CVE-2025-48956: Vllm Vllm DoS Vulnerability

CVE-2025-48956 Overview

CVE-2025-48956 is a Denial of Service (DoS) vulnerability affecting vLLM, a popular inference and serving engine for large language models (LLMs). The vulnerability exists in versions 0.1.0 through 0.10.1.1 and can be exploited by sending a single HTTP GET request with an extremely large header to an HTTP endpoint. This results in server memory exhaustion, potentially causing the vLLM server to crash or become unresponsive. Critically, the attack does not require authentication, making it exploitable by any remote attacker with network access to the vulnerable endpoint.

Critical Impact

Remote unauthenticated attackers can crash vLLM inference servers with a single malicious request, disrupting AI/ML inference workloads and potentially causing service outages for dependent applications.

Affected Products

• vLLM versions 0.1.0 through 0.10.1.0
• All vLLM deployments with HTTP endpoints exposed to untrusted networks
• AI/ML inference infrastructure utilizing vulnerable vLLM versions

Discovery Timeline

• 2025-08-21 - CVE-2025-48956 published to NVD
• 2025-10-09 - Last updated in NVD database

Technical Details for CVE-2025-48956

Vulnerability Analysis

This vulnerability is classified as CWE-400 (Uncontrolled Resource Consumption). The vLLM HTTP server fails to properly validate or limit the size of incoming HTTP headers before processing them. When a request containing an extremely large header is received, the server attempts to allocate memory to store the header content, leading to memory exhaustion.

The vulnerability is particularly dangerous because it can be triggered with a single HTTP GET request, requiring minimal effort from an attacker. Since vLLM is commonly deployed as part of AI inference pipelines serving large language models, a successful attack can disrupt critical AI/ML services and affect downstream applications that depend on model inference capabilities.

Root Cause

The root cause lies in insufficient input validation of HTTP header sizes in vLLM's HTTP request handling logic. The server does not enforce appropriate limits on header length, allowing attackers to submit arbitrarily large headers that consume excessive memory during parsing and storage. This lack of resource constraints enables trivial memory exhaustion attacks.

Attack Vector

The attack can be executed remotely over the network without authentication. An attacker simply needs to craft an HTTP GET request with an oversized header and send it to any exposed vLLM HTTP endpoint. The network-based attack vector with no authentication requirement makes this vulnerability highly accessible to potential attackers.

The attack flow typically involves:

1. Identifying a publicly accessible vLLM HTTP endpoint
2. Crafting an HTTP request with an extremely large header value (e.g., multiple megabytes or gigabytes)
3. Sending the request to the target server
4. The server attempts to process the large header, exhausting available memory
5. The vLLM service crashes or becomes unresponsive, denying service to legitimate users

Detection Methods for CVE-2025-48956

Indicators of Compromise

• Sudden memory usage spikes on vLLM server processes
• Unexpected vLLM service crashes or restarts without clear cause
• HTTP requests with abnormally large header sizes in web server or proxy logs
• Connection timeouts or service unavailability reported by monitoring systems

Detection Strategies

• Monitor vLLM process memory consumption for sudden, unexplained increases
• Implement web application firewall (WAF) rules to detect and block HTTP requests with oversized headers
• Configure alerting on vLLM process crashes or unexpected restarts
• Analyze HTTP access logs for requests with header sizes exceeding normal operational thresholds

Monitoring Recommendations

• Set up memory usage alerts for vLLM containers or processes with appropriate thresholds
• Enable detailed HTTP logging at load balancer or reverse proxy level to capture header size metrics
• Implement service availability monitoring with rapid notification for vLLM endpoint downtime
• Deploy network-level monitoring to identify unusual traffic patterns targeting vLLM services

How to Mitigate CVE-2025-48956

Immediate Actions Required

• Upgrade vLLM to version 0.10.1.1 or later immediately
• Restrict network access to vLLM HTTP endpoints to trusted sources only
• Deploy a reverse proxy or load balancer with HTTP header size limits in front of vLLM services
• Implement rate limiting on incoming requests to vLLM endpoints

Patch Information

The vulnerability has been fixed in vLLM version 0.10.1.1. The fix is available through the official vLLM repository. Organizations should upgrade to the patched version as soon as possible. For detailed patch information, refer to:

• GitHub Commit d8b736f
• GitHub Pull Request #23267
• GitHub Security Advisory GHSA-rxc4-3w6r-4v47

Workarounds

• Configure upstream reverse proxy (nginx, HAProxy, etc.) to enforce maximum HTTP header size limits
• Implement network segmentation to prevent untrusted access to vLLM endpoints
• Deploy Web Application Firewall (WAF) rules to block requests with abnormally large headers
• Use container resource limits (cgroups) to prevent runaway memory consumption from crashing the entire host

# Example nginx configuration to limit header sizes
# Add to nginx.conf or server block
large_client_header_buffers 4 8k;
client_header_buffer_size 1k;