GHSA-mrw7-hf4f-83pf
HIGHvLLM deserialization vulnerability leading to DoS and potential RCE
EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
Blast Radius
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Description
Summary
A memory corruption vulnerability that leading to a crash (denial-of-service) and potentially remote code execution (RCE) exists in vLLM versions 0.10.2 and later, in the Completions API endpoint. When processing user-supplied prompt embeddings, the endpoint loads serialized tensors using torch.load() without sufficient validation.
Due to a change introduced in PyTorch 2.8.0, sparse tensor integrity checks are disabled by default. As a result, maliciously crafted tensors can bypass internal bounds checks and trigger an out-of-bounds memory write during the call to to_dense(). This memory corruption can crash vLLM and potentially lead to code execution on the server hosting vLLM.
Details
A vulnerability that can lead to RCE from the completions API endpoint exists in vllm, where due to missing checks when loading user-provided tensors, an out-of-bounds write can be triggered. This happens because the default behavior of torch.load(tensor, weights_only=True) since pytorch 2.8.0 is to not perform validity checks for sparse tensors, and this needs to be enabled explicitly using the torch.sparse.check_sparse_tensor_invariants context manager.
The vulnerability is in the following code in vllm/entrypoints/renderer.py:148
def _load_and_validate_embed(embed: bytes) -> EngineEmbedsPrompt:
tensor = torch.load(
io.BytesIO(pybase64.b64decode(embed, validate=True)),
weights_only=True,
map_location=torch.device("cpu"),
)
assert isinstance(tensor, torch.Tensor) and tensor.dtype in (
torch.float32,
torch.bfloat16,
torch.float16,
)
tensor = tensor.to_dense()
Because of the missing checks, loading invalid prompt embedding tensors provided by the user can cause an out-of-bounds write in the call to to_dense .
Impact
All users with access to this API are able to exploit this vulnerability. Unsafe deserialization of untrusted input can be abused to achieve DoS and potentially remote code execution (RCE) in the vLLM server process. This impacts deployments running vLLM as a server or any instance that deserializes untrusted/model-provided payloads.
Fix
https://github.com/vllm-project/vllm/pull/27204
Acknowledgements
Finder: AXION Security Research Team (Omri Fainaro, Bary Levy): discovery and coordinated disclosure.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | vllm | ≥ 0.10.2&&< 0.11.1 | 0.11.1 |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for vllm. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update vllm to 0.11.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-mrw7-hf4f-83pf is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether GHSA-mrw7-hf4f-83pf is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to GHSA-mrw7-hf4f-83pf. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-mrw7-hf4f-83pf in your dependencies?
O3 detects GHSA-mrw7-hf4f-83pf across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.