CVE-2023-6730 (published 2023-12-19T13:15Z)

Deserialization of Untrusted Data in GitHub repository huggingface/transformers prior to 4.36.

CVSS v3 base score: 8.8, HIGH (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)

Weakness information:
cwe_id: CWE-502
cwe_name: Deserialization of Untrusted Data
description: The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid.
extended_description: 
            It is often convenient to serialize objects for communication or to save them for later use. However, deserialized data or code can often be modified without using the provided accessor functions if it does not use cryptography to protect itself. Furthermore, any cryptography would still be client-side security -- which is a dangerous security assumption.
            Data that is untrusted can not be trusted to be well-formed.
            When developers place no restrictions on "gadget chains," or series of instances and method invocations that can self-execute during the deserialization process (i.e., before the object is returned to the caller), it is sometimes possible for attackers to leverage them to perform unauthorized actions, like generating a shell.
         
         

Fix information:
repo_url: https://github.com/huggingface/transformers
commit_details: repo_url: https://github.com/huggingface/transformers
author: Yih-Dar
author_date: 2023-12-04 16:48:37+01:00
msg: Disallow `pickle.load` unless `TRUST_REMOTE_CODE=True` (#27776)

* fix

* fix

* Use TRUST_REMOTE_CODE

* fix doc

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
repository: repo_url: https://github.com/huggingface/transformers
repo_name: huggingface/transformers
description: 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
repo_language: Python
owner: huggingface
file_changes: filename: transfo-xl.md
change_type: ModificationType.MODIFY
diff: @@ -22,11 +22,17 @@ This model is in maintenance mode only, so we won't accept any new PRs changing
 
 We recommend switching to more recent models for improved security.
 
-In case you would still like to use `TransfoXL` in your experiments, we recommend using the [Hub checkpoint](https://huggingface.co/transfo-xl-wt103) with a specific revision to ensure you are downloading safe files from the Hub:
+In case you would still like to use `TransfoXL` in your experiments, we recommend using the [Hub checkpoint](https://huggingface.co/transfo-xl-wt103) with a specific revision to ensure you are downloading safe files from the Hub.
 
-```
+You will need to set the environment variable `TRUST_REMOTE_CODE` to `True` in order to allow the
+usage of `pickle.load()`:
+
+```python
+import os
 from transformers import TransfoXLTokenizer, TransfoXLLMHeadModel
 
+os.environ["TRUST_REMOTE_CODE"] = "True"
+
 checkpoint = 'transfo-xl-wt103'
 revision = '40a186da79458c9f9de846edfaea79c412137f97'
 

programming_language: Markdown
filename: tokenization_transfo_xl.py
change_type: ModificationType.MODIFY
diff: @@ -34,6 +34,7 @@ from ....utils import (
     is_torch_available,
     logging,
     requires_backends,
+    strtobool,
     torch_only_method,
 )
 
@@ -212,6 +213,14 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
             vocab_dict = None
             if pretrained_vocab_file is not None:
                 # Priority on pickle files (support PyTorch and TF)
+                if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")):
+                    raise ValueError(
+                        "This part uses `pickle.load` which is insecure and will execute arbitrary code that is "
+                        "potentially malicious. It's recommended to never unpickle data that could have come from an "
+                        "untrusted source, or that could have been tampered with. If you already verified the pickle "
+                        "data and decided to use it, you can set the environment variable "
+                        "`TRUST_REMOTE_CODE` to `True` to allow it."
+                    )
                 with open(pretrained_vocab_file, "rb") as f:
                     vocab_dict = pickle.load(f)
 
@@ -790,6 +799,13 @@ def get_lm_corpus(datadir, dataset):
         corpus = torch.load(fn_pickle)
     elif os.path.exists(fn):
         logger.info("Loading cached dataset from pickle...")
+        if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")):
+            raise ValueError(
+                "This part uses `pickle.load` which is insecure and will execute arbitrary code that is potentially "
+                "malicious. It's recommended to never unpickle data that could have come from an untrusted source, or "
+                "that could have been tampered with. If you already verified the pickle data and decided to use it, "
+                "you can set the environment variable `TRUST_REMOTE_CODE` to `True` to allow it."
+            )
         with open(fn, "rb") as fp:
             corpus = pickle.load(fp)
     else:

programming_language: Python
filename: retrieval_rag.py
change_type: ModificationType.MODIFY
diff: @@ -23,7 +23,7 @@ import numpy as np
 
 from ...tokenization_utils import PreTrainedTokenizer
 from ...tokenization_utils_base import BatchEncoding
-from ...utils import cached_file, is_datasets_available, is_faiss_available, logging, requires_backends
+from ...utils import cached_file, is_datasets_available, is_faiss_available, logging, requires_backends, strtobool
 from .configuration_rag import RagConfig
 from .tokenization_rag import RagTokenizer
 
@@ -131,6 +131,13 @@ class LegacyIndex(Index):
     def _load_passages(self):
         logger.info(f"Loading passages from {self.index_path}")
         passages_path = self._resolve_path(self.index_path, self.PASSAGE_FILENAME)
+        if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")):
+            raise ValueError(
+                "This part uses `pickle.load` which is insecure and will execute arbitrary code that is potentially "
+                "malicious. It's recommended to never unpickle data that could have come from an untrusted source, or "
+                "that could have been tampered with. If you already verified the pickle data and decided to use it, "
+                "you can set the environment variable `TRUST_REMOTE_CODE` to `True` to allow it."
+            )
         with open(passages_path, "rb") as passages_file:
             passages = pickle.load(passages_file)
         return passages
@@ -140,6 +147,13 @@ class LegacyIndex(Index):
         resolved_index_path = self._resolve_path(self.index_path, self.INDEX_FILENAME + ".index.dpr")
         self.index = faiss.read_index(resolved_index_path)
         resolved_meta_path = self._resolve_path(self.index_path, self.INDEX_FILENAME + ".index_meta.dpr")
+        if not strtobool(os.environ.get("TRUST_REMOTE_CODE", "False")):
+            raise ValueError(
+                "This part uses `pickle.load` which is insecure and will execute arbitrary code that is potentially "
+                "malicious. It's recommended to never unpickle data that could have come from an untrusted source, or "
+                "that could have been tampered with. If you already verified the pickle data and decided to use it, "
+                "you can set the environment variable `TRUST_REMOTE_CODE` to `True` to allow it."
+            )
         with open(resolved_meta_path, "rb") as metadata_file:
             self.index_id_to_db_id = pickle.load(metadata_file)
         assert (

programming_language: Python
filename: test_retrieval_rag.py
change_type: ModificationType.MODIFY
diff: @@ -14,7 +14,6 @@
 
 import json
 import os
-import pickle
 import shutil
 import tempfile
 from unittest import TestCase
@@ -174,37 +173,6 @@ class RagRetrieverTest(TestCase):
             )
         return retriever
 
-    def get_dummy_legacy_index_retriever(self):
-        dataset = Dataset.from_dict(
-            {
-                "id": ["0", "1"],
-                "text": ["foo", "bar"],
-                "title": ["Foo", "Bar"],
-                "embeddings": [np.ones(self.retrieval_vector_size + 1), 2 * np.ones(self.retrieval_vector_size + 1)],
-            }
-        )
-        dataset.add_faiss_index("embeddings", string_factory="Flat", metric_type=faiss.METRIC_INNER_PRODUCT)
-
-        index_file_name = os.path.join(self.tmpdirname, "hf_bert_base.hnswSQ8_correct_phi_128.c_index")
-        dataset.save_faiss_index("embeddings", index_file_name + ".index.dpr")
-        pickle.dump(dataset["id"], open(index_file_name + ".index_meta.dpr", "wb"))
-
-        passages_file_name = os.path.join(self.tmpdirname, "psgs_w100.tsv.pkl")
-        passages = {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
-        pickle.dump(passages, open(passages_file_name, "wb"))
-
-        config = RagConfig(
-            retrieval_vector_size=self.retrieval_vector_size,
-            question_encoder=DPRConfig().to_dict(),
-            generator=BartConfig().to_dict(),
-            index_name="legacy",
-            index_path=self.tmpdirname,
-        )
-        retriever = RagRetriever(
-            config, question_encoder_tokenizer=self.get_dpr_tokenizer(), generator_tokenizer=self.get_bart_tokenizer()
-        )
-        return retriever
-
     def test_canonical_hf_index_retriever_retrieve(self):
         n_docs = 1
         retriever = self.get_dummy_canonical_hf_index_retriever()
@@ -288,33 +256,6 @@ class RagRetrieverTest(TestCase):
             out = retriever.retrieve(hidden_states, n_docs=1)
             self.assertTrue(out is not None)
 
-    def test_legacy_index_retriever_retrieve(self):
-        n_docs = 1
-        retriever = self.get_dummy_legacy_index_retriever()
-        hidden_states = np.array(
-            [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
-        )
-        retrieved_doc_embeds, doc_ids, doc_dicts = retriever.retrieve(hidden_states, n_docs=n_docs)
-        self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size))
-        self.assertEqual(len(doc_dicts), 2)
-        self.assertEqual(sorted(doc_dicts[0]), ["text", "title"])
-        self.assertEqual(len(doc_dicts[0]["text"]), n_docs)
-        self.assertEqual(doc_dicts[0]["text"][0], "bar")  # max inner product is reached with second doc
-        self.assertEqual(doc_dicts[1]["text"][0], "foo")  # max inner product is reached with first doc
-        self.assertListEqual(doc_ids.tolist(), [[1], [0]])
-
-    def test_legacy_hf_index_retriever_save_and_from_pretrained(self):
-        retriever = self.get_dummy_legacy_index_retriever()
-        with tempfile.TemporaryDirectory() as tmp_dirname:
-            retriever.save_pretrained(tmp_dirname)
-            retriever = RagRetriever.from_pretrained(tmp_dirname)
-            self.assertIsInstance(retriever, RagRetriever)
-            hidden_states = np.array(
-                [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
-            )
-            out = retriever.retrieve(hidden_states, n_docs=1)
-            self.assertTrue(out is not None)
-
     @require_torch
     @require_tokenizers
     @require_sentencepiece

programming_language: Python