How severe is GHSA-69j4-grxj-j64p?

GHSA-69j4-grxj-j64p has a CVSS score of 6.5/10, rated MEDIUM. Review your exposure and patch according to your risk tolerance.

Which packages are affected by GHSA-69j4-grxj-j64p?

GHSA-69j4-grxj-j64p affects the following packages: vllm (PyPI). Ecosystems affected: PyPI.

How do I fix GHSA-69j4-grxj-j64p?

Update vllm to 0.11.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-69j4-grxj-j64p is resolved across your whole dependency graph.

How do I detect GHSA-69j4-grxj-j64p in my PyPI dependencies?

Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for vllm. 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-69j4-grxj-j64p 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-69j4-grxj-j64p?

O3 pinpoints whether GHSA-69j4-grxj-j64p 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-69j4-grxj-j64p actively exploited in the wild?

No public exploit code has been indexed for GHSA-69j4-grxj-j64p 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-69j4-grxj-j64p?

GHSA-69j4-grxj-j64p has an EPSS (Exploit Prediction Scoring System) score of 0.3%, placing it in the 23th 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-69j4-grxj-j64p published, and has it been updated?

GHSA-69j4-grxj-j64p was published on November 20, 2025 and was last updated on February 4, 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-69j4-grxj-j64p

MEDIUM

vLLM vulnerable to DoS via large Chat Completion or Tokenization requests with specially crafted `chat_template_kwargs`

Also known asCVE-2025-62426

Published

Nov 20, 2025

Updated

Feb 4, 2026

Affected

1 pkg

Patched

1 / 1

Exploits

None indexed

EPSS Exploitation Probability

via FIRST.org ↗

0.3%probability of exploitation in next 30 days

Lower Risk23th percentile+0.23%

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

🐍vllm

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 /v1/chat/completions and /tokenize endpoints allow a chat_template_kwargs request parameter that is used in the code before it is properly validated against the chat template. With the right chat_template_kwargs parameters, it is possible to block processing of the API server for long periods of time, delaying all other requests

Details

In serving_engine.py, the chat_template_kwargs are unpacked into kwargs passed to chat_utils.py apply_hf_chat_template with no validation on the keys or values in that chat_template_kwargs dict. This means they can be used to override optional parameters in the apply_hf_chat_template method, such as tokenize, changing its default from False to True.

https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/openai/serving_engine.py#L809-L814

https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/chat_utils.py#L1602-L1610

Both serving_chat.py and serving_tokenization.py call into this _preprocess_chat method of serving_engine.py and they both pass in chat_template_kwargs.

So, a chat_template_kwargs like {"tokenize": True} makes tokenization happen as part of applying the chat template, even though that is not expected. Tokenization is a blocking operation, and with sufficiently large input can block the API server's event loop, which blocks handling of all other requests until this tokenization is complete.

This optional tokenize parameter to apply_hf_chat_template does not appear to be used, so one option would be to just hard-code that to always be False instead of allowing it to be optionally overridden by callers. A better option may be to not pass chat_template_kwargs as unpacked kwargs but instead as a dict, and only unpack them after the logic in apply_hf_chat_template that resolves the kwargs against the chat template.

Impact

Any authenticated user can cause a denial of service to a vLLM server with Chat Completion or Tokenize requests.

Fix

https://github.com/vllm-project/vllm/pull/27205

Affected Packages

1 total 1 fixed

Ecosystem	Package	Vulnerable range	Fix
🐍PyPI	`vllm`	≥ 0.5.5&&< 0.11.1	0.11.1

Detection & mitigation playbook

Open-source dependency

Detect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for vllm. 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 vllm to 0.11.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-69j4-grxj-j64p 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-69j4-grxj-j64p 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-69j4-grxj-j64p. 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 /v1/chat/completions and /tokenize endpoints allow a `chat_template_kwargs` request parameter that is used in the code before it is properly validated against the chat template. With the right `chat_template_kwargs` parameters, it is possible to block processing of the API server for long periods of time, delaying all other requests ### Details In serving_engine.py, the chat_template_kwargs are unpacked into kwargs passed to chat_utils.py `apply_hf_chat_template` with no validation on the keys or values in that chat_template_kwargs dict. This means they can be used to overrid

O3 Security · Impact-Aware SCA

Is GHSA-69j4-grxj-j64p in your dependencies?

O3 detects GHSA-69j4-grxj-j64p 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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