CVE-2026-47477 in Triton Inference Server
Summary
by MITRE • 07/14/2026
NVIDIA Triton Inference Server for Linux contains a vulnerability where an attacker can cause a stack-based buffer overflow. A successful exploit of this vulnerability might lead to denial of service.
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Analysis
by VulDB Data Team • 07/15/2026
The NVIDIA Triton Inference Server represents a critical component in machine learning deployment environments, serving as a unified inference serving solution that enables organizations to deploy and manage AI models across diverse hardware platforms. This server software operates as a middleware layer between ML applications and model execution engines, processing inference requests from clients and orchestrating model execution through various backends including tensorflow, pytorch, and onnx runtime. The vulnerability under consideration manifests within the server's handling of incoming inference requests, specifically when processing certain input parameters or configuration data that triggers improper memory management during request parsing.
The technical flaw constitutes a stack-based buffer overflow occurring when the Triton Inference Server processes malformed or excessively large input data structures within its request handling pipeline. This vulnerability arises from insufficient bounds checking on user-supplied data that flows into stack-allocated buffers during the parsing of model configuration files, inference request parameters, or metadata fields. When an attacker crafts malicious input that exceeds the allocated buffer size, the overflow corrupts adjacent stack memory locations including return addresses and local variables, potentially allowing arbitrary code execution or process termination. The vulnerability aligns with CWE-121 Stack-based Buffer Overflow, which specifically addresses buffer overflows occurring in stack-allocated memory regions where insufficient boundary checks permit data to overwrite adjacent memory.
The operational impact of this vulnerability extends beyond simple denial of service, though that represents the primary exploitation vector. While a successful exploit might initially result in process crashes or service unavailability, the underlying memory corruption could potentially enable more sophisticated attacks depending on the execution environment and security mitigations in place. The server's role as a central inference point makes it an attractive target for attackers seeking to disrupt machine learning workflows, particularly in production environments where continuous availability is critical for business operations. Organizations utilizing Triton Server for model serving in cloud deployments, edge computing scenarios, or enterprise AI pipelines face significant risk if this vulnerability remains unpatched.
Mitigation strategies should prioritize immediate patching of affected versions through NVIDIA's official security advisories and software updates. Network segmentation and access controls can help limit exposure by restricting direct client access to the inference server and implementing authentication mechanisms for request processing. Input validation and sanitization should be enhanced at multiple layers including client-side preprocessing, API gateway filtering, and server-side parameter verification to prevent malformed data from reaching vulnerable code paths. Additionally, implementing runtime protections such as stack canaries, address space layout randomization, and control flow integrity checks can provide defense-in-depth measures against exploitation attempts. Organizations should monitor for potential exploitation indicators through log analysis and implement intrusion detection systems that can identify anomalous request patterns consistent with buffer overflow attack vectors. The vulnerability demonstrates the importance of secure coding practices in high-value software components and underscores the need for comprehensive security testing including fuzzing and memory safety verification of critical infrastructure software.
Reference to ATT&CK technique T1499.004 for Network Denial of Service provides context for how this vulnerability could be leveraged in broader attack campaigns, while CWE-121 classification confirms the fundamental nature of the memory corruption flaw that requires careful attention during both development and operational phases of software lifecycle management.