CVE-2026-5497
HIGH 7.5vLLM 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.
Severe if exploited (CVSS 7.5), but no known exploitation and low modeled probability. Patch on a normal cadence.
Exploitation likelihood
0.5%chance of exploitation in 30 days · 42nd percentile
Impact if exploited
7.5CVSS 3.1 · HIGH
- ConfidentialityNone
- IntegrityNone
- AvailabilityHigh
What an attacker needs
- ✓Access: Reachable over the network — no local access needed
- ✓Privileges: No account or privileges required
- ✓User interaction: No user interaction needed
- ✓Complexity: No special conditions — reliably repeatable
✓ lowers the bar for an attacker · ⚠ raises it
Affected
Vendors Vllm Project Red Hat
Products Vllm Project/Vllm Red Hat Enterprise Linux Ai 3.4 Red Hat Ai Inference Server Red Hat Enterprise Linux Ai (Rhel Ai) 3 Red Hat Openshift Ai (Rhoai)
Weakness (CWE)
- CWE-400: Uncontrolled resource consumption
- CWE-770: Allocation without limits
CVSS vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
References
Advisories
Technical & other
- https://huntr.com/bounties/7bd92629-b396-4449-8f88-6c0092530eb4
- https://github.com/vllm-project/vllm/commit/58ee61422169ce17e08248f8efa1e9df434fe395
- https://access.redhat.com/security/cve/CVE-2026-5497
- https://bugzilla.redhat.com/show_bug.cgi?id=2487813
- https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-5497.json