DOC: initializer-rename doc updates and notebook/test fixes (post #2157)

doc/code/setup/1_configuration.py

@@ -9,18 +9,6 @@
 #       jupytext_version: 1.19.4
 # ---
 
-# %%
-from pyrit.output import output_attack_async
-
-# ---
-# jupyter:
-#   jupytext:
-#     text_representation:
-#       extension: .py
-#       format_name: percent
-#       format_version: '1.3'
-#       jupytext_version: 1.19.1
-# ---
 # %% [markdown]
 # # Configuration
 #
@@ -148,6 +136,7 @@
     AttackScoringConfig,
     PromptSendingAttack,
 )
+from pyrit.output import output_attack_async
 from pyrit.prompt_normalizer.converter_configuration import (
     ConverterConfiguration,
 )

doc/code/targets/use_huggingface_chat_target.ipynb

@@ -41,14 +41,6 @@
    "id": "1",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "./git/copilot-worktrees/PyRIT/romanlutz-cautious-meme/.venv/Lib/site-packages/confusables/__init__.py:46: SyntaxWarning: \"\\*\" is an invalid escape sequence. Such sequences will not work in the future. Did you mean \"\\\\*\"? A raw string is also an option.\n",
-      "  space_regex = \"[\\*_~|`\\-\\.]*\" if include_character_padding else ''\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -69,13 +61,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running model: Qwen/Qwen2-0.5B-Instruct\n"
+      "Running model: HuggingFaceTB/SmolLM2-135M-Instruct\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2cb1ed2b5d6c4fa98456d1c217564010",
+       "model_id": "5ccc9dc2fe35480bbe25f5b8fd6bd50a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -89,12 +81,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "76523dac8fe04b87921ad444853385c3",
+       "model_id": "3fc4b0633f704408aa0ca3a28d38bc1e",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Fetching 10 files:   0%|          | 0/10 [00:00<?, ?it/s]"
+       "Fetching 25 files:   0%|          | 0/25 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -103,12 +95,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "650a316e795b4e84abdc46b06bc79cc1",
+       "model_id": "73eb775a3c3a419fbc79d3f3ce7d96ef",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Loading weights:   0%|          | 0/290 [00:00<?, ?it/s]"
+       "Loading weights:   0%|          | 0/272 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -118,7 +110,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average response time for Qwen/Qwen2-0.5B-Instruct: 7.43 seconds\n",
+      "Average response time for HuggingFaceTB/SmolLM2-135M-Instruct: 2.59 seconds\n",
       "\n",
       "\n",
       "\u001b[33m════════════════════════════════════════════════════════════════════════════════════════════════════\u001b[0m\n",
@@ -130,11 +122,11 @@
       "\u001b[1m  📋 Basic Information\u001b[0m\n",
       "\u001b[36m    • Objective: What is 3*3? Give me the solution.\u001b[0m\n",
       "\u001b[36m    • Attack Type: PromptSendingAttack\u001b[0m\n",
-      "\u001b[36m    • Conversation ID: 3e01b421-e41b-4c47-a51b-ea192f7cdb24\u001b[0m\n",
+      "\u001b[36m    • Conversation ID: 84048a6c-960d-483d-b54d-b19d8e0e3eb1\u001b[0m\n",
       "\n",
       "\u001b[1m  ⚡ Execution Metrics\u001b[0m\n",
       "\u001b[32m    • Turns Executed: 1\u001b[0m\n",
-      "\u001b[32m    • Execution Time: 12.20s\u001b[0m\n",
+      "\u001b[32m    • Execution Time: 4.61s\u001b[0m\n",
       "\n",
       "\u001b[1m  🎯 Outcome\u001b[0m\n",
       "\u001b[33m    • Status: ❓ UNDETERMINED\u001b[0m\n",
@@ -151,14 +143,12 @@
       "\u001b[33m────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
       "\u001b[1m\u001b[33m🔸 ASSISTANT\u001b[0m\n",
       "\u001b[33m────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
-      "\u001b[33m  The answer to \"What is 3 * 3?\" is 9.\u001b[0m\n",
-      "\u001b[33m  \u001b[0m\n",
-      "\u001b[33m    This is because the product of two numbers, where the first number (\u001b[0m\n",
+      "\u001b[33m  3*3 = 9\u001b[0m\n",
       "\n",
       "\u001b[34m────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
       "\n",
       "\u001b[2m\u001b[37m────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
-      "\u001b[2m\u001b[37m                            Report generated at: 2026-07-16 05:18:39 UTC                            \u001b[0m\n",
+      "\u001b[2m\u001b[37m                            Report generated at: 2026-07-13 23:25:00 UTC                            \u001b[0m\n",
       "\n",
       "\u001b[33m════════════════════════════════════════════════════════════════════════════════════════════════════\u001b[0m\n",
       "\u001b[1m\u001b[33m                                  ❓ ATTACK RESULT: UNDETERMINED ❓                                   \u001b[0m\n",
@@ -169,11 +159,11 @@
       "\u001b[1m  📋 Basic Information\u001b[0m\n",
       "\u001b[36m    • Objective: What is 4*4? Give me the solution.\u001b[0m\n",
       "\u001b[36m    • Attack Type: PromptSendingAttack\u001b[0m\n",
-      "\u001b[36m    • Conversation ID: 76b845b6-0e72-4a80-a793-09652c4d7405\u001b[0m\n",
+      "\u001b[36m    • Conversation ID: bc388b83-e4d3-441b-80f1-dc8d433bc314\u001b[0m\n",
       "\n",
       "\u001b[1m  ⚡ Execution Metrics\u001b[0m\n",
       "\u001b[32m    • Turns Executed: 1\u001b[0m\n",
-      "\u001b[32m    • Execution Time: 2.63s\u001b[0m\n",
+      "\u001b[32m    • Execution Time: 557ms\u001b[0m\n",
       "\n",
       "\u001b[1m  🎯 Outcome\u001b[0m\n",
       "\u001b[33m    • Status: ❓ UNDETERMINED\u001b[0m\n",
@@ -190,17 +180,13 @@
       "\u001b[33m────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
       "\u001b[1m\u001b[33m🔸 ASSISTANT\u001b[0m\n",
       "\u001b[33m────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
-      "\u001b[33m  The result of multiplying 4 by itself four times is:\u001b[0m\n",
-      "\u001b[33m    256.\u001b[0m\n",
-      "\u001b[33m  \u001b[0m\n",
-      "\u001b[33m    Here's why:\u001b[0m\n",
-      "\u001b[33m    First, we multiply 4 and\u001b[0m\n",
+      "\u001b[33m  4*4 = 16.\u001b[0m\n",
       "\n",
       "\u001b[34m────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
       "\n",
       "\u001b[2m\u001b[37m────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
-      "\u001b[2m\u001b[37m                            Report generated at: 2026-07-16 05:18:39 UTC                            \u001b[0m\n",
-      "Qwen/Qwen2-0.5B-Instruct: 7.43 seconds\n"
+      "\u001b[2m\u001b[37m                            Report generated at: 2026-07-13 23:25:00 UTC                            \u001b[0m\n",
+      "HuggingFaceTB/SmolLM2-135M-Instruct: 2.59 seconds\n"
      ]
     }
    ],
@@ -218,7 +204,7 @@
     "await initialize_pyrit_async(memory_db_type=IN_MEMORY)  # type: ignore\n",
     "\n",
     "# models to test\n",
-    "model_id = \"Qwen/Qwen2-0.5B-Instruct\"\n",
+    "model_id = \"HuggingFaceTB/SmolLM2-135M-Instruct\"\n",
     "\n",
     "# List of prompts to send\n",
     "prompt_list = [\"What is 3*3? Give me the solution.\", \"What is 4*4? Give me the solution.\"]\n",
@@ -228,51 +214,44 @@
     "\n",
     "print(f\"Running model: {model_id}\")\n",
     "\n",
-    "try:\n",
-    "    # Initialize HuggingFaceChatTarget with the current model\n",
-    "    target = HuggingFaceChatTarget(model_id=model_id, use_cuda=False, tensor_format=\"pt\", max_new_tokens=30)\n",
-    "\n",
-    "    # Initialize the attack\n",
-    "    attack = PromptSendingAttack(objective_target=target)\n",
+    "# Initialize HuggingFaceChatTarget with the current model\n",
+    "target = HuggingFaceChatTarget(model_id=model_id, use_cuda=False, tensor_format=\"pt\", max_new_tokens=30)\n",
     "\n",
-    "    # Record start time\n",
-    "    start_time = time.time()\n",
+    "# Initialize the attack\n",
+    "attack = PromptSendingAttack(objective_target=target)\n",
     "\n",
-    "    # Send prompts asynchronously\n",
-    "    responses = await AttackExecutor().execute_attack_async(  # type: ignore\n",
-    "        attack=attack,\n",
-    "        objectives=prompt_list,\n",
-    "    )\n",
+    "# Record start time\n",
+    "start_time = time.time()\n",
     "\n",
-    "    # Record end time\n",
-    "    end_time = time.time()\n",
+    "# Send prompts asynchronously\n",
+    "responses = await AttackExecutor().execute_attack_async(  # type: ignore\n",
+    "    attack=attack,\n",
+    "    objectives=prompt_list,\n",
+    ")\n",
     "\n",
-    "    # Calculate total and average response time\n",
-    "    total_time = end_time - start_time\n",
-    "    avg_time = total_time / len(prompt_list)\n",
-    "    model_times[model_id] = avg_time\n",
+    "# Record end time\n",
+    "end_time = time.time()\n",
     "\n",
-    "    print(f\"Average response time for {model_id}: {avg_time:.2f} seconds\\n\")\n",
+    "# Calculate total and average response time\n",
+    "total_time = end_time - start_time\n",
+    "avg_time = total_time / len(prompt_list)\n",
+    "model_times[model_id] = avg_time\n",
     "\n",
-    "    # Print the conversations\n",
-    "    for result in responses:\n",
-    "        await output_attack_async(result)\n",
+    "print(f\"Average response time for {model_id}: {avg_time:.2f} seconds\\n\")\n",
     "\n",
-    "except Exception as e:\n",
-    "    print(f\"An error occurred with model {model_id}: {e}\\n\")\n",
-    "    model_times[model_id] = None\n",
+    "# Print the conversations\n",
+    "for result in responses:\n",
+    "    await output_attack_async(result)\n",
     "\n",
     "# Print the model average time\n",
-    "if model_times[model_id] is not None:\n",
-    "    print(f\"{model_id}: {model_times[model_id]:.2f} seconds\")\n",
-    "else:\n",
-    "    print(f\"{model_id}: Error occurred, no average time calculated.\")"
+    "print(f\"{model_id}: {model_times[model_id]:.2f} seconds\")"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "-all"
+   "cell_metadata_filter": "-all",
+   "main_language": "python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -284,7 +263,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.14.4"
+   "version": "3.12.12"
   }
  },
  "nbformat": 4,

doc/code/targets/use_huggingface_chat_target.py

@@ -6,8 +6,9 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.19.0
+#       jupytext_version: 1.19.4
 # ---
+
 # %% [markdown]
 # # HuggingFace Chat Target - optional
 #
@@ -50,7 +51,7 @@
 await initialize_pyrit_async(memory_db_type=IN_MEMORY)  # type: ignore
 
 # models to test
-model_id = "Qwen/Qwen2-0.5B-Instruct"
+model_id = "HuggingFaceTB/SmolLM2-135M-Instruct"
 
 # List of prompts to send
 prompt_list = ["What is 3*3? Give me the solution.", "What is 4*4? Give me the solution."]
@@ -60,42 +61,34 @@
 
 print(f"Running model: {model_id}")
 
-try:
-    # Initialize HuggingFaceChatTarget with the current model
-    target = HuggingFaceChatTarget(model_id=model_id, use_cuda=False, tensor_format="pt", max_new_tokens=30)
-
-    # Initialize the attack
-    attack = PromptSendingAttack(objective_target=target)
+# Initialize HuggingFaceChatTarget with the current model
+target = HuggingFaceChatTarget(model_id=model_id, use_cuda=False, tensor_format="pt", max_new_tokens=30)
 
-    # Record start time
-    start_time = time.time()
+# Initialize the attack
+attack = PromptSendingAttack(objective_target=target)
 
-    # Send prompts asynchronously
-    responses = await AttackExecutor().execute_attack_async(  # type: ignore
-        attack=attack,
-        objectives=prompt_list,
-    )
+# Record start time
+start_time = time.time()
 
-    # Record end time
-    end_time = time.time()
+# Send prompts asynchronously
+responses = await AttackExecutor().execute_attack_async(  # type: ignore
+    attack=attack,
+    objectives=prompt_list,
+)
 
-    # Calculate total and average response time
-    total_time = end_time - start_time
-    avg_time = total_time / len(prompt_list)
-    model_times[model_id] = avg_time
+# Record end time
+end_time = time.time()
 
-    print(f"Average response time for {model_id}: {avg_time:.2f} seconds\n")
+# Calculate total and average response time
+total_time = end_time - start_time
+avg_time = total_time / len(prompt_list)
+model_times[model_id] = avg_time
 
-    # Print the conversations
-    for result in responses:
-        await output_attack_async(result)
+print(f"Average response time for {model_id}: {avg_time:.2f} seconds\n")
 
-except Exception as e:
-    print(f"An error occurred with model {model_id}: {e}\n")
-    model_times[model_id] = None
+# Print the conversations
+for result in responses:
+    await output_attack_async(result)
 
 # Print the model average time
-if model_times[model_id] is not None:
-    print(f"{model_id}: {model_times[model_id]:.2f} seconds")
-else:
-    print(f"{model_id}: Error occurred, no average time calculated.")
+print(f"{model_id}: {model_times[model_id]:.2f} seconds")

doc/getting_started/configuration.md

@@ -37,7 +37,7 @@ from pyrit.setup.initializers import ScorerInitializer, TargetInitializer
 await initialize_pyrit_async(memory_db_type="InMemory", initializers=[TargetInitializer(), ScorerInitializer()])
 ```
 
-This gives you an in-memory database and default converter/scorer config — enough to run most notebooks and examples. Replace the endpoint/key/model for your provider (Azure, Ollama, Groq, HuggingFace, etc.).
+This gives you an in-memory database with configured targets and scorers registered for selection by name or tag. Replace the endpoint/key/model for your provider (Azure, Ollama, Groq, HuggingFace, etc.).
 
 ## For Persistent Setup
 

doc/getting_started/pyrit_conf.md

@@ -117,6 +117,10 @@ Most users should enable the following initializers. These are what the `.pyrit_
 **Execution order follows listing order.** Initializers execute in the order they appear in the config. Ensure dependencies are satisfied — for example, list `target` before `scorer` since scorers need targets to be registered first.
 ```
 
+```{important}
+The default initializers were consolidated and renamed as part of the cleanup leading up to the **v1.0.0** release. Configurations created before v1.0.0 may still reference the removed names `simple`, `airt`, `scenario_technique`, or `scenario_objective_list` — replace them with the current initializers listed above.
+```
+
 The recommended config:
 
 ```yaml

doc/gui/0_gui.md

@@ -151,7 +151,7 @@ For `AzureMLChatTarget`, additional fields are available: **Max New Tokens**, **
 
 #### Auto-Populating Targets
 
-Targets can also be auto-populated by adding an initializer (e.g., `airt`) to your `~/.pyrit/.pyrit_conf` file. This reads endpoints from your `.env` and `.env.local` files. See [.pyrit_conf_example](https://github.com/microsoft/PyRIT/blob/main/.pyrit_conf_example) for details.
+Targets can also be auto-populated by adding the `target` initializer to your `~/.pyrit/.pyrit_conf` file. This reads endpoints from your `.env` and `.env.local` files. See [.pyrit_conf_example](https://github.com/microsoft/PyRIT/blob/main/.pyrit_conf_example) for details.
 
 ---
 

doc/scanner/1_pyrit_scan.ipynb

@@ -610,7 +610,7 @@
     "\n",
     "PyRITInitializers are how you can configure the CLI scanner. PyRIT includes several built-in initializers you can use with the `--initializers` flag.\n",
     "\n",
-    "The `--list-initializers` command shows all available initializers. Initializers are referenced by their filename (e.g., `target`, `scorer`, `simple`) regardless of which subdirectory they're in.\n",
+    "The `--list-initializers` command shows all available initializers. Initializers are referenced by their filename (e.g., `target`, `scorer`) regardless of which subdirectory they're in.\n",
     "\n",
     "List the available initializers using the --list-initializers flag."
    ]

doc/scanner/1_pyrit_scan.py

@@ -58,7 +58,7 @@
 #
 # PyRITInitializers are how you can configure the CLI scanner. PyRIT includes several built-in initializers you can use with the `--initializers` flag.
 #
-# The `--list-initializers` command shows all available initializers. Initializers are referenced by their filename (e.g., `target`, `scorer`, `simple`) regardless of which subdirectory they're in.
+# The `--list-initializers` command shows all available initializers. Initializers are referenced by their filename (e.g., `target`, `scorer`) regardless of which subdirectory they're in.
 #
 # List the available initializers using the --list-initializers flag.
 

pyrit/registry/components/initializer_registry.py

@@ -67,7 +67,7 @@ class InitializerRegistry(ParamBagRegistry["PyRITInitializer", InitializerMetada
     ``pyrit/setup/initializers`` directory structure via a filesystem scan (so
     ``_discover`` is overridden rather than supplying ``_base_type`` /
     ``_discovery_package``). Initializers are identified by their suffix-stripped
-    snake_case class name (e.g., ``"objective_target"``, ``"simple"``); the
+    snake_case class name (e.g., ``"objective_target"``, ``"technique"``); the
     directory structure is used for organization but not exposed to users.
     """