CVE-2026-12570: Keras model loading is vulnerable to denial of service through HDF5 shape bombs
Affects kerasSource GCVE Database
Mentioned vulnerabilities
Not known to be exploited · CVSS 5.5 Medium : upgrade to 3.15.0 when convenient.
Data as of 26 Oct 2026, 17:16 UTC
- EPSS
- 0.1% chance of exploitation in 30 days – higher than 8% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
- Patch
- Available – fixed in 3.15.0
CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:HExploitation evidence
Disclosed: evidence found.
CVE published 10 Aug 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.
What to do
Upgrade keras to 3.15.0.
Affected software
| Product | Affected | Fixed in |
|---|---|---|
| PyPIkeras | Affected
| Fixed in
|
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
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.
References
- nvd.nist.gov/vuln/detail/CVE-2026-12570 (opens in a new tab)
- github.com/keras-team/keras/pull/22975 (opens in a new tab)
- github.com/keras-team/keras/commit/4933ea4a5b3fcc24ceacdc276f5bb5dfbd06756c (opens in a new tab)
- github.com/keras-team/keras (opens in a new tab)
- github.com/keras-team/keras/releases/tag/v3.15.0 (opens in a new tab)
- huntr.com/bounties/a064f475-780a-409a-82f7-678512f27ad8 (opens in a new tab)