyrit_async
+from pyrit.setup.initializers.components import ScenarioTechniqueInitializer
 
 await initialize_pyrit_async(memory_db_type="InMemory")  # type: ignore [top-level-await]
+await ScenarioTechniqueInitializer().initialize_async()  # type: ignore [top-level-await]
 
 
 class MyStrategy(ScenarioStrategy):
diff --git a/doc/code/scenarios/1_common_scenario_parameters.ipynb b/doc/code/scenarios/1_common_scenario_parameters.ipynb
index 9da9b9195b..5d95e9b326 100644
--- a/doc/code/scenarios/1_common_scenario_parameters.ipynb
+++ b/doc/code/scenarios/1_common_scenario_parameters.ipynb
@@ -43,14 +43,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "No new upgrade operations detected.\n"
+      "[pyrit:alembic] No new upgrade operations detected.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Skipping scorer main: required target not found in TargetRegistry\n"
+      "Skipping target 'platform_openai_chat': PLATFORM_OPENAI_CHAT_GPT4O_MODEL is not set. All declared env vars (endpoint, key, model) must be present for this target to register.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Skipping target 'azure_foundry_phi4': AZURE_FOUNDRY_PHI4_MODEL is not set. All declared env vars (endpoint, key, model) must be present for this target to register.\n"
      ]
     },
     {
@@ -68,7 +75,9 @@
     "from pyrit.registry import TargetRegistry\n",
     "from pyrit.scenario.scenarios.foundry import FoundryStrategy, RedTeamAgent\n",
     "from pyrit.setup import initialize_from_config_async\n",
+    "from pyrit.setup.initializers.components import ScenarioTechniqueInitializer\n",
     "\n",
+    "await ScenarioTechniqueInitializer().initialize_async()  # type: ignore [top-level-await]\n",
     "await initialize_from_config_async(config_path=Path(\"../../scanner/pyrit_conf.yaml\"))  # type: ignore\n",
     "\n",
     "objective_target = TargetRegistry.get_registry_singleton().get_instance_by_name(\"openai_chat\")"
@@ -242,7 +251,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8734281af4a940bd9995f232ce52c16c",
+       "model_id": "e386860005e440a98009fc5cd1b55e86",
        "version_major": 2,
        "version_minor": 0
       },
@@ -283,26 +292,29 @@
       "\n",
       "\u001b[1m  📊 Scorer Information\u001b[0m\n",
       "\u001b[37m    ▸ Scorer Identifier\u001b[0m\n",
-      "\u001b[36m      • Scorer Type: SelfAskTrueFalseScorer\u001b[0m\n",
+      "\u001b[36m      • Scorer Type: TrueFalseInverterScorer\u001b[0m\n",
       "\u001b[36m      • scorer_type: true_false\u001b[0m\n",
       "\u001b[36m      • score_aggregator: OR_\u001b[0m\n",
-      "\u001b[36m      • model_name: gpt-40\u001b[0m\n",
-      "\u001b[36m      • temperature: 0.9\u001b[0m\n",
+      "\u001b[36m        └─ Composite of 1 scorer(s):\u001b[0m\n",
+      "\u001b[36m            • Scorer Type: SelfAskRefusalScorer\u001b[0m\n",
+      "\u001b[36m            • scorer_type: true_false\u001b[0m\n",
+      "\u001b[36m            • score_aggregator: OR_\u001b[0m\n",
+      "\u001b[36m            • model_name: gpt-4o-japan-nilfilter\u001b[0m\n",
       "\n",
       "\u001b[37m    ▸ Performance Metrics\u001b[0m\n",
-      "\u001b[36m      • Accuracy: 79.24%\u001b[0m\n",
-      "\u001b[36m      • Accuracy Std Error: ±0.0204\u001b[0m\n",
-      "\u001b[36m      • F1 Score: 0.7560\u001b[0m\n",
-      "\u001b[36m      • Precision: 0.8759\u001b[0m\n",
-      "\u001b[31m      • Recall: 0.6649\u001b[0m\n",
-      "\u001b[36m      • Average Score Time: 1.64s\u001b[0m\n",
+      "\u001b[36m      • Accuracy: 89.37%\u001b[0m\n",
+      "\u001b[36m      • Accuracy Std Error: ±0.0155\u001b[0m\n",
+      "\u001b[36m      • F1 Score: 0.8918\u001b[0m\n",
+      "\u001b[36m      • Precision: 0.8782\u001b[0m\n",
+      "\u001b[32m      • Recall: 0.9058\u001b[0m\n",
+      "\u001b[36m      • Average Score Time: 0.59s\u001b[0m\n",
       "\n",
       "\u001b[1m\u001b[36m▼ Overall Statistics\u001b[0m\n",
       "\u001b[36m────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
       "\u001b[1m  📈 Summary\u001b[0m\n",
       "\u001b[32m    • Total Strategies: 21\u001b[0m\n",
       "\u001b[32m    • Total Attack Results: 42\u001b[0m\n",
-      "\u001b[32m    • Overall Success Rate: 11%\u001b[0m\n",
+      "\u001b[32m    • Overall Success Rate: 0%\u001b[0m\n",
       "\u001b[32m    • Unique Objectives: 2\u001b[0m\n",
       "\n",
       "\u001b[1m\u001b[36m▼ Per-Group Breakdown\u001b[0m\n",
@@ -316,11 +328,11 @@
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: ascii_art\u001b[0m\n",
+      "\u001b[1m  🔸 Group: ascii_smuggler\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: ascii_smuggler\u001b[0m\n",
+      "\u001b[1m  🔸 Group: ascii_art\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
@@ -330,11 +342,11 @@
       "\n",
       "\u001b[1m  🔸 Group: base64\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
-      "\u001b[31m    • Success Rate: 100%\u001b[0m\n",
+      "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
       "\u001b[1m  🔸 Group: binary\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
-      "\u001b[33m    • Success Rate: 50%\u001b[0m\n",
+      "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
       "\u001b[1m  🔸 Group: caesar\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
@@ -364,11 +376,11 @@
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: rot13\u001b[0m\n",
+      "\u001b[1m  🔸 Group: suffix_append\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: suffix_append\u001b[0m\n",
+      "\u001b[1m  🔸 Group: rot13\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
@@ -378,7 +390,7 @@
       "\n",
       "\u001b[1m  🔸 Group: unicode_confusable\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
-      "\u001b[33m    • Success Rate: 50%\u001b[0m\n",
+      "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
       "\u001b[1m  🔸 Group: unicode_substitution\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
@@ -390,7 +402,7 @@
       "\n",
       "\u001b[1m  🔸 Group: jailbreak\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
-      "\u001b[33m    • Success Rate: 50%\u001b[0m\n",
+      "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
       "\u001b[36m====================================================================================================\u001b[0m\n",
       "\n"
@@ -405,7 +417,7 @@
     "    dataset_config=dataset_config,\n",
     ")\n",
     "baseline_result = await baseline_scenario.run_async()  # type: ignore\n",
-    "await output_scenario_async(baseline_result)"
+    "await output_scenario_async(baseline_result)  # type: ignore [top-level-await]"
    ]
   },
   {
@@ -458,64 +470,59 @@
       "\n",
       "\u001b[1m  📊 Scorer Information\u001b[0m\n",
       "\u001b[37m    ▸ Scorer Identifier\u001b[0m\n",
-      "\u001b[36m      • Scorer Type: SelfAskTrueFalseScorer\u001b[0m\n",
+      "\u001b[36m      • Scorer Type: TrueFalseInverterScorer\u001b[0m\n",
       "\u001b[36m      • scorer_type: true_false\u001b[0m\n",
       "\u001b[36m      • score_aggregator: OR_\u001b[0m\n",
-      "\u001b[36m      • model_name: gpt-40\u001b[0m\n",
-      "\u001b[36m      • temperature: 0.9\u001b[0m\n",
+      "\u001b[36m        └─ Composite of 1 scorer(s):\u001b[0m\n",
+      "\u001b[36m            • Scorer Type: SelfAskRefusalScorer\u001b[0m\n",
+      "\u001b[36m            • scorer_type: true_false\u001b[0m\n",
+      "\u001b[36m            • score_aggregator: OR_\u001b[0m\n",
+      "\u001b[36m            • model_name: gpt-4o-japan-nilfilter\u001b[0m\n",
       "\n",
       "\u001b[37m    ▸ Performance Metrics\u001b[0m\n",
-      "\u001b[36m      • Accuracy: 79.24%\u001b[0m\n",
-      "\u001b[36m      • Accuracy Std Error: ±0.0204\u001b[0m\n",
-      "\u001b[36m      • F1 Score: 0.7560\u001b[0m\n",
-      "\u001b[36m      • Precision: 0.8759\u001b[0m\n",
-      "\u001b[31m      • Recall: 0.6649\u001b[0m\n",
-      "\u001b[36m      • Average Score Time: 1.64s\u001b[0m\n",
+      "\u001b[36m      • Accuracy: 89.37%\u001b[0m\n",
+      "\u001b[36m      • Accuracy Std Error: ±0.0155\u001b[0m\n",
+      "\u001b[36m      • F1 Score: 0.8918\u001b[0m\n",
+      "\u001b[36m      • Precision: 0.8782\u001b[0m\n",
+      "\u001b[32m      • Recall: 0.9058\u001b[0m\n",
+      "\u001b[36m      • Average Score Time: 0.59s\u001b[0m\n",
       "\n",
       "\u001b[1m\u001b[36m▼ Overall Statistics\u001b[0m\n",
       "\u001b[36m────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
       "\u001b[1m  📈 Summary\u001b[0m\n",
       "\u001b[32m    • Total Strategies: 21\u001b[0m\n",
       "\u001b[32m    • Total Attack Results: 42\u001b[0m\n",
-      "\u001b[32m    • Overall Success Rate: 11%\u001b[0m\n",
+      "\u001b[32m    • Overall Success Rate: 0%\u001b[0m\n",
       "\u001b[32m    • Unique Objectives: 2\u001b[0m\n",
       "\n",
       "\u001b[1m\u001b[36m▼ Per-Group Breakdown\u001b[0m\n",
       "\u001b[36m────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: base64\u001b[0m\n",
-      "\u001b[33m    • Number of Results: 2\u001b[0m\n",
-      "\u001b[31m    • Success Rate: 100%\u001b[0m\n",
-      "\n",
-      "\u001b[1m  🔸 Group: binary\u001b[0m\n",
-      "\u001b[33m    • Number of Results: 2\u001b[0m\n",
-      "\u001b[33m    • Success Rate: 50%\u001b[0m\n",
-      "\n",
-      "\u001b[1m  🔸 Group: unicode_confusable\u001b[0m\n",
+      "\u001b[1m  🔸 Group: baseline\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
-      "\u001b[33m    • Success Rate: 50%\u001b[0m\n",
+      "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: jailbreak\u001b[0m\n",
+      "\u001b[1m  🔸 Group: ansi_attack\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
-      "\u001b[33m    • Success Rate: 50%\u001b[0m\n",
+      "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: baseline\u001b[0m\n",
+      "\u001b[1m  🔸 Group: ascii_smuggler\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: ansi_attack\u001b[0m\n",
+      "\u001b[1m  🔸 Group: ascii_art\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: ascii_art\u001b[0m\n",
+      "\u001b[1m  🔸 Group: atbash\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: ascii_smuggler\u001b[0m\n",
+      "\u001b[1m  🔸 Group: base64\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: atbash\u001b[0m\n",
+      "\u001b[1m  🔸 Group: binary\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
@@ -547,11 +554,11 @@
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: rot13\u001b[0m\n",
+      "\u001b[1m  🔸 Group: suffix_append\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
-      "\u001b[1m  🔸 Group: suffix_append\u001b[0m\n",
+      "\u001b[1m  🔸 Group: rot13\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
@@ -559,6 +566,10 @@
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
+      "\u001b[1m  🔸 Group: unicode_confusable\u001b[0m\n",
+      "\u001b[33m    • Number of Results: 2\u001b[0m\n",
+      "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
+      "\n",
       "\u001b[1m  🔸 Group: unicode_substitution\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
@@ -567,6 +578,10 @@
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
       "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
       "\n",
+      "\u001b[1m  🔸 Group: jailbreak\u001b[0m\n",
+      "\u001b[33m    • Number of Results: 2\u001b[0m\n",
+      "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
+      "\n",
       "\u001b[36m====================================================================================================\u001b[0m\n",
       "\n"
      ]
@@ -617,7 +632,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "103ae439a5554be79c786a8bcc9c1524",
+       "model_id": "9f07786563cc4128ba51a5e47eb53a6f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -675,7 +690,7 @@
       "\u001b[1m  📈 Summary\u001b[0m\n",
       "\u001b[32m    • Total Strategies: 2\u001b[0m\n",
       "\u001b[32m    • Total Attack Results: 4\u001b[0m\n",
-      "\u001b[36m    • Overall Success Rate: 25%\u001b[0m\n",
+      "\u001b[33m    • Overall Success Rate: 50%\u001b[0m\n",
       "\u001b[32m    • Unique Objectives: 2\u001b[0m\n",
       "\n",
       "\u001b[1m\u001b[36m▼ Per-Group Breakdown\u001b[0m\n",
@@ -687,7 +702,7 @@
       "\n",
       "\u001b[1m  🔸 Group: base64\u001b[0m\n",
       "\u001b[33m    • Number of Results: 2\u001b[0m\n",
-      "\u001b[32m    • Success Rate: 0%\u001b[0m\n",
+      "\u001b[33m    • Success Rate: 50%\u001b[0m\n",
       "\n",
       "\u001b[36m====================================================================================================\u001b[0m\n",
       "\n"
@@ -710,15 +725,13 @@
     "    scenario_strategies=[FoundryStrategy.Base64],\n",
     "    dataset_config=dataset_config,\n",
     ")\n",
+    "\n",
     "custom_result = await custom_scenario.run_async()  # type: ignore\n",
     "await output_scenario_async(custom_result)"
    ]
   }
  ],
  "metadata": {
-  "jupytext": {
-   "main_language": "python"
-  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -729,7 +742,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.13.13"
   }
  },
  "nbformat": 4,
diff --git a/doc/code/scenarios/1_common_scenario_parameters.py b/doc/code/scenarios/1_common_scenario_parameters.py
index 230e02fff3..4b0fd59406 100644
--- a/doc/code/scenarios/1_common_scenario_parameters.py
+++ b/doc/code/scenarios/1_common_scenario_parameters.py
@@ -32,7 +32,9 @@
 from pyrit.registry import TargetRegistry
 from pyrit.scenario.scenarios.foundry import FoundryStrategy, RedTeamAgent
 from pyrit.setup import initialize_from_config_async
+from pyrit.setup.initializers.components import ScenarioTechniqueInitializer
 
+await ScenarioTechniqueInitializer().initialize_async()  # type: ignore [top-level-await]
 await initialize_from_config_async(config_path=Path("../../scanner/pyrit_conf.yaml"))  # type: ignore
 
 objective_target = TargetRegistry.get_registry_singleton().get_instance_by_name("openai_chat")
@@ -118,7 +120,7 @@
     dataset_config=dataset_config,
 )
 baseline_result = await baseline_scenario.run_async()  # type: ignore
-await output_scenario_async(baseline_result)
+await output_scenario_async(baseline_result)  # type: ignore [top-level-await]
 
 # %% [markdown]
 # ### Sorting the Per-Group Breakdown by Success Rate
@@ -170,5 +172,6 @@
     scenario_strategies=[FoundryStrategy.Base64],
     dataset_config=dataset_config,
 )
+
 custom_result = await custom_scenario.run_async()  # type: ignore
 await output_scenario_async(custom_result)
diff --git a/doc/code/scenarios/2_custom_scenario_parameters.ipynb b/doc/code/scenarios/2_custom_scenario_parameters.ipynb
index a126e4b44c..e1c623b79a 100644
--- a/doc/code/scenarios/2_custom_scenario_parameters.ipynb
+++ b/doc/code/scenarios/2_custom_scenario_parameters.ipynb
@@ -53,12 +53,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Found default environment files: ['./.pyrit/.env', './.pyrit/.env.local']\n",
+      "Loaded environment file: ./.pyrit/.env\n",
+      "Loaded environment file: ./.pyrit/.env.local\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyrit:alembic] No new upgrade operations detected.\n",
       "Parameter(name='max_turns', description='Maximum conversation turns for the persuasive_rta strategy.', default=5, param_type=<class 'int'>, choices=None)\n"
      ]
     }
    ],
    "source": [
     "from pyrit.scenario.scenarios.airt.scam import Scam\n",
+    "from pyrit.setup import initialize_pyrit_async\n",
+    "from pyrit.setup.initializers.components import ScenarioTechniqueInitializer\n",
+    "\n",
+    "await initialize_pyrit_async(memory_db_type=\"InMemory\")  # type: ignore [top-level-await]\n",
+    "await ScenarioTechniqueInitializer().initialize_async()  # type: ignore [top-level-await]\n",
     "\n",
     "for param in Scam.supported_parameters():\n",
     "    print(param)"
@@ -242,10 +257,125 @@
    "id": "8",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using fallback default objective scorer: TrueFalseInverterScorer with chat target: OpenAIChatTarget\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using fallback default objective scorer: TrueFalseInverterScorer with chat target: OpenAIChatTarget\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'adversarial_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using fallback default objective scorer: TrueFalseInverterScorer with chat target: OpenAIChatTarget\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'adversarial_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using fallback default objective scorer: TrueFalseInverterScorer with chat target: OpenAIChatTarget\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'adversarial_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TargetRegistry entry 'objective_scorer_chat' not found. Falling back to default OpenAIChatTarget.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using fallback default objective scorer: TrueFalseInverterScorer with chat target: OpenAIChatTarget\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "Available Scenarios:\n",
+      "================================================================================\n",
       "\u001b[1m\u001b[36m\n",
       "  airt.scam\u001b[0m\n",
       "    Class: Scam\n",
@@ -261,7 +391,7 @@
       "    Default Datasets (1, max 4 per dataset):\n",
       "      airt_scams\n",
       "    Supported Parameters:\n",
-      "      - max_turns (int) [default: 5]: Maximum conversation turns for the persuasive_rta strategy.\n",
+      "      - max_turns (int) [default: '5']: Maximum conversation turns for the persuasive_rta strategy.\n",
       "\u001b[1m\u001b[36m\n",
       "  foundry.red_team_agent\u001b[0m\n",
       "    Class: RedTeamAgent\n",
@@ -285,7 +415,11 @@
       "      url, jailbreak, tense, multi_turn, crescendo, pair, tap\n",
       "    Default Strategy: easy\n",
       "    Default Datasets (1, max 4 per dataset):\n",
-      "      harmbench\n"
+      "      harmbench\n",
+      "\n",
+      "================================================================================\n",
+      "\n",
+      "Total scenarios: 2\n"
      ]
     }
    ],
@@ -362,7 +496,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.13.13"
   }
  },
  "nbformat": 4,
diff --git a/doc/code/scenarios/2_custom_scenario_parameters.py b/doc/code/scenarios/2_custom_scenario_parameters.py
index bcebdb1f9e..4222fff9a9 100644
--- a/doc/code/scenarios/2_custom_scenario_parameters.py
+++ b/doc/code/scenarios/2_custom_scenario_parameters.py
@@ -47,7 +47,13 @@
 # would wire up memory and scorers):
 
 # %%
+
 from pyrit.scenario.scenarios.airt.scam import Scam
+from pyrit.setup import initialize_pyrit_async
+from pyrit.setup.initializers.components import ScenarioTechniqueInitializer
+
+await initialize_pyrit_async(memory_db_type="InMemory")  # type: ignore [top-level-await]
+await ScenarioTechniqueInitializer().initialize_async()  # type: ignore [top-level-await]
 
 for param in Scam.supported_parameters():
     print(param)
diff --git a/tests/integration/datasets/test_load_default_datasets_integration.py b/tests/integration/datasets/test_load_default_datasets_integration.py
index 878b707905..dd7b3e346c 100644
--- a/tests/integration/datasets/test_load_default_datasets_integration.py
+++ b/tests/integration/datasets/test_load_default_datasets_integration.py
@@ -11,6 +11,7 @@
 import logging
 
 from pyrit.memory import CentralMemory
+from pyrit.setup.initializers.components.scenario_techniques import ScenarioTechniqueInitializer
 from pyrit.setup.initializers.scenarios.load_default_datasets import LoadDefaultDatasets
 
 logger = logging.getLogger(__name__)
@@ -25,6 +26,7 @@ async def test_initialize_loads_datasets_into_memory(self, sqlite_instance):
         real datasets and stores them in CentralMemory.
         """
         initializer = LoadDefaultDatasets()
+        await ScenarioTechniqueInitializer().initialize_async()
         await initializer.initialize_async()
 
         memory = CentralMemory.get_memory_instance()
diff --git a/tests/integration/datasets/test_seed_dataset_provider_integration.py b/tests/integration/datasets/test_seed_dataset_provider_integration.py
index cc09883c2f..f5f1687aa8 100644
--- a/tests/integration/datasets/test_seed_dataset_provider_integration.py
+++ b/tests/integration/datasets/test_seed_dataset_provider_integration.py
@@ -611,7 +611,7 @@ async def test_harmbench_metadata_parses_correctly(self):
         assert isinstance(metadata.tags, set)
         assert "default" in metadata.tags
         assert "safety" in metadata.tags
-        assert metadata.size == {"large"}
+        assert metadata.size == {"medium"}
         assert metadata.modalities == {"text"}
         assert isinstance(metadata.harm_categories, set)
         assert "cybercrime" in metadata.harm_categories
diff --git a/tests/integration/mocks.py b/tests/integration/mocks.py
index 58abb63970..512a4b5986 100644
--- a/tests/integration/mocks.py
+++ b/tests/integration/mocks.py
@@ -69,7 +69,7 @@ def set_system_prompt(
                     converted_value=system_prompt,
                     conversation_id=conversation_id,
                     attack_identifier=attack_identifier,
-                    labels=labels,
+                    labels=labels or {},
                 ).to_message()
             )