CVE-2026-100651 in vLLM
Summary
by MITRE • 09/26/2026
vLLM before 0.29.0 fails to enforce decoder prompt-length validation on the disaggregated serving endpoint /inference/v1/generate. When the request contains a 'features' (multimodal) payload, vllm/entrypoints/serve/disagg/serving.py builds a multimodal EngineInput directly from the caller-supplied token_ids, and GenerateRequest.token_ids (vllm/entrypoints/serve/disagg/protocol.py) is not checked against model_config.max_model_len. For multimodal processors that report skip_prompt_length_check=True (for example Nemotron Parse, Whisper, and FireRedLID), InputProcessor._validate_prompt_len() returns immediately for both encoder and decoder prompts, so an overlong prompt becomes an EngineCoreRequest and reaches the worker input-batch copy into a fixed max_model_len-wide NumPy row. A client able to reach the endpoint on an affected model configuration can therefore submit an overlong token_ids list to trigger a worker failure and denial of service. Fixed in 0.29.0.
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Analysis
by VulDB Data Team • 09/26/2026
The vulnerability identified in vLLM versions prior to 0.29.0 represents a critical input validation flaw within the disaggregated serving architecture, specifically affecting the /inference/v1/generate endpoint. This security issue stems from an incomplete enforcement of prompt length constraints when handling multimodal payloads that include features such as images or audio data. In standard operation, vLLM is designed to validate incoming requests against a configured maximum model length to prevent buffer overflows and resource exhaustion. However, in the disaggregated serving mode, the code path responsible for constructing EngineInput objects bypasses these critical safety checks under specific conditions involving multimodal processors that signal they do not require prompt length validation.
The technical root cause lies in how the system processes token identifiers supplied by the client. When a request contains a features payload, the serving module directly constructs an EngineInput using caller-supplied token_ids without verifying them against the model_config.max_model_len parameter defined for the specific model being served. This oversight is exacerbated by the behavior of certain multimodal input processors, such as those used for Nemotron Parse, Whisper, and FireRedLID models. These processors report a skip_prompt_length_check flag set to true, which causes the InputProcessor._validate_prompt_len() method to return immediately without performing any length verification on either encoder or decoder prompts. Consequently, excessively long token sequences are not rejected at the entry point but are instead passed through as EngineCoreRequest objects destined for the worker nodes.
The operational impact of this flaw is severe, primarily manifesting as a denial of service condition. When an overlong prompt reaches the worker node, it triggers a memory copy operation into a fixed-size NumPy array allocated with dimensions based on max_model_len. Because the incoming token sequence exceeds these pre-allocated boundaries, the system encounters a buffer overflow or index out-of-bounds error during the batch processing phase. This results in immediate worker failure and process termination, effectively disrupting service availability for all users connected to that specific endpoint. An attacker capable of sending HTTP requests to this API can exploit this behavior by submitting crafted multimodal payloads with artificially inflated token lists, thereby causing repeated crashes and rendering the inference service unstable or completely unavailable.
This vulnerability aligns with CWE-20 Improper Input Validation, as the application fails to verify that user-supplied input conforms to expected constraints before processing it. Furthermore, from a threat modeling perspective consistent with MITRE ATT&CK techniques, this flaw facilitates Denial of Service (T1499) by exploiting resource exhaustion mechanisms within the inference engine. The lack of boundary checks on critical parameters like sequence length allows for efficient exploitation without requiring complex bypasses or authentication privileges beyond basic API access.
To mitigate this risk, organizations running vLLM in disaggregated serving modes must upgrade to version 0.29.0 or later, where these validation gaps have been addressed. In the interim, if upgrading is not immediately feasible, administrators should implement strict rate limiting and input size restrictions at the network perimeter using a Web Application Firewall or reverse proxy configuration. It is also advisable to disable multimodal features on endpoints that do not strictly require them, thereby reducing the attack surface associated with complex processor logic paths. Regular auditing of model configurations ensures that max_model_len settings are appropriately calibrated to prevent accidental overflows even if future code changes introduce similar validation gaps.