Vulnerability-Lookup

OESA-2026-3952 (CVE-2026-12570)

Vulnerability from osv_openeuler – Published: 2026-09-20 13:23 – Updated: 2026-09-20 13:23 – Source website

Summary

python-Keras security update

Details

Keras is a high-level neural networks API for Python.

Security Fix(es):

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.(CVE-2026-12570)

Severity

5.5 (Medium)


                    
                      CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H

References

URL

Type

	https://www.openeuler.org/zh/security/security-bu…	ADVISORY
	https://nvd.nist.gov/vuln/detail/CVE-2026-12570	ADVISORY

JSON

To clipboard

{
  "affected": [
    {
      "ecosystem_specific": {
        "noarch": [
          "python-Keras-help-2.12.0-5.oe2403sp1.noarch.rpm",
          "python3-Keras-2.12.0-5.oe2403sp1.noarch.rpm"
        ],
        "src": [
          "python-Keras-2.12.0-5.oe2403sp1.src.rpm"
        ]
      },
      "package": {
        "ecosystem": "openEuler:24.03-LTS-SP1",
        "name": "python-Keras",
        "purl": "pkg:rpm/openEuler/python-Keras\u0026distro=openEuler-24.03-LTS-SP1"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.12.0-5.oe2403sp1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "database_specific": {
    "severity": "Medium"
  },
  "details": "Keras is a high-level neural networks API for Python.\r\n\r\nSecurity Fix(es):\n\nA vulnerability in keras-team/keras versions \u0026lt;= 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.(CVE-2026-12570)",
  "id": "OESA-2026-3952",
  "modified": "2026-09-20T13:23:32Z",
  "published": "2026-09-20T13:23:32Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://www.openeuler.org/zh/security/security-bulletins/detail/?id=openEuler-SA-2026-3952"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-12570"
    }
  ],
  "schema_version": "1.7.2",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "python-Keras security update",
  "upstream": [
    "CVE-2026-12570"
  ]
}

CVE-2026-12570 (GCVE-0-2026-12570)

Vulnerability from cvelistv5 – Published: 2026-08-10 06:29 – Updated: 2026-08-10 17:37

Title

Denial of Service via HDF5 Shape Bomb in keras.models.load_model() in keras-team/keras

Summary

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.

Severity

5.5 (Medium)


                        
                          CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H

SSVC

Exploitation: poc Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-08-10 17:37 UTC

CWE

CWE-770 - Allocation of Resources Without Limits or Throttling

Assigner

@huntr_ai mitre root

References

2 references

URL	Tags
https://huntr.com/bounties/a064f475-780a-409a-82f…
https://github.com/keras-team/keras/commit/4933ea…

Impacted products

1 product

Vendor	Product	Version
keras-team	keras-team/keras	Affected: unspecified , < 3.12.3, 3.15.0 (custom)

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "metrics": [
          {
            "other": {
              "content": {
                "id": "CVE-2026-12570",
                "options": [
                  {
                    "Exploitation": "poc"
                  },
                  {
                    "Automatable": "no"
                  },
                  {
                    "Technical Impact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2026-08-10T17:37:17.637683Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2026-08-10T17:37:39.037Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "references": [
          {
            "tags": [
              "exploit"
            ],
            "url": "https://huntr.com/bounties/a064f475-780a-409a-82f7-678512f27ad8"
          }
        ],
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "keras-team/keras",
          "vendor": "keras-team",
          "versions": [
            {
              "lessThan": "3.12.3, 3.15.0",
              "status": "affected",
              "version": "unspecified",
              "versionType": "custom"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "A vulnerability in keras-team/keras versions \u003c= 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."
        }
      ],
      "metrics": [
        {
          "cvssV3_0": {
            "attackComplexity": "LOW",
            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 5.5,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
            "version": "3.0"
          }
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-770",
              "description": "CWE-770 Allocation of Resources Without Limits or Throttling",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2026-08-10T06:29:56.111Z",
        "orgId": "c09c270a-b464-47c1-9133-acb35b22c19a",
        "shortName": "@huntr_ai"
      },
      "references": [
        {
          "url": "https://huntr.com/bounties/a064f475-780a-409a-82f7-678512f27ad8"
        },
        {
          "url": "https://github.com/keras-team/keras/commit/4933ea4a5b3fcc24ceacdc276f5bb5dfbd06756c"
        }
      ],
      "source": {
        "advisory": "a064f475-780a-409a-82f7-678512f27ad8",
        "discovery": "EXTERNAL"
      },
      "title": "Denial of Service via HDF5 Shape Bomb in keras.models.load_model() in keras-team/keras"
    }
  },
  "cveMetadata": {
    "assignerOrgId": "c09c270a-b464-47c1-9133-acb35b22c19a",
    "assignerShortName": "@huntr_ai",
    "cveId": "CVE-2026-12570",
    "datePublished": "2026-08-10T06:29:56.111Z",
    "dateReserved": "2026-06-18T01:55:25.311Z",
    "dateUpdated": "2026-08-10T17:37:39.037Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.2"
}

Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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OESA-2026-3952 (CVE-2026-12570)

CVE-2026-12570 (GCVE-0-2026-12570)

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