CVE-2026-100652 in vLLM
Summary
by MITRE • 09/26/2026
vLLM versions 0.22.0 through 0.23.0 fail to validate stop_token_ids against vocabulary bounds in Rust HTTP and gRPC frontends, allowing out-of-vocabulary token IDs to reach MinTokensLogitsProcessor. Attackers can submit requests with min_tokens greater than zero and out-of-vocabulary stop_token_ids to trigger CUDA tensor indexing failures that leave EngineCore in a fatal state requiring service restart.
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Analysis
by VulDB Data Team • 09/26/2026
The vulnerability identified in vLLM versions 0.22.0 through 0.23.0 represents a critical input validation failure within the Rust-based HTTP and gRPC frontends of the inference engine. The core technical flaw lies in the insufficient boundary checking performed on stop_token_ids before they are passed downstream to the MinTokensLogitsProcessor. In normal operation, token identifiers must correspond strictly to indices within the model's vocabulary size to ensure safe memory access during tensor operations. However, due to this missing validation layer, malicious actors can craft requests containing out-of-vocabulary token IDs for the stop_token_ids parameter while simultaneously setting min_tokens greater than zero. This specific combination bypasses standard safety checks and allows invalid integer values to propagate into the core inference logic where they are used as indices for CUDA tensor operations.
When these invalid token identifiers reach the MinTokensLogitsProcessor, they trigger a catastrophic failure in the underlying CUDA execution environment. The processor attempts to index into tensors using the out-of-bounds stop_token_ids, which results in illegal memory access errors on the GPU. This type of error is particularly severe because it does not merely return an HTTP or gRPC error code; instead, it corrupts the state of the EngineCore instance running on the accelerator hardware. The CUDA runtime detects the invalid memory operation and forces a fatal exception that cannot be gracefully recovered from within the application logic. Consequently, the entire inference service becomes unresponsive until the process is manually restarted by an administrator or automated orchestration system.
From an operational impact perspective, this vulnerability poses a significant risk to availability and reliability in production environments hosting large language models. An attacker does not require authentication to exploit this flaw if the API endpoints are publicly accessible or exposed internally without strict input sanitization policies. By sending carefully crafted requests with out-of-vocabulary stop_token_ids, an adversary can induce denial of service conditions by repeatedly crashing the inference engine. This leads to service interruptions for legitimate users and increases operational overhead due to the need for frequent restarts and potential data loss if in-flight completions are not persisted. The impact is compounded in high-throughput scenarios where rapid successive requests could keep the system in a perpetual state of failure, effectively rendering the model deployment unusable until manual intervention occurs.
This vulnerability aligns with CWE-20 Improper Input Validation as it stems from the acceptance and processing of externally supplied input without adequate verification that it meets specified requirements. Specifically, it relates to CWE-125 Out-of-bounds Read because the invalid token ID causes an attempt to read or index memory outside the allocated bounds of the CUDA tensor structures. In terms of offensive security frameworks, this exploit maps to MITRE ATT&CK technique T1496 Resource Hijacking under the sub-category of Denial of Service via resource exhaustion or system instability. The attack vector is classified as Network-Accessible since it can be triggered remotely over HTTP or gRPC protocols without prior authentication in many default configurations.
To mitigate this vulnerability, immediate updates to vLLM versions 0.23.1 and later are recommended, where the input validation logic has been strengthened to enforce strict bounds checking on all token identifiers before they enter the logits processing pipeline. For environments that cannot immediately upgrade, defensive measures should include implementing robust API gateway rules or reverse proxy configurations that validate request payloads against expected vocabulary sizes prior to forwarding them to the vLLM service. Additionally, deploying health checks and automated restart policies can help minimize downtime by quickly recovering from crashes caused by such malformed inputs. It is also advisable to restrict access to inference endpoints using authentication mechanisms where possible, reducing the attack surface available to potential adversaries seeking to disrupt services through resource exhaustion techniques.