CVE-2026-24272 in TensorRT
Summary
by MITRE • 07/15/2026
NVIDIA TensorRT contains a vulnerability where an attacker might cause an overflow to a heap-based buffer. A successful exploit of this vulnerability might lead to code execution.
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Analysis
by VulDB Data Team • 07/15/2026
This vulnerability resides within NVIDIA TensorRT, a high-performance deep learning inference optimizer and runtime that is widely deployed in production environments for accelerating neural network inference workloads. The specific flaw manifests as a heap-based buffer overflow condition that occurs when processing certain input data structures within the tensorrt runtime components. This type of vulnerability represents a critical security weakness that can be exploited by malicious actors to gain unauthorized control over affected systems. The buffer overflow arises from insufficient bounds checking during memory allocation and data handling operations, allowing an attacker to write beyond allocated memory boundaries in the heap region.
The technical exploitation of this vulnerability follows a predictable pattern where crafted input data triggers improper memory management within TensorRT's processing pipeline. When the system attempts to handle oversized or malformed tensor data structures, the insufficient validation mechanisms fail to prevent excessive memory writes that overwrite adjacent heap memory regions. This overflow condition creates opportunities for attackers to manipulate program execution flow by overwriting critical memory locations such as return addresses, function pointers, or other control data structures. The vulnerability aligns with CWE-121, which describes heap-based buffer overflow conditions, and represents a classic example of improper input validation leading to memory corruption. Attackers can leverage this weakness to execute arbitrary code within the context of the TensorRT process, potentially gaining full system compromise when the runtime operates with elevated privileges.
The operational impact of this vulnerability extends across numerous industries that rely on NVIDIA's inference acceleration technology, including cloud computing platforms, edge AI deployments, autonomous vehicles, and enterprise machine learning workloads. Systems running affected TensorRT versions become susceptible to remote code execution attacks, particularly in environments where untrusted input data is processed through neural network inference pipelines. The attack surface is broad as TensorRT is integrated into various applications such as web services, mobile apps, and backend processing systems that utilize deep learning models for image recognition, natural language processing, or predictive analytics. Organizations deploying these inference engines face significant risk exposure since the vulnerability can be exploited through network-based attacks without requiring local system access, making it particularly dangerous in multi-tenant cloud environments where isolation between different workloads is crucial.
Mitigation strategies should prioritize immediate patch deployment from NVIDIA to address the buffer overflow condition within TensorRT components. System administrators must implement comprehensive input validation and sanitization procedures for all data entering TensorRT processing pipelines to prevent malformed inputs from triggering memory corruption conditions. Network segmentation and access controls should be enforced to limit exposure of TensorRT services to untrusted networks and users, while runtime monitoring systems can detect anomalous memory usage patterns that may indicate exploitation attempts. The implementation of address space layout randomization ASLR and stack canaries provides additional defense-in-depth measures that complicate exploitation attempts by making it more difficult for attackers to predict memory layouts and execute controlled code injection attacks. Organizations should also consider applying principle of least privilege configurations where TensorRT processes operate with minimal required permissions, reducing potential impact if exploitation occurs. This vulnerability demonstrates the critical importance of maintaining up-to-date security patches in machine learning infrastructure components that handle sensitive data processing workloads across enterprise environments.