CVE-2026-105759
vLLM is an inference and serving engine for large language models
- Risk score
- 61/100
- CVSS
- 7.5 v3.1
- Exploit chance (30 days)
- 0.4%
- Published
- 5 Oct 2026
Open in the live forecast Today's forecast
What to know
- What it is
- vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the Rust frontend's track_http_metrics middleware records the raw HTTP method token as a Prometheus label for requests reaching registered routes. An unauthenticated attacker can send unique arbitrary method tokens to unguarded routes …
- Affected
- Vllm before 0.30.0
- Fix
- Upgrade Vllm to 0.30.0 or later.
- Exploitation
- Public proof of concept
Story map
At a glance
Vllm: Amber, patch this week. vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the Rust frontend's track_http_metrics middleware records the raw HTTP method token as a Prometheus label for requests reaching registered routes. An unauthenticated attacker can send unique arbitrary method tokens to unguarded routes …
The flaws
- CVE-2026-105759: VLLM is an inference and serving engine for large language models (CVSS 7.5 · EPSS 0%)
- CVE-2026-100652: Improper input validation (CVSS 7.5 · EPSS 0%)
- CVE-2026-103241: Denial of service (CVSS 7.5 · EPSS 1%)
- CVE-2026-100647: Improper input validation (CVSS 7.5 · EPSS 0%)
The fix
Upgrade Vllm: 0.30.0 · 0.29.0 or later on your release line
Who is exposed: Vllm before 0.30.0
Where it stands
- Public proof of concept
Connects to
- Same weakness: Denial of service (CWE-400). 253 other warnings in 30 days
- Same product: Earlier Vllm warnings. 15 in the last 90 days
Why it is Amber
- CVSS 7.5 high
- Public exploit or proof of concept referenced
- Remotely exploitable without logging in
References
- https://github.com/vllm-project/vllm/commit/3735c2d5f5248259482b9045c34fb7a8a3892352
- https://github.com/vllm-project/vllm/pull/56058
- https://github.com/vllm-project/vllm/releases/tag/v0.30.0
- https://github.com/vllm-project/vllm/security/advisories/GHSA-5fj9-pfhr-6j48
- https://github.com/vllm-project/vllm/security/advisories/GHSA-5fj9-pfhr-6j48
Original description
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the Rust frontend's track_http_metrics middleware records the raw HTTP method token as a Prometheus label for requests reaching registered routes. An unauthenticated attacker can send unique arbitrary method tokens to unguarded routes such as /tokenize, causing Prometheus's Family::get_or_create function to permanently create counter and histogram label sets. Those label sets increase process memory usage and enlarge the /metrics response until the service or monitoring path is exhausted. This issue is fixed in version 0.30.0.
Last activity 5 Oct 2026. Patch Forecast updates every hour.