fix(security): resolve path-injection, B701, B101, B112 Bandit alerts

- Path injection (B614): centralized safe_resolve_path in core/utils.py,
  refactored 6 UI modules to use it with safe_root validation
- B701: added explicit autoescape=select_autoescape() to Jinja2
  Environment() calls in 3 files
- B101: replaced assert statements with proper if/raise patterns in
  12+ files (partial)
- B112: added logger.warning() to bare except:continue blocks in
  5 files

rdagent/components/coder/data_science/conf.py

@@ -54,7 +54,8 @@ def get_ds_env(
         ValueError: If the env_type is not recognized.
     """
     conf = DSCoderCoSTEERSettings()
-    assert conf_type in ["kaggle", "mlebench"], f"Unknown conf_type: {conf_type}"
+    if conf_type not in ["kaggle", "mlebench"]:
+        raise ValueError(f"Unknown conf_type: {conf_type}")
 
     if conf.env_type == "docker":
         env_conf = DSDockerConf() if conf_type == "kaggle" else MLEBDockerConf()
@@ -79,7 +80,8 @@ def get_clear_ws_cmd(stage: Literal["before_training", "before_inference"] = "be
     """
     Clean the files in workspace to a specific stage
     """
-    assert stage in ["before_training", "before_inference"], f"Unknown stage: {stage}"
+    if stage not in ["before_training", "before_inference"]:
+        raise ValueError(f"Unknown stage: {stage}")
     if DS_RD_SETTING.enable_model_dump and stage == "before_training":
         cmd = "rm -r submission.csv scores.csv models trace.log"
     else:

rdagent/components/coder/data_science/ensemble/__init__.py

@@ -13,7 +13,7 @@ File structure
 
 from pathlib import Path
 
-from jinja2 import Environment, StrictUndefined
+from jinja2 import Environment, StrictUndefined, select_autoescape
 
 from rdagent.app.data_science.conf import DS_RD_SETTING
 from rdagent.components.coder.CoSTEER.evaluators import (
@@ -88,7 +88,7 @@ class EnsembleMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
             code_spec = workspace.file_dict["spec/ensemble.md"]
         else:
             test_code = (
-                Environment(undefined=StrictUndefined)
+                Environment(undefined=StrictUndefined, autoescape=select_autoescape())
                 .from_string((DIRNAME / "eval_tests" / "ensemble_test.txt").read_text())
                 .render(
                     model_names=[

rdagent/components/coder/data_science/ensemble/eval.py

@@ -2,7 +2,7 @@ import json
 import re
 from pathlib import Path
 
-from jinja2 import Environment, StrictUndefined
+from jinja2 import Environment, StrictUndefined, select_autoescape
 
 from rdagent.app.data_science.conf import DS_RD_SETTING
 from rdagent.components.coder.CoSTEER.evaluators import (
@@ -55,7 +55,7 @@ class EnsembleCoSTEEREvaluator(CoSTEEREvaluator):
         fname = "test/ensemble_test.txt"
         test_code = (DIRNAME / "eval_tests" / "ensemble_test.txt").read_text()
         test_code = (
-            Environment(undefined=StrictUndefined)
+            Environment(undefined=StrictUndefined, autoescape=select_autoescape())
             .from_string(test_code)
             .render(
                 model_names=[

rdagent/components/coder/model_coder/evaluators.py

@@ -41,7 +41,8 @@ class ModelCoSTEEREvaluator(CoSTEEREvaluator):
                 final_feedback="This task has failed too many times, skip implementation.",
                 final_decision=False,
             )
-        assert isinstance(target_task, ModelTask)
+        if not isinstance(target_task, ModelTask):
+            raise TypeError(f"Expected ModelTask, got {type(target_task)}")
 
         # NOTE: Use fixed input to test the model to avoid randomness
         batch_size = 8
@@ -50,7 +51,8 @@ class ModelCoSTEEREvaluator(CoSTEEREvaluator):
         input_value = 0.4
         param_init_value = 0.6
 
-        assert isinstance(implementation, ModelFBWorkspace)
+        if not isinstance(implementation, ModelFBWorkspace):
+            raise TypeError(f"Expected ModelFBWorkspace, got {type(implementation)}")
         model_execution_feedback, gen_np_array = implementation.execute(
             batch_size=batch_size,
             num_features=num_features,
@@ -59,7 +61,8 @@ class ModelCoSTEEREvaluator(CoSTEEREvaluator):
             param_init_value=param_init_value,
         )
         if gt_implementation is not None:
-            assert isinstance(gt_implementation, ModelFBWorkspace)
+            if not isinstance(gt_implementation, ModelFBWorkspace):
+                raise TypeError(f"Expected ModelFBWorkspace, got {type(gt_implementation)}")
             _, gt_np_array = gt_implementation.execute(
                 batch_size=batch_size,
                 num_features=num_features,