CVE-2026-116177 in vLLM
Summary
by MITRE • 10/25/2026
vLLM from 0.10.2 before 0.25.0 contains an uncontrolled resource consumption vulnerability that allows unauthenticated attackers to exhaust memory because the Nemotron-VL processors globally set Image.MAX_IMAGE_PIXELS to None. Attackers can submit a pathological decompression-bomb image to /v1/chat/completions to OOM-kill the worker and deny service to co-tenants.
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Analysis
by VulDB Data Team • 10/25/2026
The vulnerability identified in vLLM versions prior to 0.25.0 represents a critical uncontrolled resource consumption flaw within the Nemotron-VL processor component. This issue stems from an insecure default configuration where the Image.MAX_IMAGE_PIXELS parameter is explicitly set to None, effectively disabling any upper limit on image dimensions or file sizes processed by the system. In standard software development practices, limiting input size is a fundamental defense-in-depth strategy against resource exhaustion attacks. By removing this constraint, the application becomes susceptible to attackers submitting pathological inputs designed to consume excessive computational resources and memory during processing.
The technical mechanism of exploitation involves an unauthenticated attacker interacting with the /v1/chat/completions API endpoint. The attacker crafts or selects a decompression-bomb image, also known as a zip bomb or inflate bomb, which is small in its compressed form but expands to consume gigabytes of RAM when decoded by the underlying imaging libraries. Because vLLM processes these images without validating their potential memory footprint prior to full decomposition, the system allocates massive amounts of heap space to handle the inflated data structure. This behavior directly violates the principle of least privilege and secure defaults, as the application assumes all incoming media is benign and within reasonable bounds.
The operational impact of this vulnerability is severe service degradation or complete denial of service for multi-tenant environments. When a worker process attempts to decompress such an image, it rapidly exhausts available memory resources on the host machine. This typically results in the operating system invoking its Out-of-Memory killer mechanism to terminate the offending vLLM worker process. In shared hosting or cloud-based inference scenarios where multiple tenants rely on the same cluster for AI model serving, the termination of one worker can lead to cascading failures, increased latency for remaining requests, and potential data loss if stateful operations were in progress. This effectively allows a single unauthenticated user to disrupt service availability for all other users sharing the infrastructure.
This vulnerability aligns with CWE-400 Uncontrolled Resource Consumption, specifically falling under the sub-category of resource exhaustion via decompression bombs or excessive input size. From an offensive security perspective, this attack vector is consistent with ATT&CK technique T1496 Resource Hijacking, where adversaries leverage system resources to cause denial of service rather than stealing data directly. The lack of authentication required for exploitation further exacerbates the risk profile, as any external actor on the network can trigger the condition without needing valid credentials or prior interaction history.
To mitigate this vulnerability, immediate patching is recommended by upgrading vLLM to version 0.25.0 or later, where the default configuration has been corrected to enforce reasonable limits on image processing resources. For organizations unable to upgrade immediately due to compatibility constraints, a temporary mitigation involves implementing network-level controls such as web application firewalls that inspect and limit the size of POST requests containing multipart form data with images. Additionally, deploying reverse proxies or API gateways can help validate input sizes before they reach the vLLM worker processes. It is also advisable to configure container resource limits using Kubernetes or Docker constraints to cap the maximum memory usage per pod, ensuring that even if an attack succeeds, it does not compromise the stability of the entire host node or affect other critical services running on the same infrastructure.