Answer in brief
CVE-2026-12570 records a Unknown severity vulnerability in Denial of Service via HDF5 Shape Bomb in keras.models.load_model() in keras-team/keras. The current sources do not mark it as known exploited. The current feed maps keras-team/keras-team/keras (generic). Check affected ranges and fixed versions before updating.
Analysis pending evidence review
HOL Guard separates source facts from reviewed analysis. See the methodology.
A CVSS score is not reported in the current record. The current sources do not mark it as known exploited. Treat this as a source-backed prioritization signal, not a statement about your environment.
Analysis status
Analysis pending evidence review
Factual feed record only; HOL analysis is not approved for indexing. Read the methodology.
The current feed maps keras-team/keras-team/keras (generic). Check affected ranges and fixed versions before updating.
| Package | Affected range | Fixed version |
|---|---|---|
| keras-team/keras-team/kerasgeneric | >=unspecified <3.12.3, 3.15.0 | 3.12.3, 3.15.0 |
Published upstream
Aug 10, 2026
Evidence: source:cvelist:source_dates:source-dates:recordSource modified
Aug 10, 2026
Evidence: source:cvelist:source_dates:source-dates:recordFirst seen by HOL
Aug 10, 2026
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.
Quoted source text, attributed separately from HOL analysis.
Answer in brief
CVE-2026-12570 records a Unknown severity vulnerability in Denial of Service via HDF5 Shape Bomb in keras.models.load_model() in keras-team/keras. The current sources do not mark it as known exploited. The current feed maps keras-team/keras-team/keras (generic). Check affected ranges and fixed versions before updating.
Analysis pending evidence review
HOL Guard separates source facts from reviewed analysis. See the methodology.
A CVSS score is not reported in the current record. The current sources do not mark it as known exploited. Treat this as a source-backed prioritization signal, not a statement about your environment.
Analysis status
Analysis pending evidence review
Factual feed record only; HOL analysis is not approved for indexing. Read the methodology.
The current feed maps keras-team/keras-team/keras (generic). Check affected ranges and fixed versions before updating.
| Package | Affected range | Fixed version |
|---|---|---|
| keras-team/keras-team/kerasgeneric | >=unspecified <3.12.3, 3.15.0 | 3.12.3, 3.15.0 |
Published upstream
Aug 10, 2026
Evidence: source:cvelist:source_dates:source-dates:recordSource modified
Aug 10, 2026
Evidence: source:cvelist:source_dates:source-dates:recordFirst seen by HOL
Aug 10, 2026
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.
Quoted source text, attributed separately from HOL analysis.