CVE-2026-5497
Unbounded Frame Count in video/jpeg Base64 Data URL Processing Leads to OOM DoS in vllm-project/vllm
Description
vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.
INFO
Published Date :
June 11, 2026, 10:16 a.m.
Last Modified :
June 11, 2026, 10:16 a.m.
Remotely Exploit :
Yes !
Source :
[email protected]
CVSS Scores
| Score | Version | Severity | Vector | Exploitability Score | Impact Score | Source |
|---|---|---|---|---|---|---|
| CVSS 3.0 | HIGH | [email protected] |
Solution
- Update vLLM to a patched version.
- Enforce a maximum frame count for processing.
- Validate input data for excessive frame counts.
- Monitor server memory usage.
References to Advisories, Solutions, and Tools
Here, you will find a curated list of external links that provide in-depth
information, practical solutions, and valuable tools related to
CVE-2026-5497.
| URL | Resource |
|---|---|
| https://github.com/vllm-project/vllm/commit/58ee61422169ce17e08248f8efa1e9df434fe395 | |
| https://huntr.com/bounties/7bd92629-b396-4449-8f88-6c0092530eb4 |
CWE - Common Weakness Enumeration
While CVE identifies
specific instances of vulnerabilities, CWE categorizes the common flaws or
weaknesses that can lead to vulnerabilities. CVE-2026-5497 is
associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification
(CAPEC)
stores attack patterns, which are descriptions of the common attributes and
approaches employed by adversaries to exploit the CVE-2026-5497
weaknesses.
We scan GitHub repositories to detect new proof-of-concept exploits. Following list is a collection of public exploits and proof-of-concepts, which have been published on GitHub (sorted by the most recently updated).
Results are limited to the first 15 repositories due to potential performance issues.
The following list is the news that have been mention
CVE-2026-5497 vulnerability anywhere in the article.
The following table lists the changes that have been made to the
CVE-2026-5497 vulnerability over time.
Vulnerability history details can be useful for understanding the evolution of a vulnerability, and for identifying the most recent changes that may impact the vulnerability's severity, exploitability, or other characteristics.
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New CVE Received by [email protected]
Jun. 11, 2026
Action Type Old Value New Value Added Description vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication. Added CVSS V3 AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H Added CWE CWE-400 Added Reference https://github.com/vllm-project/vllm/commit/58ee61422169ce17e08248f8efa1e9df434fe395 Added Reference https://huntr.com/bounties/7bd92629-b396-4449-8f88-6c0092530eb4