CVE-2025-48956: vllm · Uncontrolled Resource Consumption
Affects vllmSource CVE Database V5
Not known to be exploited · CVSS 7.5 High · fixed in 0.10.1.1 : patch vllm soon.
Data as of 26 Oct 2026, 07:57 UTC
- EPSS
- 0.5% chance of exploitation in 30 days – higher than 46% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
- Patch
- Available – fixed in 0.10.1.1
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HExploitation evidence
Disclosed: evidence found.
CVE published 21 Aug 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-26.
What to do
AI-suggested
European organizations should immediately verify their vLLM deployment versions and upgrade to v0.10.1.1 or later to remediate this vulnerability. In environments where immediate patching is not feasible, implementing network-level protections is critical. This includes configuring web application firewalls (WAFs) or reverse proxies to limit the size of HTTP headers accepted by the vLLM endpoints, effectively blocking requests with abnormally large headers. Rate limiting and anomaly detection mechanisms should be employed to identify and block suspicious traffic patterns indicative of DoS attempts. Additionally, isolating vLLM services within segmented network zones and restricting access to trusted IP ranges can reduce exposure. Monitoring system memory usage and setting up alerts for unusual resource consumption can provide early warning signs of exploitation attempts. Finally, organizations should review incident response plans to include scenarios involving AI service unavailability.
Description
vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.10.1.1, a Denial of Service (DoS) vulnerability can be triggered by sending a single HTTP GET request with an extremely large header to an HTTP endpoint. This results in server memory exhaustion, potentially leading to a crash or unresponsiveness. The attack does not require authentication, making it exploitable by any remote user. This vulnerability is fixed in 0.10.1.1.