CVE-2026-93989

Analyzed Analyzed - Analysis Complete

vLLM SamplingParams Out-of-Bounds Token Index Vulnerability

Publication date: 2026-09-19

Last updated on: 2026-09-28

Assigner: VulnCheck

Description

vLLM through 0.29.0 fails to properly validate bad_words token indices against the model's generation output width in SamplingParams.update_from_tokenizer(). Attackers can supply out-of-bounds token indices that corrupt logits memory of concurrent requests, causing different in-flight HTTP requests to return incorrect tokens.

CVSS Scores

EPSS Scores

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Meta Information

Published

2026-09-19

Last Modified

2026-09-28

Generated

2026-10-10

AI Q&A

2026-09-20

EPSS Evaluated

2026-10-09

NVD

CVE-2026-93989

Affected Vendors & Products

Vendor	Product	Version / Range
vllm	vllm	to 0.29.0 (inc)

Helpful Resources

Exploitability

CWE

KEV

CWE ID	Description
CWE-129	The product uses untrusted input when calculating or using an array index, but the product does not validate or incorrectly validates the index to ensure the index references a valid position within the array.

Attack-Flow Graph

Executive Summary

vLLM through version 0.29.0 has a flaw in its SamplingParams.update_from_tokenizer() function where it fails to validate bad_words token indices against the model's output width. This allows attackers to provide out-of-bounds token indices that corrupt logits memory for concurrent requests, leading to incorrect tokens being returned in different in-flight HTTP requests.

Detection Guidance

Detection requires code review or runtime monitoring of vLLM instances. Check if SamplingParams.update_from_tokenizer() improperly validates bad_words token indices against model output width. Monitor for memory corruption or incorrect token returns in concurrent HTTP requests.

Impact Analysis

This vulnerability could cause applications using vLLM to return incorrect or corrupted responses to users. If exploited, it may lead to data processing errors, misinformation, or unexpected behavior in systems relying on vLLM for language model inference.

Compliance Impact

This vulnerability does not directly affect compliance with GDPR or HIPAA as it involves memory corruption in token indices during model inference, not data breaches or unauthorized access. However, if incorrect tokens are returned in responses, it could lead to improper data handling or processing errors, potentially violating integrity requirements under these regulations.

Mitigation Strategies

Upgrade vLLM to a patched version beyond 0.29.0. If upgrading is not possible, restrict concurrent HTTP requests and validate all token indices manually before processing. Monitor logs for memory corruption or incorrect token returns.

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