What is the CVSS score of CVE-2026-24142?

CVE-2026-24142 has a CVSS 3.1 base score of 6.3 (Medium). CVSS vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:L/I:L/A:L. EPSS exploitation probability: 0.1%.

TensorRT-LLM CVE-2026-24142

EUVD-2026-31056 MEDIUM

Deserialization of Untrusted Data (CWE-502)

2026-05-20 nvidia

GHSA-xcw5-rrcj-8hx5

Deserialization Nvidia Information Disclosure RCE

6.3

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

6.3 MEDIUM

AV:L/AC:L/PR:L/UI:N/S:C/C:L/I:L/A:L

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:L/I:L/A:L

Attack Vector

Local

Attack Complexity

Low

Privileges Required

Low

User Interaction

None

Scope

Changed

Confidentiality

Low

Integrity

Low

Availability

Low

Lifecycle Timeline

Analysis Generated

May 20, 2026 - 04:04 vuln.today

DescriptionCVE.org

NVIDIA TRT-LLM for any platform contains a deserialization vulnerability and unsafe serialized handle. A successful exploit of this vulnerability might lead to code execution, data tampering, and information disclosure.

AnalysisAI

Deserialization of untrusted data in NVIDIA TensorRT-LLM across all platforms allows a local, low-privileged attacker to achieve code execution, data tampering, and information disclosure by exploiting an unsafe serialized handle. The CVSS Changed Scope (S:C) indicates the impact can extend beyond the vulnerable component itself - notable given TensorRT-LLM's role as an inference serving library often integrated into multi-tenant or production AI infrastructure. No public exploit identified at time of analysis, and the vulnerability is not listed in the CISA KEV catalog.

Technical ContextAI

NVIDIA TensorRT-LLM (cpe:2.3:a:nvidia:tensorrt-llm:*:*:*:*:*:*:*:*) is an open-source library that optimizes and serves large language model inference using NVIDIA GPUs. The vulnerability is rooted in CWE-502 (Deserialization of Untrusted Data): the library accepts serialized data - likely model weights, engine plans, or runtime handles - without sufficient integrity validation. The additional detail about an 'unsafe serialized handle' suggests a file descriptor or OS-level handle is embedded in the serialized stream, which when deserialized can be redirected or hijacked to access resources outside the intended scope. This class of vulnerability is well-documented in ML frameworks, where model loading pipelines often bypass security scrutiny applied to general application inputs.

RemediationAI

Consult the NVIDIA Product Security advisory at https://nvidia.custhelp.com/app/answers/detail/a_id/5805 for the official patched release version; an exact fix version is not independently confirmed in the available data beyond the advisory reference. As a compensating control pending patching, restrict local system access to the TensorRT-LLM process to only trusted, known users - reducing the PR:L attack surface. Where TensorRT-LLM loads model engines or serialized artifacts from disk, enforce path restrictions and file integrity checks (e.g., cryptographic signatures on engine files) to prevent substitution of malicious serialized payloads. Avoid loading serialized model data from untrusted or shared network locations.