CVE-2026-24226 in TensorRT-LLM
Summary
by MITRE • 07/15/2026
NVIDIA TensorRT-LLM for Linux contains a vulnerability where an attacker could cause improper control of code generation. A successful exploit of this vulnerability might lead to code execution, data tampering, and information disclosure.
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Analysis
by VulDB Data Team • 07/15/2026
This vulnerability in NVIDIA TensorRT-LLM for Linux represents a critical security flaw that undermines the integrity of machine learning inference workflows. The issue stems from inadequate controls during code generation processes within the tensorrt-llm framework, which is designed to optimize deep learning models for deployment on NVIDIA hardware platforms. When exploited, this weakness allows malicious actors to manipulate the code generation phase, potentially introducing arbitrary code execution capabilities that bypass normal security boundaries.
The technical implementation of this vulnerability involves insufficient validation mechanisms during the compilation and optimization stages of neural network models. Attackers can craft specifically designed inputs or model configurations that trigger unexpected behavior in the tensorrt-llm compiler, leading to improper code generation sequences. This flaw operates at the intersection of software compilation and machine learning optimization, where standard security controls may not adequately address the unique attack surface presented by AI inference frameworks. The vulnerability aligns with CWE-787 (Out-of-bounds Write) and CWE-121 (Stack-based Buffer Overflow) classifications, as it involves improper handling of memory allocation during code generation processes.
The operational impact of this vulnerability extends beyond simple privilege escalation to encompass complete system compromise within environments utilizing NVIDIA TensorRT-LLM. Organizations deploying machine learning workloads on Linux systems may face unauthorized access to sensitive data, manipulation of inference results, and potential lateral movement within their infrastructure. The attack surface is particularly concerning in enterprise environments where AI models process confidential information such as personal data, financial records, or proprietary business intelligence. This vulnerability can be leveraged by attackers to gain persistent access to systems, potentially enabling long-term surveillance or data exfiltration operations that align with ATT&CK technique T1059.001 (Command and Scripting Interpreter) and T1566.001 (Phishing: Spearphishing Attachment).
Mitigation strategies for this vulnerability require immediate patching of affected NVIDIA TensorRT-LLM installations and implementation of additional runtime safeguards. Organizations should establish strict input validation procedures for all model parameters and ensure that only trusted sources can submit configurations to the tensorrt-llm compiler. Network segmentation and privilege separation practices can limit the potential damage from successful exploitation attempts, while monitoring systems should be deployed to detect anomalous code generation patterns or unexpected memory usage spikes. Regular security assessments of AI infrastructure components are essential to identify similar vulnerabilities in other machine learning frameworks that may present comparable attack surfaces. The remediation process must also include comprehensive testing procedures to verify that patches do not introduce regressions in model performance or compatibility with existing deployments.