CVE-2026-41523

### Summary An `assert`-based security check in vLLM's activation function loading allows any unauthenticated attacker to achieve arbitrary code execution on the server by publishing a malicious HuggingFace model, when vLLM runs in Python optimized mode (`python -O` or `PYTHONOPTIMIZE=1`). ### Details vLLM uses an `assert` statement at [`vllm/model_executor/layers/pooler/activations.py:48`](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/pooler/activations.py#L48) as its sole security control to restrict which activation functions can be loaded from a HuggingFace model's `config.json`: ```python # vllm/model_executor/layers/pooler/activations.py:35-53 function_name: str | None = None if ( hasattr(config, "sentence_transformers") and "activation_fn" in config.sentence_transformers ): function_name = config.sentence_transformers["activation_fn"] elif ( hasattr(config, "sbert_ce_default_activation_function") and config.sbert_ce_default_activation_function is not None ): function_name = config.sbert_ce_default_activation_function if function_name is not None: assert function_name.startswith("torch.nn.modules."), ( "Loading of activation functions is restricted to " "torch.nn.modules for security reasons" ) fn = resolve_obj_by_qualname(function_name)() ``` Python's `assert` statements are stripped at compile time when running in optimized mode (`python -O` or `PYTHONOPTIMIZE=1`). When the assert is absent, the attacker-controlled `function_name` from the model's `config.json` is passed directly to [`resolve_obj_by_qualname()`](https://github.com/vllm-project/vllm/blob/main/vllm/utils/import_utils.py#L106) — an unrestricted import gadget: ```python def resolve_obj_by_qualname(qualname: str) -> Any: module_name, obj_name = qualname.rsplit(".", 1) module = importlib.import_module(module_name) return getattr(module, obj_name) ``` This is the same vulnerability class as **CVE-2017-1000433** (pysaml2 assert-based auth bypass), flagged by Bandit B101 and Ruff S101, and the reason Django proactively replaced all assert-based security checks (ticket #32508). **Attacker-controlled input sources:** - `config.sentence_transformers["activation_fn"]` (line 40) - `config.sbert_ce_default_activation_function` (line 45) **Affected call sites** — `get_act_fn()` is called via `resolve_classifier_act_fn()` from: - `vllm/model_executor/layers/pooler/seqwise/poolers.py:122` — SequencePooler - `vllm/model_executor/layers/pooler/tokwise/poolers.py:130` — TokenPooler **Broader systemic risk:** `resolve_obj_by_qualname` is called from ~20 locations across the codebase with no validation of its own. Any future caller feeding user-controlled input to it without validation creates the same vulnerability class. **Suggested fix:** Replace the `assert` with an explicit conditional raise: ```python if not function_name.startswith("torch.nn.modules."): raise ValueError( "Loading of activation functions is restricted to " "torch.nn.modules for security reasons" ) ``` ### Impact **Arbitrary code execution.** A malicious model author publishes a HuggingFace model with a crafted `config.json`. When a victim loads this model with vLLM running under `python -O` or `PYTHONOPTIMIZE=1`, arbitrary code executes during model initialization with the privileges of the vLLM process. The attack requires: 1. Victim loads a malicious model from HuggingFace (user interaction) 2. vLLM runs under `python -O` or `PYTHONOPTIMIZE=1` (documented in production use) 3. Model uses a cross-encoder architecture (e.g. BERT or RoBERTa with sequence classification) **Coordinated disclosure note:** This vulnerability was also reported via huntr.com on April 2, 2026 (https://huntr.com/bounties/dcb05b04-e625-41e7-adbc-bbae0cc2d64c). A GitHub Security Advisory was also filed because it is vLLM's stated preferred disclosure channel per SECURITY.md. ### Fix A fix for this was introduced in this commit: https://github.com/vllm-project/vllm/commit/b3c7ffcab82c2439726f8cb213800f6f38c023d3

No confirmed active exploitation of CVE-2026-41523 has been reported, but organizations should still patch proactively.

Update to patched version: vLLM 0.22.0.

CVE-2026-41523 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.05%.