CVE-2026-47473 in TensorRT-LLM
Summary
by MITRE • 07/15/2026
NVIDIA TensorRT-LLM contains a vulnerability where an attacker could cause a write-what-where condition. A successful exploit of this vulnerability might lead to data tampering, denial of service, and information disclosure.
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Analysis
by VulDB Data Team • 07/15/2026
The vulnerability in NVIDIA TensorRT-LLM represents a critical write-what-where condition that fundamentally compromises the integrity and security of machine learning inference workloads. This flaw exists within the deep learning framework's memory management systems where improper input validation allows attackers to manipulate memory addresses and write arbitrary data to specified locations. The vulnerability stems from insufficient bounds checking and pointer validation mechanisms during tensor operations and memory allocation processes, creating opportunities for malicious actors to redirect program execution flow and corrupt system memory structures.
From a technical perspective, the write-what-where condition manifests when attacker-controlled inputs bypass proper validation checks in the TensorRT-LLM runtime environment. This allows unauthorized memory writes at arbitrary addresses, potentially overwriting critical data structures, function pointers, or code segments within the application process. The vulnerability aligns with CWE-787 (Out-of-bounds Write) and CWE-121 (Stack-based Buffer Overflow) categories, representing a fundamental breakdown in memory safety controls that enables privilege escalation and arbitrary code execution capabilities.
The operational impact of this vulnerability extends beyond simple denial of service scenarios to encompass serious data integrity threats and potential information disclosure. Attackers could manipulate model weights, inference results, or system configurations through controlled memory corruption, leading to incorrect computational outputs that undermine the reliability of machine learning applications. Additionally, the vulnerability may enable attackers to extract sensitive information from memory regions, including cryptographic keys, model parameters, or proprietary training data that could be valuable for competitive advantage or further exploitation attempts.
Security professionals should implement multiple layers of defense to mitigate this vulnerability across deployed TensorRT-LLM environments. Immediate remediation efforts must focus on applying official NVIDIA security patches and updates to eliminate the underlying memory corruption conditions. Organizations should also deploy runtime monitoring solutions capable of detecting anomalous memory access patterns and unauthorized write operations that could indicate exploitation attempts. Network segmentation and process isolation techniques can limit the potential impact of successful attacks by containing compromised processes within restricted environments. Furthermore, regular vulnerability assessments and security audits of machine learning infrastructure should be conducted to identify similar memory safety issues in other components of the deep learning stack.
The ATT&CK framework categorizes this vulnerability under T1059 (Command and Scripting Interpreter) and T1068 (Exploitation for Privilege Escalation) tactics, as exploitation would likely involve crafting malicious inputs that trigger the write-what-where condition. The vulnerability also maps to T1499 (Endpoint Termination) through potential denial of service impacts, and T1566 (Phishing with Social Engineering) if attackers leverage this weakness in conjunction with initial access vectors. Organizations implementing AI-powered security solutions using TensorRT-LLM must recognize that these vulnerabilities directly impact the trustworthiness of their machine learning pipelines and require comprehensive security measures to maintain operational integrity.