Merge branch 'main' into sphinx_pyscript

bandit/blacklists/calls.py

@@ -205,6 +205,9 @@
 |      |                     | - random.uniform                   |           |
 |      |                     | - random.triangular                |           |
 |      |                     | - random.randbytes                 |           |
+|      |                     | - random.randrange                 |           |
+|      |                     | - random.sample                    |           |
+|      |                     | - random.getrandbits               |           |
 +------+---------------------+------------------------------------+-----------+
 
 B312: telnetlib
@@ -515,6 +518,9 @@ def gen_blacklist():
                 "random.uniform",
                 "random.triangular",
                 "random.randbytes",
+                "random.sample",
+                "random.randrange",
+                "random.getrandbits",
             ],
             "Standard pseudo-random generators are not suitable for "
             "security/cryptographic purposes.",

bandit/plugins/pytorch_load_save.py → bandit/plugins/pytorch_load.py

@@ -2,21 +2,26 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 r"""
-==========================================
-B614: Test for unsafe PyTorch load or save
-==========================================
+==================================
+B614: Test for unsafe PyTorch load
+==================================
 
-This plugin checks for the use of `torch.load` and `torch.save`. Using
-`torch.load` with untrusted data can lead to arbitrary code execution, and
-improper use of `torch.save` might expose sensitive data or lead to data
-corruption. A safe alternative is to use `torch.load` with the `safetensors`
-library from hugingface, which provides a safe deserialization mechanism.
+This plugin checks for unsafe use of `torch.load`. Using `torch.load` with
+untrusted data can lead to arbitrary code execution. There are two safe
+alternatives:
+1. Use `torch.load` with `weights_only=True` where only tensor data is
+   extracted, and no arbitrary Python objects are deserialized
+2. Use the `safetensors` library from huggingface, which provides a safe
+   deserialization mechanism
+
+With `weights_only=True`, PyTorch enforces a strict type check, ensuring
+that only torch.Tensor objects are loaded.
 
 :Example:
 
 .. code-block:: none
 
-        >> Issue: Use of unsafe PyTorch load or save
+        >> Issue: Use of unsafe PyTorch load
         Severity: Medium   Confidence: High
         CWE: CWE-94 (https://cwe.mitre.org/data/definitions/94.html)
         Location: examples/pytorch_load_save.py:8
@@ -42,12 +47,11 @@
 
 @test.checks("Call")
 @test.test_id("B614")
-def pytorch_load_save(context):
+def pytorch_load(context):
     """
-    This plugin checks for the use of `torch.load` and `torch.save`. Using
-    `torch.load` with untrusted data can lead to arbitrary code execution,
-    and improper use of `torch.save` might expose sensitive data or lead
-    to data corruption.
+    This plugin checks for unsafe use of `torch.load`. Using `torch.load`
+    with untrusted data can lead to arbitrary code execution. The safe
+    alternative is to use `weights_only=True` or the safetensors library.
     """
     imported = context.is_module_imported_exact("torch")
     qualname = context.call_function_name_qual
@@ -59,14 +63,18 @@ def pytorch_load_save(context):
     if all(
         [
             "torch" in qualname_list,
-            func in ["load", "save"],
-            not context.check_call_arg_value("map_location", "cpu"),
+            func == "load",
         ]
     ):
+        # For torch.load, check if weights_only=True is specified
+        weights_only = context.get_call_arg_value("weights_only")
+        if weights_only == "True" or weights_only is True:
+            return
+
         return bandit.Issue(
             severity=bandit.MEDIUM,
             confidence=bandit.HIGH,
-            text="Use of unsafe PyTorch load or save",
+            text="Use of unsafe PyTorch load",
             cwe=issue.Cwe.DESERIALIZATION_OF_UNTRUSTED_DATA,
             lineno=context.get_lineno_for_call_arg("load"),
         )

doc/source/plugins/b614_pytorch_load.rst

@@ -0,0 +1,5 @@
+------------------
+B614: pytorch_load
+------------------
+
+.. automodule:: bandit.plugins.pytorch_load

examples/pytorch_load.py

@@ -0,0 +1,26 @@
+import torch
+import torchvision.models as models
+
+# Example of saving a model
+model = models.resnet18(pretrained=True)
+torch.save(model.state_dict(), 'model_weights.pth')
+
+# Example of loading the model weights in an insecure way (should trigger B614)
+loaded_model = models.resnet18()
+loaded_model.load_state_dict(torch.load('model_weights.pth'))
+
+# Example of loading with weights_only=True (should NOT trigger B614)
+safe_model = models.resnet18()
+safe_model.load_state_dict(torch.load('model_weights.pth', weights_only=True))
+
+# Example of loading with weights_only=False (should trigger B614)
+unsafe_model = models.resnet18()
+unsafe_model.load_state_dict(torch.load('model_weights.pth', weights_only=False))
+
+# Example of loading with map_location but no weights_only (should trigger B614)
+cpu_model = models.resnet18()
+cpu_model.load_state_dict(torch.load('model_weights.pth', map_location='cpu'))
+
+# Example of loading with both map_location and weights_only=True (should NOT trigger B614)
+safe_cpu_model = models.resnet18()
+safe_cpu_model.load_state_dict(torch.load('model_weights.pth', map_location='cpu', weights_only=True))

examples/random_module.py

@@ -11,6 +11,9 @@
 bad = random.uniform()
 bad = random.triangular()
 bad = random.randbytes()
+bad = random.sample()
+bad = random.randrange()
+bad = random.getrandbits()
 
 good = os.urandom()
 good = random.SystemRandom()

setup.cfg

@@ -155,8 +155,8 @@ bandit.plugins =
     #bandit/plugins/tarfile_unsafe_members.py
     tarfile_unsafe_members = bandit.plugins.tarfile_unsafe_members:tarfile_unsafe_members
 
-    #bandit/plugins/pytorch_load_save.py
-    pytorch_load_save = bandit.plugins.pytorch_load_save:pytorch_load_save
+    #bandit/plugins/pytorch_load.py
+    pytorch_load = bandit.plugins.pytorch_load:pytorch_load
 
     # bandit/plugins/trojansource.py
     trojansource = bandit.plugins.trojansource:trojansource

tests/functional/test_functional.py

@@ -365,8 +365,8 @@ def test_popen_wrappers(self):
     def test_random_module(self):
         """Test for the `random` module."""
         expect = {
-            "SEVERITY": {"UNDEFINED": 0, "LOW": 9, "MEDIUM": 0, "HIGH": 0},
-            "CONFIDENCE": {"UNDEFINED": 0, "LOW": 0, "MEDIUM": 0, "HIGH": 9},
+            "SEVERITY": {"UNDEFINED": 0, "LOW": 12, "MEDIUM": 0, "HIGH": 0},
+            "CONFIDENCE": {"UNDEFINED": 0, "LOW": 0, "MEDIUM": 0, "HIGH": 12},
         }
         self.check_example("random_module.py", expect)
 
@@ -872,13 +872,13 @@ def test_tarfile_unsafe_members(self):
         }
         self.check_example("tarfile_extractall.py", expect)
 
-    def test_pytorch_load_save(self):
-        """Test insecure usage of torch.load and torch.save."""
+    def test_pytorch_load(self):
+        """Test insecure usage of torch.load."""
         expect = {
-            "SEVERITY": {"UNDEFINED": 0, "LOW": 0, "MEDIUM": 4, "HIGH": 0},
-            "CONFIDENCE": {"UNDEFINED": 0, "LOW": 0, "MEDIUM": 0, "HIGH": 4},
+            "SEVERITY": {"UNDEFINED": 0, "LOW": 0, "MEDIUM": 3, "HIGH": 0},
+            "CONFIDENCE": {"UNDEFINED": 0, "LOW": 0, "MEDIUM": 0, "HIGH": 3},
         }
-        self.check_example("pytorch_load_save.py", expect)
+        self.check_example("pytorch_load.py", expect)
 
     def test_trojansource(self):
         expect = {