CVE-2026-12570
mediumpriority · track · 0EPSS 0.002 · 9 %ileEUUSEPSS · scored 2026-10-27
A vulnerability in keras-team/keras versions <= 3.15.0 allows for a denial of service (DoS) attack when loading malicious .keras model files via the keras.models.load_model() function. The H5IOStore.__getitem__ method in keras/src/saving/saving_lib.py does not validate the shape or size of datasets, leading to unbounded memory allocation. A specially crafted .keras file can exploit this flaw to trigger an out-of-memory (OOM) condition, causing the process to be terminated (exit code 137). This issue bypasses the fix for CVE-2026-0897, which only addressed a similar vulnerability in KerasFileEditor. The attack vector includes poisoned models from public repositories or malicious model registries, posing a risk to machine learning pipelines that process untrusted models.
cvss
Technical severity, 0–10. One row per scoring source.
base score 0–10 · higher = more severe · hover a vector to decode
ssvc
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weaknesses
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affected products (2)
Products and version ranges known to be affected.
advisories (2)
Vendor and ecosystem advisories describing this.
- GHSA-74m6-m3xx-3vmjKeras model loading is vulnerable to denial of service through HDF5 shape bombs2026-08-10ghsa · osvUS
A vulnerability in keras-team/keras versions < 3.15.0 allows for a denial of service (DoS) attack when loading malicious .keras model files via the keras.models.load_model() function. The H5IOStore.__getitem__ method in keras/src/saving/saving_lib.py does not validate the shape or size of datasets, leading to unbounded memory allocation. A specially crafted .keras file can exploit this flaw to trigger an out-of-memory (OOM) condition, causing the process to be terminated (exit code 137). This issue bypasses the fix for CVE-2026-0897, which only addressed a similar vulnerability in KerasFileEditor. The attack vector includes poisoned models from public repositories or malicious model registries, posing a risk to machine learning pipelines that process untrusted models.
- PYSEC-2026-3856Keras model loading is vulnerable to denial of service through HDF5 shape bombs2026-09-10vendor · osvUS
A vulnerability in keras-team/keras versions < 3.15.0 allows for a denial of service (DoS) attack when loading malicious .keras model files via the keras.models.load_model() function. The H5IOStore.__getitem__ method in keras/src/saving/saving_lib.py does not validate the shape or size of datasets, leading to unbounded memory allocation. A specially crafted .keras file can exploit this flaw to trigger an out-of-memory (OOM) condition, causing the process to be terminated (exit code 137). This issue bypasses the fix for CVE-2026-0897, which only addressed a similar vulnerability in KerasFileEditor. The attack vector includes poisoned models from public repositories or malicious model registries, posing a risk to machine learning pipelines that process untrusted models.
also known as (5)
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sources
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references (6)
External citations — patches, advisories, analysis.
- https://github.com/keras-team/keras/commit/4933ea4a5b3fcc24ceacdc276f5bb5dfbd06756ccnnvd
- https://huntr.com/bounties/a064f475-780a-409a-82f7-678512f27ad8cnnvd
- https://github.com/keras-team/keras/commit/4933ea4a5b3fcc24ceacdc276f5bb5dfbd06756cenisa_euvd
- https://huntr.com/bounties/a064f475-780a-409a-82f7-678512f27ad8enisa_euvd
- https://github.com/keras-team/keras/commit/4933ea4a5b3fcc24ceacdc276f5bb5dfbd06756cnvd
- https://huntr.com/bounties/a064f475-780a-409a-82f7-678512f27ad8nvd
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