CVE-2026-24259: NVIDIA TensorRT-LLM Auth Bypass Flaw

CVE-2026-24259 Overview

CVE-2026-24259 affects NVIDIA TensorRT-LLM for Linux, a large language model inference optimization framework. The vulnerability stems from missing authentication for a critical function [CWE-306]. A local attacker with high privileges can exploit this weakness to trigger code execution, tamper with data, or disclose sensitive information. The issue carries a CVSS 3.1 base score of 6.4 and requires local access with high attack complexity. No public proof-of-concept exploit is available, and the vulnerability is not listed in the CISA Known Exploited Vulnerabilities catalog.

Critical Impact
Successful exploitation of CVE-2026-24259 can result in arbitrary code execution, unauthorized modification of inference workloads, and exposure of model data or system information.

Affected Products

NVIDIA TensorRT-LLM for Linux
Deployments hosting large language model inference workloads on NVIDIA GPU infrastructure
Linux-based AI/ML platforms integrating TensorRT-LLM components

Discovery Timeline

2026-07-14 - CVE-2026-24259 published to the National Vulnerability Database
2026-07-15 - Last updated in NVD database

Technical Details for CVE-2026-24259

Vulnerability Analysis

CVE-2026-24259 is a missing authentication weakness in NVIDIA TensorRT-LLM for Linux. The affected component exposes a critical function that fails to verify the identity of the caller before executing sensitive operations. An attacker with local access and elevated privileges on the host can invoke this function directly, bypassing the authentication checks that should gate access to inference control paths.

The impact spans confidentiality, integrity, and availability. Successful exploitation can lead to code execution within the context of the TensorRT-LLM process, tampering with model inputs or outputs, and disclosure of information processed by the inference engine. Because TensorRT-LLM commonly runs alongside proprietary models and customer data, the downstream consequences extend beyond the affected host.

Root Cause

The root cause is classified under [CWE-306] Missing Authentication for Critical Function. The affected code path performs a security-relevant operation without validating that the caller is authorized. The trust model assumes that local invocation implies legitimacy, which breaks down in multi-tenant environments or on hosts where lower-trust workloads share the system.

Attack Vector

Exploitation requires local access to the system running TensorRT-LLM. The attacker must already hold high privileges on the host and overcome high attack complexity conditions. User interaction is not required. Once the preconditions are met, the attacker calls the unauthenticated function directly to trigger code execution, alter inference behavior, or extract data from the process.

No verified exploitation code has been published for CVE-2026-24259. Refer to the NVD CVE-2026-24259 Entry and the CVE.org Record for CVE-2026-24259 for authoritative technical details as they become available.

Detection Methods for CVE-2026-24259

Indicators of Compromise

Unexpected child processes spawned by the TensorRT-LLM inference process on Linux hosts
Anomalous file reads or writes touching model artifacts, tokenizer files, or configuration directories used by TensorRT-LLM
Unauthorized local invocations of TensorRT-LLM management or control interfaces outside of scheduled inference workflows

Detection Strategies

Monitor process ancestry for the TensorRT-LLM runtime and alert on shell interpreters or scripting engines launched as children
Correlate local privileged sessions with subsequent access to GPU inference endpoints to identify misuse patterns
Baseline normal API and IPC calls to TensorRT-LLM components and flag deviations that bypass expected authentication flows

Monitoring Recommendations

Enable Linux audit logging for the user accounts and service principals that own TensorRT-LLM processes
Forward GPU workload telemetry and host process events to a centralized analytics platform for retention and correlation
Track privilege changes, sudo events, and container escapes on hosts running inference workloads

How to Mitigate CVE-2026-24259

Immediate Actions Required

Inventory all Linux systems running NVIDIA TensorRT-LLM and identify version levels in production and pre-production
Restrict local access to inference hosts and enforce least privilege for accounts that can interact with TensorRT-LLM
Isolate TensorRT-LLM workloads from lower-trust tenants and services running on the same host

Patch Information

Consult NVIDIA product security advisories for the fixed release addressing CVE-2026-24259 and apply the patched version once available. Track the NVD CVE-2026-24259 Entry for updated references to vendor guidance and fixed builds.

Workarounds

Limit shell and interactive access to hosts running TensorRT-LLM to a small, audited group of administrators
Deploy mandatory access controls such as SELinux or AppArmor profiles that constrain the TensorRT-LLM process and its callers
Segment inference infrastructure onto dedicated hosts or nodes to reduce co-tenancy risk until a patch is applied

bash

# Configuration example: restrict access to the TensorRT-LLM runtime directory
sudo chown -R trtllm-svc:trtllm-svc /opt/tensorrt-llm
sudo chmod -R 750 /opt/tensorrt-llm
sudo setfacl -m g:developers:--- /opt/tensorrt-llm