CVE-2026-92971 in LMDeploy
Summary
by MITRE • 09/17/2026
InternLM LMDeploy through 0.17.0 contains a reachable assertion vulnerability in the DistServe decode migration loop that allows unauthenticated attackers to terminate the inference engine. Attackers can submit a migration_request with an empty remote_block_ids list to trigger an AssertionError that crashes the engine loop and causes subsequent inference requests to fail.
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Analysis
by VulDB Data Team • 09/17/2026
The InternLM LMDeploy framework, specifically versions up through 0.17.0, contains a critical reachable assertion vulnerability located within the DistServe decode migration loop. This component is responsible for managing the distribution of computational tasks across multiple nodes in a distributed inference environment. The flaw arises from insufficient input validation when processing migration requests that involve block ID lists. Specifically, the code assumes that any incoming migration request will contain at least one valid remote block identifier to facilitate state transfer between worker processes. When this assumption is violated by an attacker submitting a malformed request with an empty list of remote_block_ids, the internal assertion logic fails and triggers an AssertionError exception. This type of vulnerability falls under CWE-617, which describes Reachable Assertion in Remote Code Execution contexts, indicating that a security-critical check can be triggered from outside the trusted boundary without proper authentication or input sanitization mechanisms being enforced prior to execution.
The operational impact of this flaw is severe for any deployment relying on LMDeploy for serving large language models via distributed inference architectures. Because the assertion error occurs within the main decode migration loop, it causes an unhandled exception that terminates the entire inference engine process rather than gracefully handling the malformed input or returning a standard HTTP error code. This results in a complete denial of service condition where all subsequent inference requests fail until the service is manually restarted by an administrator. The vulnerability allows any network-accessible client to trigger this crash, meaning no authentication credentials are required to exploit it. In production environments where high availability and continuous model serving are critical, such as API endpoints for chatbots or automated content generation systems, this single malformed packet can disrupt service continuity indefinitely if the process is not configured with automatic restart mechanisms that mask the underlying instability.
From a threat modeling perspective aligned with MITRE ATT&CK techniques, this vulnerability facilitates Denial of Service (T1499) and potentially impacts Availability by disrupting critical infrastructure services. The attack vector involves sending a specifically crafted HTTP request to the DistServe endpoint, exploiting the lack of schema validation on the migration_request payload structure. Attackers can automate this process using simple scripting tools to send repeated empty block ID lists, ensuring persistent disruption even if the service attempts automatic recovery loops that may themselves be susceptible to race conditions or resource exhaustion during restart cycles. The simplicity of the exploit makes it particularly dangerous as it requires no specialized knowledge beyond understanding basic API interaction patterns and JSON payload structures.
Mitigation strategies must focus on both immediate patching and architectural hardening. The primary remediation is to upgrade LMDeploy to a version later than 0.17.0 where this assertion has been replaced with proper error handling logic that validates the non-emptiness of remote_block_ids before proceeding with migration operations. If upgrading is not immediately feasible, organizations should implement network-level controls such as Web Application Firewalls (WAF) or API gateways to inspect incoming requests and reject payloads containing empty arrays for block IDs. Additionally, deploying health checks with automatic restart policies can mitigate the impact by minimizing downtime, though this does not address the root cause. Developers integrating LMDeploy should also enforce strict input validation at the application layer, ensuring that all distributed communication protocols adhere to defined schemas before processing migration logic. Regular security audits of inference engine configurations and dependency updates are essential to maintain resilience against such assertion-based denial-of-service attacks in modern AI infrastructure deployments.