EUVD-2026-44489
GHSA-jp25-r59g-9p63
Severity by source
AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Local file-parsing flaw needing no privileges but victim interaction (AV:L/PR:N/UI:R); crafted-input heap overflow yields full code execution, so C/I/A all High.
Primary rating from Vendor (nvidia).
CVSS VectorVendor: nvidia
Lifecycle Timeline
2DescriptionCVE.org
NVIDIA TensorRT contains a vulnerability where an attacker might cause an overflow to a heap-based buffer. A successful exploit of this vulnerability might lead to code execution.
Articles & Coverage 1
AnalysisAI
Code execution in NVIDIA TensorRT is possible when the SDK processes a maliciously crafted input that overflows a heap-based buffer (CWE-122), corrupting adjacent heap memory. The flaw affects the TensorRT deep-learning inference library and requires a local user to load attacker-supplied content, per the AV:L/UI:R CVSS vector; there is no public exploit identified at time of analysis and it is not listed in CISA KEV. Successful exploitation yields full loss of confidentiality, integrity, and availability (C:H/I:H/A:H) in the context of the process running the inference job.
Technical ContextAI
TensorRT is NVIDIA's high-performance deep-learning inference optimizer and runtime SDK, used to build and execute optimized inference 'engines' from model formats such as ONNX. The affected CPE cpe:2.3:a:nvidia:tensorrt:*:*:*:*:*:*:*:* indicates the application/library itself rather than a specific driver or firmware component. The root cause is classified as CWE-122 (Heap-Based Buffer Overflow): during parsing or deserialization of model/engine data, the library writes past the bounds of a heap-allocated buffer - typically the result of trusting an attacker-controllable size or length field without adequate bounds validation - allowing controlled heap corruption that can be steered toward arbitrary code execution.
RemediationAI
Consult NVIDIA's advisory (a_id/5855) at https://nvidia.custhelp.com/app/answers/detail/a_id/5855 and upgrade TensorRT to the fixed release identified there; an exact fixed version is not present in the provided input, so treat the advisory as authoritative rather than assuming a version number. Because exploitation requires processing attacker-supplied content, the most effective compensating control is to only load models and serialized engine files from trusted, integrity-verified sources - validate provenance and checksums before deserialization, and avoid deserializing engines received from untrusted parties (deserialization of untrusted TensorRT engines is inherently risky). Where feasible, run inference workloads in a sandboxed or least-privilege container so heap corruption is contained to a low-privilege context, accepting the trade-off of added deployment complexity and marginal performance overhead. Patch status: Patch available per vendor advisory; released fixed version not independently confirmed from the provided data.
Local code execution in NVIDIA TensorRT is possible when the library parses an attacker-supplied input (such as a crafte
Improper array index validation (CWE-129) in NVIDIA TensorRT allows an attacker to trigger out-of-bounds memory access t
Out-of-bounds write in NVIDIA TensorRT prior to version 10.16.1 allows an attacker to corrupt memory and tamper with dat
Code execution in NVIDIA TensorRT (all versions through v10.16.1) arises from unsafe deserialization of untrusted data (
Same weakness CWE-122 – Heap-based Buffer Overflow
View allSame technique Heap Overflow
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External POC / Exploit Code
Leaving vuln.today