Path Traversal in Sharded Checkpoint Loader via Unsanitized `weight_map` Entries in `*.index.json`
Author: karnakarreddi
Pull request: no
Labels: name: bug

### System Info

Details
The vulnerable code is in get_checkpoint_shard_files in hub.py. When loading a sharded checkpoint from a local directory, the function reads an index JSON file and extracts shard filenames from the weight_map field without any validation:

with open(index_filename) as f:
    index = json.loads(f.read())

shard_filenames = sorted(set(index["weight_map"].values()))
These filenames are then joined directly to the model directory path:

if os.path.isdir(pretrained_model_name_or_path):
    shard_filenames = [os.path.join(pretrained_model_name_or_path, subfolder, f) for f in shard_filenames]
    return shard_filenames, sharded_metadata
There is no check for:

Path traversal sequences (..)
Absolute path prefixes (/)
Symbolic links
Whether the resolved paths remain within the model directory
The returned file paths are passed back to the caller (_get_resolved_checkpoint_files in modeling_utils.py), which uses them to load model weights — effectively enabling reads of arbitrary files the process has access to.

Why existing guards are insufficient
The caller _get_resolved_checkpoint_files only validates that the index file itself exists on disk (via os.path.isfile on the .safetensors.index.json path). It does not inspect or sanitize the contents of the index file before passing them to get_checkpoint_shard_files. An attacker-controlled directory needs only contain a valid index JSON file to satisfy this check.

The cached_files function (called for non-local/Hub models) does include file existence checks, but the local directory branch in get_checkpoint_shard_files returns immediately after os.path.join — cached_files is never reached for local paths.


https://github.com/huggingface/transformers/blob/ba06e3fbdf355c363ac067ebcda210017e90a852/src/transformers/utils/hub.py#L836 

### Who can help?

@Cyrilvallez 

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

PoC
Step 1: Create a malicious model directory
mkdir -p /tmp/malicious_model
Step 2: Create a crafted index file
Write the following to /tmp/malicious_model/model.safetensors.index.json:

{
  "metadata": {
    "total_size": 1000
  },
  "weight_map": {
    "model.layer.weight": "../../etc/passwd",
    "model.embed.weight": "../../etc/hostname"
  }
}
Step 3: Trigger the vulnerability
from transformers import AutoModel

 The loading pipeline will:
 1. Find model.safetensors.index.json in the local directory
 2. Set is_sharded = True
 3. Call get_checkpoint_shard_files, which will return:
      ["/tmp/malicious_model/../../etc/passwd",
       "/tmp/malicious_model/../../etc/hostname"]
    These resolve to /etc/passwd and /etc/hostname
model = AutoModel.from_pretrained("/tmp/malicious_model")


### Expected behavior

Observe the result
get_checkpoint_shard_files returns the traversed paths without error. The downstream model loading code will attempt to open and read these files as tensor data. While the files may fail to deserialize as valid safetensors, the file contents are accessed by the process, and depending on error handling, logging, or exception messages, data may be exposed.

A more targeted attack could point shard paths at:

Other users' cached model files in ~/.cache/huggingface/
API tokens stored in ~/.cache/huggingface/token
Application configuration or secrets files
Any file readable by the process

Vulnerability type: Arbitrary file read via path traversal (CWE-20 / CWE-22)

Who is affected:

Any user or automated system that loads models from untrusted local directories using from_pretrained or any code path that invokes get_checkpoint_shard_files.
ML pipelines and platforms that accept user-uploaded model directories (e.g., evaluation platforms, model hosting services, shared compute environments).
Developers who download and load models from sources outside the Hugging Face Hub without additional validation.
Attack prerequisites:

The attacker must be able to provide a local directory (or a directory downloaded/extracted from an untrusted source) that the victim passes to from_pretrained.
No authentication or special privileges are required beyond the ability to place files on the filesystem.
Recommended fix:
Sanitize all filenames extracted from the weight_map before constructing paths:

Reject any filename containing .. components or absolute path prefixes.
After joining paths, validate that the resolved path (via os.path.realpath) remains within the expected model directory.
Consider rejecting filenames with path separator characters entirely, since shard files should be flat names like model-00001-of-00003.safetensors.
Example fix:

import os

if os.path.isdir(pretrained_model_name_or_path):
    base_dir = os.path.realpath(os.path.join(pretrained_model_name_or_path, subfolder))
    safe_paths = []
    for f in shard_filenames:
        full_path = os.path.realpath(os.path.join(base_dir, f))
        if not full_path.startswith(base_dir + os.sep):
            raise ValueError(
                f"Shard filename '{f}' in the checkpoint index resolves outside "
                f"the model directory. This may indicate a malicious index file."
            )
        safe_paths.append(full_path)
    return safe_paths, sharded_metadata



Comments:
Hmn, this doesn't seem like a serious bug, right? The attacker would have to induce the user to load a malicious model, and the only result would be that it tries to read a file on the user's local system and fails because that file is not safetensors format. Even in the unlikely event that sensitive data is contained in the error message, the attacker would have no access to it because it's entirely local to the user machine.

My main concern is that weight_map entries are treated as trusted filesystem paths without any boundary validation. Even if safetensors parsing fails later, the loader still resolves and opens attacker-controlled paths outside the model directory. so i kept severity as medium though..

The "entirely local" argument assumes single-user deployments. Any service that calls from_pretrained on a user-supplied path, so evaluation APIs, CI pipelines, etc. is exposed. 
The fix is a handful of lines of path validation (negligible). Seems worth merging.

Thanks, that matches my concern as well. I can also put together a small PR with the boundary validation if that would help move this forward.


https://github.com/huggingface/transformers/pull/46134 I created small PR. @Rocketknight1 Please have a look whenever you get some time. 

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Humm, I don't really see the point here either. As @Rocketknight1 said, any wrong path will only fail when reading torch/safetensors files. So nothing can be exploited here

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.