EUVD-2026-44488
GHSA-89jv-4g4v-pr3h
Severity by source
AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Local malicious-file parsing requires the victim to load input (AV:L/UI:R) with no privileges (PR:N), and memory-corruption code execution yields full C:H/I:H/A:H.
Primary rating from Vendor (nvidia).
CVSS VectorVendor: nvidia
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Lifecycle Timeline
2DescriptionCVE.org
NVIDIA TensorRT contains a vulnerability where an attacker might cause a heap-based buffer overflow. A successful exploit of this vulnerability might lead to code execution.
Articles & Coverage 1
AnalysisAI
Local code execution in NVIDIA TensorRT is possible when the library parses an attacker-supplied input (such as a crafted model/engine file), triggering a heap-based buffer overflow (CWE-122) that can corrupt memory and lead to arbitrary code execution in the context of the process using TensorRT. The CVSS 3.1 vector (AV:L/UI:R) indicates the attacker needs local access and must induce a user or application to load malicious content, and there is no public exploit identified at time of analysis. …
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Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata
Vulnerability AssessmentAI
| Exploitation | Exploitation requires the attacker to get NVIDIA TensorRT to parse attacker-controlled input (a crafted model, ONNX, or serialized engine/plan file) on the local system, and per the CVSS vector requires user interaction (UI:R) - meaning a user or application must actively load/process the malicious file; it is not remotely triggerable without that step (AV:L). … Additional conditions and limiting factors are described in the full assessment. |
| Risk Assessment | The signals are moderately aligned rather than alarming: CVSS 7.8 (HIGH) reflects strong technical impact (C:H/I:H/A:H) but is bounded by AV:L (local vector) and UI:R (the victim must load the malicious input), which meaningfully lowers real-world exploitability versus a network-facing flaw. … Full risk analysis with EPSS, KEV, and SSVC signal comparison available after sign-in. |
| Exploit Scenario | An attacker crafts a malicious TensorRT engine/plan or model file and delivers it to a victim - for example by uploading it to a shared model repository, an inference service that accepts user-supplied models, or a developer's build pipeline. When the local TensorRT process deserializes or parses the file, the heap-based buffer overflow is triggered, corrupting memory and allowing the attacker to execute code in the context of the inference application. … |
| Remediation | Patch available per vendor advisory: consult NVIDIA security bulletin https://nvidia.custhelp.com/app/answers/detail/a_id/5855 and upgrade to the fixed TensorRT version specified there (an exact fixed version string is not present in the provided input, so verify it directly from that advisory before rollout). … Detailed patch versions, workarounds, and compensating controls in full report. |
Recommended ActionAI
Within 24 hours, identify all systems running NVIDIA TensorRT and document their role in production ML/AI workflows; isolate or restrict external access to systems where TensorRT is non-critical. …
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Code execution in NVIDIA TensorRT is possible when the SDK processes a maliciously crafted input that overflows a heap-b
Improper array index validation (CWE-129) in NVIDIA TensorRT allows an attacker to trigger out-of-bounds memory access t
NVIDIA TensorRT for contains a vulnerability where a user might cause a deserialization of untrusted data. A successful
Same weakness CWE-122 – Heap-based Buffer Overflow
View allSame technique Heap Overflow
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External POC / Exploit Code
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