NVIDIA Megatron-LM and CUDA Toolkit CVEs: Your AI Tooling is an Attack Surface
NVIDIA’s late-September disclosures land exactly where many engineers work every day: Megatron-LM training helpers and CUDA Toolkit utilities like nvdisasm, cuobjdump, and nvJPEG. The Megatron-LM issues represent “data → code” risk: poisoned datasets or manifests can become execution inside preprocessing or training steps. The CUDA set is a reminder that file parsers aren’t safe just because they’re developer tools—crafted ELF or image inputs can crash your session, leak memory, or, in certain cases, execute code with your user privileges. Patch first, then change habits: sandbox analyzers in short-lived containers/VMs, cut network/host mounts, drop caps, and treat every dataset as untrusted.
Megatron-LM CVEs
Each CVE links to our annotated report with a tailored Q&A, an AI Assistant that answers “what does this mean in my environment?”, and an Attack Flow Graph.
| CVE ID | Summary (short) |
|---|---|
| CVE-2025-23348 | pretrain_gpt script – code injection (CVSS 7.8) |
| CVE-2025-23353 | msdp preprocessing – injection → code execution / EoP / disclosure (7.8) |
| CVE-2025-23354 | ensemble_classifier – injection (7.8) |
| CVE-2025-23349 | tasks/orqa/unsupervised/nq.py – code injection (7.8) |
CUDA Toolkit & nvJPEG CVEs
These affect workflows where developers inspect ELF binaries or process images with NVIDIA tooling.
| CVE ID | Component | Summary (short) |
|---|---|---|
| CVE-2025-23308 | nvdisasm |
Heap overflow via crafted ELF → user-priv code execution |
| CVE-2025-23248 | nvdisasm |
Out-of-bounds read → partial DoS |
| CVE-2025-23338 | nvdisasm |
Out-of-bounds write → DoS |
| CVE-2025-23340 | nvdisasm |
Out-of-bounds read → partial DoS |
| CVE-2025-23271 | nvdisasm |
Out-of-bounds read → partial DoS |
| CVE-2025-23339 | cuobjdump |
Stack-based buffer overflow → user-priv code execution |
| CVE-2025-23346 | cuobjdump |
NULL pointer dereference → limited DoS |
| CVE-2025-23255 | cuobjdump |
Out-of-bounds read → partial DoS |
| CVE-2025-23273 | nvJPEG |
Divide-by-zero via crafted JPEG → DoS |
| CVE-2025-23274 | nvJPEG |
OOB read via integer overflow in indexing → DoS |
| CVE-2025-23275 | nvJPEG |
GPU OOB write on certain dimensions → DoS / info disclosure |
| CVE-2025-23272 | nvJPEG |
OOB read via crafted JPEG → info disclosure / DoS |
Patch & Mitigate
Start with versions, not theory. Update Megatron-LM to 0.13.1 (or 0.12.3 on the previous branch) and pin those releases in CI images so you don’t drift back. Move CUDA to 13.0 so the nvdisasm/cuobjdump fixes are present, and align nvJPEG with 13.0.0 (plus 25.03 where NVIDIA’s matrix notes it). After upgrading, record hashes of training scripts and helper binaries and store them with build metadata to prove remediation.
Assume you will encounter malformed inputs again. Run nvdisasm/cuobjdump inside ephemeral containers or VMs with no network, no host mounts, and dropped capabilities; consider user-namespaces for additional blast-radius control. Keep sample inputs read-only and isolated from home and project trees. In training code, strip dynamic evaluation and shell-outs from preprocessing; parse strictly, validate aggressively, and treat every dataset, manifest, and annotation as untrusted. Place CUDA analysis and training stages on isolated CI runners that rebuild from clean images between jobs.
Add components to your Watch List
Use your actual deployed versions—one entry per branch you operate:
cpe:2.3:a:nvidia:megatron-lm:0.13.1cpe:2.3:a:nvidia:megatron-lm:0.12.3cpe:2.3:a:nvidia:cuda_toolkit:13.0cpe:2.3:a:nvidia:nvjpeg:13.0.0cpe:2.3:a:nvidia:nvjpeg:25.03
If you want this analysis—and future NVIDIA advisories—mapped automatically to your machines, runners, and training hosts, our annotated reports include a tailored Q&A, an AI Assistant that answers impact questions in context, and an Attack Flow Graph per CVE to speed up triage. Register free: https://basefortify.eu/register
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