CVE intelligence and bounded remediation
CVE-2025-48956 — vLLM is an inference and serving engine for large language models (LLMs)
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.
- Severity
- High
- CVSS
- 7.5 (3.1)
- Published
- 2025-08-21
- CISA KEV
- Not currently listed
- Ecosystem
- software/application
- Weaknesses
- CWE-400
Affected products
- vllm / vllm
Matched remediation archetype
Resource exhaustion and denial of service
This catalog composition supplies bounded fallback guidance. Explicitly reviewed curated workflows load with the complete record below.
Check exposure
- Identify attacker-influenced work factors including input size, nesting, compression, fan-out, regex cost, allocation, recursion, retries, and connection lifetime.
- Map per-request and shared CPU, memory, disk, descriptor, thread, queue, and downstream-service limits.
- Determine whether authentication, tenancy, quotas, and rate controls apply before expensive processing begins.
Remediate safely
- Bound input size, nesting, expansion, work, concurrency, queue depth, retries, and execution time before resource-intensive processing.
- Release resources on every success, error, cancellation, and timeout path and use backpressure instead of unbounded buffering.
- Update affected components and add small deterministic tests that assert resource ceilings rather than exhausting a host.
Authoritative sources
Complete CVE record and remediation plan
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