CVE-2026-24234 in TensorRT-LLM
Summary
by MITRE • 07/15/2026
NVIDIA TensorRT-LLM for Linux contains a vulnerability in the multimodal media fetching functions, where a network-accessible attacker could cause server-side request forgery. A successful exploit of this vulnerability might lead to denial of service and information disclosure.
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Analysis
by VulDB Data Team • 07/15/2026
The vulnerability identified in NVIDIA TensorRT-LLM for Linux represents a critical security flaw within the multimodal media fetching functionality that enables remote attackers to execute server-side request forgery attacks. This issue stems from inadequate validation mechanisms in the media retrieval processes that handle external resource access, creating an attack vector where malicious actors can manipulate the system's behavior by crafting specially crafted requests. The vulnerability exists at the intersection of web application security and artificial intelligence infrastructure, specifically affecting how TensorRT-LLM processes multimedia content when integrated into Linux environments. The flaw demonstrates a classic server-side request forgery pattern where the application fails to properly validate or sanitize external URLs or resources that it attempts to fetch on behalf of users or system components.
The technical implementation of this vulnerability allows attackers to leverage the legitimate media fetching capabilities of TensorRT-LLM to make unauthorized requests to internal or external systems that should normally be inaccessible to the application. This occurs because the software does not adequately restrict or validate the destinations to which it attempts to connect when retrieving multimedia content, potentially enabling attackers to access internal network resources, bypass firewall restrictions, or perform reconnaissance activities. The flaw can be exploited through various means including direct manipulation of input parameters that control media source URLs, or by leveraging other vulnerabilities within the application stack that could facilitate more complex attack chains. The lack of proper access controls and validation mechanisms in the multimedia processing pipeline creates an environment where unauthorized resource access becomes possible.
The operational impact of this vulnerability extends beyond simple denial of service conditions to include potential information disclosure risks that could compromise sensitive system data or intellectual property. When exploited successfully, the server-side request forgery could result in unauthorized access to internal systems, exposure of configuration files, credentials, or other sensitive information stored on network resources that are typically protected from external access. Organizations using NVIDIA TensorRT-LLM for Linux may experience service disruption as attackers leverage this vulnerability to consume system resources or redirect requests to malicious endpoints, potentially leading to cascading failures within AI processing workflows. The vulnerability affects the integrity and confidentiality of data flowing through the multimedia processing pipeline, particularly in environments where TensorRT-LLM handles sensitive content or operates in security-sensitive contexts such as healthcare, financial services, or government applications.
Mitigation strategies for this vulnerability should focus on implementing robust input validation and access control mechanisms within the media fetching functions of NVIDIA TensorRT-LLM. System administrators should ensure that all external resource URLs are properly validated against a trusted whitelist of acceptable domains, and that appropriate network segmentation prevents unauthorized internal system access. The implementation of proper URL sanitization and protocol validation can prevent attackers from crafting malicious requests that exploit the server-side request forgery mechanism. Organizations should also consider implementing network monitoring and intrusion detection systems to identify anomalous patterns of resource access that may indicate exploitation attempts. Additionally, regular security updates and patches from NVIDIA should be applied promptly to address known vulnerabilities in the TensorRT-LLM components, while maintaining proper configuration management practices that limit unnecessary network access permissions for multimedia processing functions. This vulnerability aligns with CWE-918 Server-Side Request Forgery and follows ATT&CK technique T1071.004 Application Layer Protocol: DNS, where attackers leverage application functionality to perform unauthorized network requests.
The security implications of this flaw demonstrate the importance of considering web application security principles when developing and deploying AI infrastructure components. The vulnerability highlights how seemingly benign functionality like multimedia content retrieval can become a critical attack surface when proper security controls are not implemented. Organizations should conduct comprehensive security assessments of their AI processing pipelines to identify similar vulnerabilities that may exist in other components or third-party libraries used within their machine learning environments, particularly focusing on network access patterns and external resource handling capabilities. This vulnerability serves as a reminder that even specialized AI frameworks require the same fundamental security considerations as traditional web applications, emphasizing the need for security-by-design principles throughout the software development lifecycle of artificial intelligence systems.