CVE-2026-112269 in SGLang
Summary
by MITRE • 10/19/2026
SGLang through 0.5.22 with the Mooncake disaggregation backend accepts unauthenticated decode registration messages on the Prefill rank ZMQ PULL socket, allowing denial of service. Network-adjacent attackers can send many registrations with unique mooncake_session_id values and large nested-buffer frames to exhaust Prefill memory until the scheduler is OOM-killed.
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Analysis
by VulDB Data Team • 10/19/2026
The SGLang framework, specifically versions up through 0.5.22 when configured with the Mooncake disaggregation backend, contains a critical vulnerability related to unauthenticated access on its inter-process communication layer. This architectural design separates the prefill and decode stages of large language model inference into distinct ranks that communicate via ZeroMQ sockets. The specific flaw resides in the Prefill rank's ZMQ PULL socket, which is designed to accept registration messages from other components or clients within the distributed system. However, this endpoint lacks any form of authentication or authorization checks, allowing network-adjacent attackers to interact with it directly without valid credentials. This misconfiguration effectively exposes an internal operational interface to external actors who can reach the host over the network, violating the principle of least privilege and proper access control for sensitive infrastructure components.
The technical mechanism of this vulnerability involves the manipulation of message payloads sent to the registration endpoint. Attackers can exploit this by sending a high volume of decode registration messages that contain unique mooncake_session_id values alongside large nested-buffer frames. Each incoming request triggers memory allocation within the Prefill rank to handle the session data and buffer structures. Because there is no limit on the number of concurrent sessions or the size of the buffers accepted, an attacker can systematically exhaust available system resources. By continuously sending these crafted requests, the attacker forces the operating system's scheduler to consume increasing amounts of RAM until it reaches a critical threshold. This resource exhaustion leads directly to the process being terminated by the kernel's Out-Of-Memory killer mechanism, resulting in a complete denial of service for the inference engine and any dependent services relying on that instance.
From an operational perspective, this vulnerability poses a severe risk to the availability of large language model deployments that utilize disaggregated architectures. The impact is not limited to temporary downtime; it can also lead to data loss if active inference tasks are abruptly terminated without proper cleanup or checkpointing mechanisms in place. Furthermore, repeated exploitation could destabilize the broader cluster environment, potentially affecting other nodes sharing storage or network resources. This type of attack aligns with CWE-787, which describes out-of-bounds write vulnerabilities that can lead to memory corruption and system crashes, although in this specific case, it manifests more directly as a resource exhaustion issue akin to CWE-400, where uncontrolled consumption of system resources leads to denial of service. The attack vector is classified under ATT&CK technique T1498, specifically Network Denial of Service via volumetric attacks or resource exhaustion strategies that target application-level services rather than just network bandwidth.
To mitigate this vulnerability, immediate action should be taken by upgrading SGLang to a version later than 0.5.22 where the issue has been addressed in subsequent releases. If an upgrade is not immediately feasible, administrators must implement strict access control lists on the firewall or host-based security groups to restrict network traffic to the ZMQ PULL socket exclusively from trusted internal IP addresses associated with legitimate decode ranks. Additionally, configuring ZeroMQ to enforce message size limits and implementing rate limiting at the application level can help prevent resource exhaustion attacks. It is also recommended to enable authentication mechanisms for inter-process communication where possible, ensuring that only authorized components can register sessions or send control messages to the prefill rank. Regular monitoring of memory usage patterns on inference nodes should be established to detect anomalous spikes in allocation activity indicative of such exploitation attempts.