CVE-2026-12570: CWE-770 Allocation of Resources Without Limits or Throttling in keras-team keras-team/keras
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.
AI Analysis
Technical Summary
CVE-2026-12570 is a resource exhaustion vulnerability (CWE-770) in keras-team/keras versions up to 3.15.0. The flaw exists in the keras.models.load_model() function, specifically in the H5IOStore.__getitem__ method within keras/src/saving/saving_lib.py, which does not validate the shape or size of datasets when loading .keras files. An attacker can craft a malicious .keras model file that triggers unbounded memory allocation, causing an out-of-memory (OOM) condition and process termination (exit code 137). This vulnerability bypasses the previous fix for CVE-2026-0897 and poses a risk when loading models from untrusted sources such as public repositories or model registries.
Potential Impact
Successful exploitation results in denial of service due to out-of-memory conditions causing the Keras process to terminate unexpectedly. There is no impact on confidentiality or integrity, only availability is affected.
Mitigation Recommendations
Patch status is not yet confirmed — check the vendor advisory for current remediation guidance. Until a fix is available, avoid loading .keras model files from untrusted or unverified sources to mitigate risk.
CVE-2026-12570: CWE-770 Allocation of Resources Without Limits or Throttling in keras-team keras-team/keras
Description
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 v3.0
Score 5.5medium
Affected software
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Weaknesses
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
CVE-2026-12570 is a resource exhaustion vulnerability (CWE-770) in keras-team/keras versions up to 3.15.0. The flaw exists in the keras.models.load_model() function, specifically in the H5IOStore.__getitem__ method within keras/src/saving/saving_lib.py, which does not validate the shape or size of datasets when loading .keras files. An attacker can craft a malicious .keras model file that triggers unbounded memory allocation, causing an out-of-memory (OOM) condition and process termination (exit code 137). This vulnerability bypasses the previous fix for CVE-2026-0897 and poses a risk when loading models from untrusted sources such as public repositories or model registries.
Potential Impact
Successful exploitation results in denial of service due to out-of-memory conditions causing the Keras process to terminate unexpectedly. There is no impact on confidentiality or integrity, only availability is affected.
Mitigation Recommendations
Patch status is not yet confirmed — check the vendor advisory for current remediation guidance. Until a fix is available, avoid loading .keras model files from untrusted or unverified sources to mitigate risk.
Technical Details
- Data Version
- 5.2
- Assigner Short Name
- @huntr_ai
- Date Reserved
- 2026-06-18T01:55:25.311Z
- Cvss Version
- 3.0
- State
- PUBLISHED
- Remediation Level
- null
Threat ID: 6a7972aabf8831d5392faf66
Added to database: 08/10/2026, 06:41:46 UTC
Last enriched: 08/10/2026, 07:33:41 UTC
Last updated: 08/11/2026, 03:40:59 UTC
Views: 8
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