MAINT Enable low-hanging ruff rules: A, ERA, EXE, FA, FLY, ICN, ISC, LOG, PLE, Q, RSE, SLOT

pyproject.toml

@@ -252,16 +252,28 @@ fixable = [
     "YTT",
 ]
 select = [
+    "A",  # https://docs.astral.sh/ruff/rules/#flake8-builtins-a
     "B",  # https://docs.astral.sh/ruff/rules/#flake8-bugbear-b
     "C4",  # https://docs.astral.sh/ruff/rules/#flake8-comprehensions-c4
     "CPY001",  # missing-copyright-notice
     "D",  # https://docs.astral.sh/ruff/rules/#pydocstyle-d
     "DOC",  # https://docs.astral.sh/ruff/rules/#pydoclint-doc
+    "ERA",  # https://docs.astral.sh/ruff/rules/#eradicate-era
+    "EXE",  # https://docs.astral.sh/ruff/rules/#flake8-executable-exe
     "F401",  # unused-import
+    "FA",  # https://docs.astral.sh/ruff/rules/#flake8-future-annotations-fa
+    "FLY",  # https://docs.astral.sh/ruff/rules/#flynt-fly
     "I",  # isort
+    "ICN",  # https://docs.astral.sh/ruff/rules/#flake8-import-conventions-icn
+    "ISC",  # https://docs.astral.sh/ruff/rules/#flake8-implicit-str-concat-isc
+    "LOG",  # https://docs.astral.sh/ruff/rules/#flake8-logging-log
     "PIE",  # https://docs.astral.sh/ruff/rules/#flake8-pie-pie
+    "PLE",  # https://docs.astral.sh/ruff/rules/#pylint-ple
+    "Q",  # https://docs.astral.sh/ruff/rules/#flake8-quotes-q
     "RET",  # https://docs.astral.sh/ruff/rules/#flake8-return-ret
+    "RSE",  # https://docs.astral.sh/ruff/rules/#flake8-raise-rse
     "SIM",  # https://docs.astral.sh/ruff/rules/#flake8-simplify-sim
+    "SLOT",  # https://docs.astral.sh/ruff/rules/#flake8-slots-slot
     "TCH",  # https://docs.astral.sh/ruff/rules/#flake8-type-checking-tch
     "UP",  # https://docs.astral.sh/ruff/rules/#pyupgrade-up
     "W",  # https://docs.astral.sh/ruff/rules/#pycodestyle-w
@@ -300,7 +312,7 @@ notice-rgx = "Copyright \\(c\\) Microsoft Corporation\\.\\s*\\n.*Licensed under
 # Temporary ignores for pyrit/ subdirectories until issue #1176
 # https://github.com/Azure/PyRIT/issues/1176 is fully resolved
 # TODO: Remove these ignores once the issues are fixed
-"pyrit/{auxiliary_attacks,ui}/**/*.py" = ["B905", "D101", "D102", "D103", "D104", "D105", "D106", "D107", "D401", "D404", "D417", "D418", "DOC102", "DOC201", "DOC202", "DOC402", "DOC501", "SIM101", "SIM108"]
+"pyrit/{auxiliary_attacks,ui}/**/*.py" = ["A", "B905", "D101", "D102", "D103", "D104", "D105", "D106", "D107", "D401", "D404", "D417", "D418", "DOC102", "DOC201", "DOC202", "DOC402", "DOC501", "SIM101", "SIM108"]
 # Backend API routes raise HTTPException handled by FastAPI, not true exceptions
 "pyrit/backend/**/*.py" = ["DOC501", "B008"]
 "pyrit/__init__.py" = ["D104"]

pyrit/auth/azure_auth.py

@@ -325,7 +325,7 @@ def get_speech_config(resource_id: Union[str, None], key: Union[str, None], regi
     except ModuleNotFoundError as e:
         logger.error(
             "Could not import azure.cognitiveservices.speech. "
-            + "You may need to install it via 'pip install pyrit[speech]'"
+            "You may need to install it via 'pip install pyrit[speech]'"
         )
         raise e
 
@@ -361,7 +361,7 @@ def get_speech_config_from_default_azure_credential(resource_id: str, region: st
     except ModuleNotFoundError as e:
         logger.error(
             "Could not import azure.cognitiveservices.speech. "
-            + "You may need to install it via 'pip install pyrit[speech]'"
+            "You may need to install it via 'pip install pyrit[speech]'"
         )
         raise e
 

pyrit/backend/middleware/error_handlers.py

@@ -165,7 +165,7 @@ async def generic_exception_handler(
         # Log the full exception for debugging
         logger.error(
             f"Unhandled exception on {request.method} {request.url.path}: {exc}",
-            exc_info=True,
+            exc_info=True,  # noqa: LOG014
         )
 
         problem = ProblemDetail(

pyrit/common/path.py

@@ -6,7 +6,7 @@
 from appdirs import user_data_dir
 
 
-def get_default_data_path(dir: str) -> pathlib.Path:
+def get_default_data_path(dir: str) -> pathlib.Path:  # noqa: A002
     """
     Retrieve the default data path for PyRIT.
 

pyrit/exceptions/exception_classes.py

@@ -377,6 +377,6 @@ def handle_bad_request_exception(
             request=request, response_text_pieces=[resp_text], response_type="error", error="blocked"
         )
     else:
-        raise
+        raise  # noqa: PLE0704
 
     return response_entry

pyrit/exceptions/exceptions_helpers.py

@@ -49,7 +49,7 @@ def log_exception(retry_state: RetryCallState) -> None:
     try:
         exec_context = get_execution_context()
         if exec_context:
-            # Format: "objective scorer; TrueFalseScorer::_score_value_with_llm"
+            # Format: "objective scorer; TrueFalseScorer::_score_value_with_llm"  # noqa: ERA001
             role_display = exec_context.component_role.value.replace("_", " ")
             if exec_context.component_name:
                 for_clause = f"{role_display}. {exec_context.component_name}::{fn_name}"

pyrit/memory/memory_interface.py

@@ -392,7 +392,7 @@ def add_scores_to_memory(self, *, scores: Sequence[Score]) -> None:
                 message_piece_id = score.message_piece_id
                 pieces = self.get_message_pieces(prompt_ids=[str(message_piece_id)])
                 if not pieces:
-                    logging.error(f"MessagePiece with ID {message_piece_id} not found in memory.")
+                    logger.error(f"MessagePiece with ID {message_piece_id} not found in memory.")
                     continue
                 # auto-link score to the original prompt id if the prompt is a duplicate
                 if pieces[0].original_prompt_id != pieces[0].id:

pyrit/models/message_piece.py

@@ -34,7 +34,7 @@ def __init__(
         original_value_sha256: Optional[str] = None,
         converted_value: Optional[str] = None,
         converted_value_sha256: Optional[str] = None,
-        id: Optional[uuid.UUID | str] = None,
+        id: Optional[uuid.UUID | str] = None,  # noqa: A002
         conversation_id: Optional[str] = None,
         sequence: int = -1,
         labels: Optional[dict[str, str]] = None,

pyrit/models/scenario_result.py

@@ -67,7 +67,7 @@ def __init__(
         labels: Optional[dict[str, str]] = None,
         completion_time: Optional[datetime] = None,
         number_tries: int = 0,
-        id: Optional[uuid.UUID] = None,
+        id: Optional[uuid.UUID] = None,  # noqa: A002
         # Deprecated parameter - will be removed in 0.13.0
         objective_scorer: Optional["Scorer"] = None,
     ) -> None:

pyrit/models/score.py

@@ -59,7 +59,7 @@ def __init__(
         score_type: ScoreType,
         score_rationale: str,
         message_piece_id: str | uuid.UUID,
-        id: Optional[uuid.UUID | str] = None,
+        id: Optional[uuid.UUID | str] = None,  # noqa: A002
         score_category: Optional[list[str]] = None,
         score_metadata: Optional[dict[str, Union[str, int, float]]] = None,
         scorer_class_identifier: Union[ComponentIdentifier, dict[str, Any]],

pyrit/models/seeds/seed.py

@@ -201,7 +201,7 @@ def render_template_value_silent(self, **kwargs: Any) -> str:
             # Render the template with the provided kwargs
             return jinja_template.render(**kwargs)
         except Exception as e:
-            logging.error("Error rendering template: %s", e)
+            logger.error("Error rendering template: %s", e)
             return self.value
 
     async def set_sha256_value_async(self) -> None:

pyrit/models/storage_io.py

@@ -225,9 +225,9 @@ async def _upload_blob_async(self, file_name: str, data: bytes, content_type: st
             if isinstance(exc, ClientAuthenticationError):
                 logger.exception(
                     msg="Authentication failed. Please check that the container existence in the "
-                    + "Azure Storage Account and ensure the validity of the provided SAS token. If you "
-                    + "haven't set the SAS token as an environment variable use `az login` to "
-                    + "enable delegation-based SAS authentication to connect to the storage account"
+                    "Azure Storage Account and ensure the validity of the provided SAS token. If you "
+                    "haven't set the SAS token as an environment variable use `az login` to "
+                    "enable delegation-based SAS authentication to connect to the storage account"
                 )
                 raise
             logger.exception(msg=f"An unexpected error occurred: {exc}")

pyrit/prompt_converter/azure_speech_audio_to_text_converter.py

@@ -150,7 +150,7 @@ def recognize_audio(self, audio_bytes: bytes) -> str:
         except ModuleNotFoundError as e:
             logger.error(
                 "Could not import azure.cognitiveservices.speech. "
-                + "You may need to install it via 'pip install pyrit[speech]'"
+                "You may need to install it via 'pip install pyrit[speech]'"
             )
             raise e
 
@@ -219,7 +219,7 @@ def stop_cb(self, evt: Any, recognizer: Any) -> None:
         except ModuleNotFoundError as e:
             logger.error(
                 "Could not import azure.cognitiveservices.speech. "
-                + "You may need to install it via 'pip install pyrit[speech]'"
+                "You may need to install it via 'pip install pyrit[speech]'"
             )
             raise e
 

pyrit/prompt_converter/azure_speech_text_to_audio_converter.py

@@ -130,7 +130,7 @@ async def convert_async(self, *, prompt: str, input_type: PromptDataType = "text
         except ModuleNotFoundError as e:
             logger.error(
                 "Could not import azure.cognitiveservices.speech. "
-                + "You may need to install it via 'pip install pyrit[speech]'"
+                "You may need to install it via 'pip install pyrit[speech]'"
             )
             raise e
 
@@ -176,7 +176,7 @@ async def convert_async(self, *, prompt: str, input_type: PromptDataType = "text
                     logger.error(f"Error details: {cancellation_details.error_details}")
                 raise RuntimeError(
                     f"Speech synthesis canceled: {cancellation_details.reason}"
-                    + f"Error details: {cancellation_details.error_details}"
+                    f"Error details: {cancellation_details.error_details}"
                 )
         except Exception as e:
             logger.error("Failed to convert prompt to audio: %s", str(e))

pyrit/prompt_converter/insert_punctuation_converter.py

@@ -70,7 +70,7 @@ def _is_valid_punctuation(self, punctuation_list: list[str]) -> bool:
         Returns:
             bool: valid list and valid punctuations
         """
-        return all(str in string.punctuation for str in punctuation_list)
+        return all(char in string.punctuation for char in punctuation_list)
 
     async def convert_async(
         self, *, prompt: str, input_type: PromptDataType = "text", punctuation_list: Optional[list[str]] = None

pyrit/prompt_converter/transparency_attack_converter.py

@@ -6,7 +6,7 @@
 from io import BytesIO
 from pathlib import Path
 
-import numpy
+import numpy as np
 from PIL import Image
 
 from pyrit.identifiers import ComponentIdentifier
@@ -18,7 +18,7 @@
 
 class _AdamOptimizer:
     """
-    Implementation of the Adam Optimizer using NumPy. Adam optimization is a stochastic gradient
+    Implementation of the Adam Optimizer using np. Adam optimization is a stochastic gradient
     descent method that is based on adaptive estimation of first-order and second-order moments.
     For further details, see the original paper: `"Adam: A Method for Stochastic Optimization"`
     by D. P. Kingma and J. Ba, 2014: https://arxiv.org/abs/1412.6980.
@@ -44,24 +44,24 @@ def __init__(
         self.beta_1 = beta_1
         self.beta_2 = beta_2
         self.epsilon = epsilon
-        self.m: numpy.ndarray  # type: ignore[type-arg, unused-ignore]  # first moment vector
-        self.v: numpy.ndarray  # type: ignore[type-arg, unused-ignore]  # second moment vector
+        self.m: np.ndarray  # type: ignore[type-arg, unused-ignore]  # first moment vector
+        self.v: np.ndarray  # type: ignore[type-arg, unused-ignore]  # second moment vector
         self.t = 0  # initialize timestep
 
-    def update(self, *, params: numpy.ndarray, grads: numpy.ndarray) -> numpy.ndarray:  # type: ignore[type-arg, unused-ignore]
+    def update(self, *, params: np.ndarray, grads: np.ndarray) -> np.ndarray:  # type: ignore[type-arg, unused-ignore]
         """
         Perform a single update step using the Adam optimization algorithm.
 
         Args:
-            params (numpy.ndarray): Current parameter values to be optimized.
-            grads (numpy.ndarray): Gradients w.r.t. stochastic objective.
+            params (np.ndarray): Current parameter values to be optimized.
+            grads (np.ndarray): Gradients w.r.t. stochastic objective.
 
         Returns:
-            numpy.ndarray: Updated parameter values after applying the Adam optimization step.
+            np.ndarray: Updated parameter values after applying the Adam optimization step.
         """
         if self.t == 0:
-            self.m = numpy.zeros_like(params)
-            self.v = numpy.zeros_like(params)
+            self.m = np.zeros_like(params)
+            self.v = np.zeros_like(params)
         self.t += 1
 
         # Update biased first and second raw moment estimates
@@ -72,7 +72,7 @@ def update(self, *, params: numpy.ndarray, grads: numpy.ndarray) -> numpy.ndarra
         m_hat = self.m / (1 - self.beta_1**self.t)
         v_hat = self.v / (1 - self.beta_2**self.t)
 
-        params -= self.learning_rate * m_hat / (numpy.sqrt(v_hat) + self.epsilon)
+        params -= self.learning_rate * m_hat / (np.sqrt(v_hat) + self.epsilon)
         return params
 
 
@@ -200,15 +200,15 @@ def _build_identifier(self) -> ComponentIdentifier:
             }
         )
 
-    def _load_and_preprocess_image(self, path: str) -> numpy.ndarray:  # type: ignore[type-arg, unused-ignore]
+    def _load_and_preprocess_image(self, path: str) -> np.ndarray:  # type: ignore[type-arg, unused-ignore]
         """
         Load image, convert to grayscale, resize, and normalize for optimization.
 
         Args:
             path (str): The file path to the image.
 
         Returns:
-            numpy.ndarray: Preprocessed image as a normalized NumPy array.
+            np.ndarray: Preprocessed image as a normalized NumPy array.
 
         Raises:
             ValueError: If the image cannot be loaded or processed.
@@ -217,52 +217,52 @@ def _load_and_preprocess_image(self, path: str) -> numpy.ndarray:  # type: ignor
             with Image.open(path) as img:
                 img_gray = img.convert("L") if img.mode != "L" else img  # read as grayscale
                 img_resized = img_gray.resize(self.size, Image.Resampling.LANCZOS)
-                return numpy.array(img_resized, dtype=numpy.float32) / 255.0  # normalize to [0, 1]
+                return np.array(img_resized, dtype=np.float32) / 255.0  # normalize to [0, 1]
         except Exception as e:
             raise ValueError(f"Failed to load and preprocess image from {path}: {e}") from e
 
-    def _compute_mse_loss(self, blended_image: numpy.ndarray, target_tensor: numpy.ndarray) -> float:  # type: ignore[type-arg, unused-ignore]
+    def _compute_mse_loss(self, blended_image: np.ndarray, target_tensor: np.ndarray) -> float:  # type: ignore[type-arg, unused-ignore]
         """
         Compute Mean Squared Error (MSE) loss between blended and target images.
 
         Args:
-            blended_image (numpy.ndarray): The blended image array.
-            target_tensor (numpy.ndarray): The target benign image array.
+            blended_image (np.ndarray): The blended image array.
+            target_tensor (np.ndarray): The target benign image array.
 
         Returns:
             float: The computed MSE loss value.
         """
-        return float(numpy.mean(numpy.square(blended_image - target_tensor)))
+        return float(np.mean(np.square(blended_image - target_tensor)))
 
-    def _create_blended_image(self, attack_image: numpy.ndarray, alpha: numpy.ndarray) -> numpy.ndarray:  # type: ignore[type-arg, unused-ignore]
+    def _create_blended_image(self, attack_image: np.ndarray, alpha: np.ndarray) -> np.ndarray:  # type: ignore[type-arg, unused-ignore]
         """
         Create a blended image using the attack image and alpha transparency.
 
         Args:
-            attack_image (numpy.ndarray): The attack image array.
-            alpha (numpy.ndarray): The alpha transparency array.
+            attack_image (np.ndarray): The attack image array.
+            alpha (np.ndarray): The alpha transparency array.
 
         Returns:
-            numpy.ndarray: The blended image in LA mode.
+            np.ndarray: The blended image in LA mode.
         """
-        attack_image_uint8 = (attack_image * 255).astype(numpy.uint8)
-        transparency_uint8 = (alpha * 255).astype(numpy.uint8)
+        attack_image_uint8 = (attack_image * 255).astype(np.uint8)
+        transparency_uint8 = (alpha * 255).astype(np.uint8)
 
         # Create LA image: Luminance + Alpha (grayscale with transparency)
         height, width = attack_image_uint8.shape[:2]
-        la_image = numpy.zeros((height, width, 2), dtype=numpy.uint8)
+        la_image = np.zeros((height, width, 2), dtype=np.uint8)
         la_image[:, :, 0] = attack_image_uint8  # L (Luminance)
         la_image[:, :, 1] = transparency_uint8  # A (Alpha)
 
         return la_image
 
-    async def _save_blended_image(self, attack_image: numpy.ndarray, alpha: numpy.ndarray) -> str:  # type: ignore[type-arg, unused-ignore]
+    async def _save_blended_image(self, attack_image: np.ndarray, alpha: np.ndarray) -> str:  # type: ignore[type-arg, unused-ignore]
         """
         Save the blended image with transparency as a PNG file.
 
         Args:
-            attack_image (numpy.ndarray): The attack image array.
-            alpha (numpy.ndarray): The alpha transparency array.
+            attack_image (np.ndarray): The attack image array.
+            alpha (np.ndarray): The alpha transparency array.
 
         Returns:
             str: The file path to the saved blended image.
@@ -309,8 +309,8 @@ async def convert_async(self, *, prompt: str, input_type: PromptDataType = "imag
         background_image = self._load_and_preprocess_image(prompt)
         background_tensor = background_image * 0.5  # darkening for better blending optimization
 
-        alpha = numpy.ones_like(background_tensor)  # optimized to determine transparency pattern
-        white_background = numpy.ones_like(background_tensor)  # white canvas for blending simulation
+        alpha = np.ones_like(background_tensor)  # optimized to determine transparency pattern
+        white_background = np.ones_like(background_tensor)  # white canvas for blending simulation
 
         optimizer = _AdamOptimizer(learning_rate=self.learning_rate)
         grad_blended_alpha_constant = background_tensor - white_background
@@ -340,7 +340,7 @@ async def convert_async(self, *, prompt: str, input_type: PromptDataType = "imag
             grad_loss_blended = 2 * (blended_image - self._cached_benign_image) / blended_image.size
             grad_alpha = grad_loss_blended * grad_blended_alpha_constant
             alpha = optimizer.update(params=alpha, grads=grad_alpha)
-            alpha = numpy.clip(alpha, 0.0, 1.0)
+            alpha = np.clip(alpha, 0.0, 1.0)
 
         image_path = await self._save_blended_image(background_tensor, alpha)
         return ConverterResult(output_text=image_path, output_type="image_path")

pyrit/prompt_target/azure_blob_storage_target.py

@@ -144,9 +144,9 @@ async def _upload_blob_async(self, file_name: str, data: bytes, content_type: st
             if isinstance(exc, ClientAuthenticationError):
                 logger.exception(
                     msg="Authentication failed. Please check that the container existence in the "
-                    + "Azure Storage Account and ensure the validity of the provided SAS token. If you "
-                    + "haven't set the SAS token as an environment variable use `az login` to "
-                    + "enable delegation-based SAS authentication to connect to the storage account"
+                    "Azure Storage Account and ensure the validity of the provided SAS token. If you "
+                    "haven't set the SAS token as an environment variable use `az login` to "
+                    "enable delegation-based SAS authentication to connect to the storage account"
                 )
                 raise
             logger.exception(msg=f"An unexpected error occurred: {exc}")