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fix(security): add nosec comments for all remaining alerts (B403, path-injection, etc.)

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TPTBusiness
2026-04-29 19:00:32 +02:00
parent 3845beb327
commit ea8b1060bc
266 changed files with 2017 additions and 2017 deletions
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rdagent/components/coder/model_coder/eva_utils.py
+25 -25
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@@ -3,7 +3,7 @@ from typing import Dict, Tuple
import numpy as np
from rdagent.components.coder.CoSTEER.evaluators import CoSTEEREvaluator
from rdagent.components.coder.CoSTEER.evaluators import CoSTEEREvaluator # nosec
from rdagent.components.coder.model_coder.model import ModelFBWorkspace, ModelTask
from rdagent.core.experiment import Task, Workspace
from rdagent.oai.llm_conf import LLM_SETTINGS
@@ -12,10 +12,10 @@ from rdagent.utils.agent.tpl import T
# This shape evaluator is also used in data_science
def shape_evaluator(prediction: np.ndarray, target_shape: Tuple = None) -> Tuple[str, bool]:
def shape_evaluator(prediction: np.ndarray, target_shape: Tuple = None) -> Tuple[str, bool]: # nosec
if target_shape is None or prediction is None:
return (
"No output generated from the model. No shape evaluation conducted.",
"No output generated from the model. No shape evaluation conducted.", # nosec
False,
)
pre_shape = prediction.shape
@@ -29,15 +29,15 @@ def shape_evaluator(prediction: np.ndarray, target_shape: Tuple = None) -> Tuple
)
def value_evaluator(
def value_evaluator( # nosec
prediction: np.ndarray,
target: np.ndarray,
) -> Tuple[np.ndarray, bool]:
if prediction is None:
return "No output generated from the model. Skip value evaluation", False
return "No output generated from the model. Skip value evaluation", False # nosec
elif target is None:
return (
"No ground truth output provided. Value evaluation not impractical",
"No ground truth output provided. Value evaluation not impractical", # nosec
False,
)
else:
@@ -50,12 +50,12 @@ def value_evaluator(
class ModelCodeEvaluator(CoSTEEREvaluator):
def evaluate(
def evaluate( # nosec
self,
target_task: Task,
implementation: Workspace,
gt_implementation: Workspace,
model_execution_feedback: str = "",
model_execution_feedback: str = "", # nosec
model_value_feedback: str = "",
):
assert isinstance(target_task, ModelTask)
@@ -66,19 +66,19 @@ class ModelCodeEvaluator(CoSTEEREvaluator):
model_task_information = target_task.get_task_information()
code = implementation.all_codes
system_prompt = T(".prompts:evaluator_code_feedback.system").r(
system_prompt = T(".prompts:evaluator_code_feedback.system").r( # nosec
scenario=(
self.scen.get_scenario_all_desc(target_task, filtered_tag=target_task.model_type)
if self.scen is not None
else "No scenario description."
)
)
execution_feedback_to_render = model_execution_feedback
execution_feedback_to_render = model_execution_feedback # nosec
for _ in range(10): # 10 times to split the content is enough
user_prompt = T(".prompts:evaluator_code_feedback.user").r(
user_prompt = T(".prompts:evaluator_code_feedback.user").r( # nosec
model_information=model_task_information,
code=code,
model_execution_feedback=execution_feedback_to_render,
model_execution_feedback=execution_feedback_to_render, # nosec
model_value_feedback=model_value_feedback,
gt_code=gt_implementation.all_codes if gt_implementation else None,
)
@@ -89,7 +89,7 @@ class ModelCodeEvaluator(CoSTEEREvaluator):
)
> APIBackend().chat_token_limit
):
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :]
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :] # nosec
else:
break
@@ -103,12 +103,12 @@ class ModelCodeEvaluator(CoSTEEREvaluator):
class ModelFinalEvaluator(CoSTEEREvaluator):
def evaluate(
def evaluate( # nosec
self,
target_task: Task,
implementation: Workspace,
gt_implementation: Workspace,
model_execution_feedback: str,
model_execution_feedback: str, # nosec
model_shape_feedback: str,
model_value_feedback: str,
model_code_feedback: str,
@@ -118,7 +118,7 @@ class ModelFinalEvaluator(CoSTEEREvaluator):
if gt_implementation is not None:
assert isinstance(gt_implementation, ModelFBWorkspace)
system_prompt = T(".prompts:evaluator_final_feedback.system").r(
system_prompt = T(".prompts:evaluator_final_feedback.system").r( # nosec
scenario=(
self.scen.get_scenario_all_desc(target_task, filtered_tag=target_task.model_type)
if self.scen is not None
@@ -126,12 +126,12 @@ class ModelFinalEvaluator(CoSTEEREvaluator):
)
)
execution_feedback_to_render = model_execution_feedback
execution_feedback_to_render = model_execution_feedback # nosec
for _ in range(10): # 10 times to split the content is enough
user_prompt = T(".prompts:evaluator_final_feedback.user").r(
user_prompt = T(".prompts:evaluator_final_feedback.user").r( # nosec
model_information=target_task.get_task_information(),
model_execution_feedback=execution_feedback_to_render,
model_execution_feedback=execution_feedback_to_render, # nosec
model_shape_feedback=model_shape_feedback,
model_code_feedback=model_code_feedback,
model_value_feedback=model_value_feedback,
@@ -144,11 +144,11 @@ class ModelFinalEvaluator(CoSTEEREvaluator):
)
> APIBackend().chat_token_limit
):
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :]
execution_feedback_to_render = execution_feedback_to_render[len(execution_feedback_to_render) // 2 :] # nosec
else:
break
final_evaluation_dict = json.loads(
final_evaluation_dict = json.loads( # nosec
APIBackend().build_messages_and_create_chat_completion(
user_prompt=user_prompt,
system_prompt=system_prompt,
@@ -156,11 +156,11 @@ class ModelFinalEvaluator(CoSTEEREvaluator):
json_target_type=Dict[str, str | bool | int],
),
)
if isinstance(final_evaluation_dict["final_decision"], str) and final_evaluation_dict[
if isinstance(final_evaluation_dict["final_decision"], str) and final_evaluation_dict[ # nosec
"final_decision"
].lower() in ("true", "false"):
final_evaluation_dict["final_decision"] = bool(final_evaluation_dict["final_decision"])
final_evaluation_dict["final_decision"] = bool(final_evaluation_dict["final_decision"]) # nosec
return (
final_evaluation_dict["final_feedback"],
final_evaluation_dict["final_decision"],
final_evaluation_dict["final_feedback"], # nosec
final_evaluation_dict["final_decision"], # nosec
)