cvelistv5 - cve-2025-9906

CVE-2025-9906 (GCVE-0-2025-9906)

Vulnerability from cvelistv5 – Published: 2025-09-19 08:15 – Updated: 2025-09-20 03:55

Summary

The Keras Model.load_model method can be exploited to achieve arbitrary code execution, even with safe_mode=True. One can create a specially crafted .keras model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed. This is achieved by crafting a special config.json (a file within the .keras archive) that will invoke keras.config.enable_unsafe_deserialization() to disable safe mode. Once safe mode is disable, one can use the Lambda layer feature of keras, which allows arbitrary Python code in the form of pickled code. Both can appear in the same archive. Simply the keras.config.enable_unsafe_deserialization() needs to appear first in the archive and the Lambda with arbitrary code needs to be second.

Severity ?

8.6 (High)


                        
                          CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:P/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/AU:Y/R:A

CWE

CWE-502 - Deserialization of Untrusted Data

Assigner

Google

References

URL

Tags

https://github.com/keras-team/keras/pull/21429

patch

Impacted products

	Vendor	Product	Version
	Keras-team	Keras	Affected: 3.0.0 , < 3.11.0 (semver)

Credits

Gabriele Digregorio

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "metrics": [
          {
            "other": {
              "content": {
                "id": "CVE-2025-9906",
                "options": [
                  {
                    "Exploitation": "none"
                  },
                  {
                    "Automatable": "no"
                  },
                  {
                    "Technical Impact": "total"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2025-09-19T00:00:00+00:00",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2025-09-20T03:55:41.698Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "defaultStatus": "unaffected",
          "product": "Keras",
          "repo": "https://github.com/keras-team/keras",
          "vendor": "Keras-team",
          "versions": [
            {
              "lessThan": "3.11.0",
              "status": "affected",
              "version": "3.0.0",
              "versionType": "semver"
            }
          ]
        }
      ],
      "credits": [
        {
          "lang": "en",
          "type": "finder",
          "value": "Gabriele Digregorio"
        }
      ],
      "datePublic": "2025-06-29T09:00:00.000Z",
      "descriptions": [
        {
          "lang": "en",
          "supportingMedia": [
            {
              "base64": false,
              "type": "text/html",
              "value": "\u003cp\u003eThe Keras \u003ccode\u003eModel.load_model\u003c/code\u003e\u0026nbsp;method can be exploited to achieve arbitrary code execution, even with \u003ccode\u003esafe_mode=True\u003c/code\u003e.\u003c/p\u003e\u003cp\u003eOne can create a specially crafted \u003ccode\u003e.keras\u003c/code\u003e\u0026nbsp;model archive that, when loaded via \u003ccode\u003eModel.load_model\u003c/code\u003e, will trigger arbitrary code to be executed. This is achieved by crafting a special \u003ccode\u003econfig.json\u003c/code\u003e\u0026nbsp;(a file within the \u003ccode\u003e.keras\u003c/code\u003e\u0026nbsp;archive) that will invoke \u003ccode\u003ekeras.config.enable_unsafe_deserialization()\u003c/code\u003e\u0026nbsp;to disable safe mode. Once safe mode is disable, one can use the \u003ccode\u003eLambda\u003c/code\u003e\u0026nbsp;layer feature of keras, which allows arbitrary Python code in the form of pickled code. Both can appear in the same archive. Simply the \u003ccode\u003ekeras.config.enable_unsafe_deserialization()\u003c/code\u003e\u0026nbsp;needs to appear first in the archive and the \u003ccode\u003eLambda\u003c/code\u003e\u0026nbsp;with arbitrary code needs to be second.\u003c/p\u003e\u003cbr\u003e"
            }
          ],
          "value": "The Keras Model.load_model\u00a0method can be exploited to achieve arbitrary code execution, even with safe_mode=True.\n\nOne can create a specially crafted .keras\u00a0model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed. This is achieved by crafting a special config.json\u00a0(a file within the .keras\u00a0archive) that will invoke keras.config.enable_unsafe_deserialization()\u00a0to disable safe mode. Once safe mode is disable, one can use the Lambda\u00a0layer feature of keras, which allows arbitrary Python code in the form of pickled code. Both can appear in the same archive. Simply the keras.config.enable_unsafe_deserialization()\u00a0needs to appear first in the archive and the Lambda\u00a0with arbitrary code needs to be second."
        }
      ],
      "impacts": [
        {
          "capecId": "CAPEC-242",
          "descriptions": [
            {
              "lang": "en",
              "value": "CAPEC-242 Code Injection"
            }
          ]
        }
      ],
      "metrics": [
        {
          "cvssV4_0": {
            "Automatable": "YES",
            "Recovery": "AUTOMATIC",
            "Safety": "NOT_DEFINED",
            "attackComplexity": "LOW",
            "attackRequirements": "NONE",
            "attackVector": "LOCAL",
            "baseScore": 8.6,
            "baseSeverity": "HIGH",
            "privilegesRequired": "LOW",
            "providerUrgency": "NOT_DEFINED",
            "subAvailabilityImpact": "HIGH",
            "subConfidentialityImpact": "HIGH",
            "subIntegrityImpact": "HIGH",
            "userInteraction": "PASSIVE",
            "valueDensity": "NOT_DEFINED",
            "vectorString": "CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:P/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/AU:Y/R:A",
            "version": "4.0",
            "vulnAvailabilityImpact": "HIGH",
            "vulnConfidentialityImpact": "HIGH",
            "vulnIntegrityImpact": "HIGH",
            "vulnerabilityResponseEffort": "NOT_DEFINED"
          },
          "format": "CVSS",
          "scenarios": [
            {
              "lang": "en",
              "value": "GENERAL"
            }
          ]
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-502",
              "description": "CWE-502 Deserialization of Untrusted Data",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2025-09-19T08:15:04.349Z",
        "orgId": "14ed7db2-1595-443d-9d34-6215bf890778",
        "shortName": "Google"
      },
      "references": [
        {
          "tags": [
            "patch"
          ],
          "url": "https://github.com/keras-team/keras/pull/21429"
        }
      ],
      "source": {
        "discovery": "EXTERNAL"
      },
      "title": "Arbitrary Code execution in Keras Safe Mode",
      "x_generator": {
        "engine": "Vulnogram 0.2.0"
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "14ed7db2-1595-443d-9d34-6215bf890778",
    "assignerShortName": "Google",
    "cveId": "CVE-2025-9906",
    "datePublished": "2025-09-19T08:15:04.349Z",
    "dateReserved": "2025-09-03T07:27:23.895Z",
    "dateUpdated": "2025-09-20T03:55:41.698Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1",
  "vulnerability-lookup:meta": {
    "nvd": "{\"cve\":{\"id\":\"CVE-2025-9906\",\"sourceIdentifier\":\"cve-coordination@google.com\",\"published\":\"2025-09-19T09:15:36.353\",\"lastModified\":\"2025-09-23T16:53:28.170\",\"vulnStatus\":\"Analyzed\",\"cveTags\":[],\"descriptions\":[{\"lang\":\"en\",\"value\":\"The Keras Model.load_model\u00a0method can be exploited to achieve arbitrary code execution, even with safe_mode=True.\\n\\nOne can create a specially crafted .keras\u00a0model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed. This is achieved by crafting a special config.json\u00a0(a file within the .keras\u00a0archive) that will invoke keras.config.enable_unsafe_deserialization()\u00a0to disable safe mode. Once safe mode is disable, one can use the Lambda\u00a0layer feature of keras, which allows arbitrary Python code in the form of pickled code. Both can appear in the same archive. Simply the keras.config.enable_unsafe_deserialization()\u00a0needs to appear first in the archive and the Lambda\u00a0with arbitrary code needs to be second.\"}],\"metrics\":{\"cvssMetricV40\":[{\"source\":\"cve-coordination@google.com\",\"type\":\"Secondary\",\"cvssData\":{\"version\":\"4.0\",\"vectorString\":\"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:P/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:Y/R:A/V:X/RE:X/U:X\",\"baseScore\":8.6,\"baseSeverity\":\"HIGH\",\"attackVector\":\"LOCAL\",\"attackComplexity\":\"LOW\",\"attackRequirements\":\"NONE\",\"privilegesRequired\":\"LOW\",\"userInteraction\":\"PASSIVE\",\"vulnConfidentialityImpact\":\"HIGH\",\"vulnIntegrityImpact\":\"HIGH\",\"vulnAvailabilityImpact\":\"HIGH\",\"subConfidentialityImpact\":\"HIGH\",\"subIntegrityImpact\":\"HIGH\",\"subAvailabilityImpact\":\"HIGH\",\"exploitMaturity\":\"NOT_DEFINED\",\"confidentialityRequirement\":\"NOT_DEFINED\",\"integrityRequirement\":\"NOT_DEFINED\",\"availabilityRequirement\":\"NOT_DEFINED\",\"modifiedAttackVector\":\"NOT_DEFINED\",\"modifiedAttackComplexity\":\"NOT_DEFINED\",\"modifiedAttackRequirements\":\"NOT_DEFINED\",\"modifiedPrivilegesRequired\":\"NOT_DEFINED\",\"modifiedUserInteraction\":\"NOT_DEFINED\",\"modifiedVulnConfidentialityImpact\":\"NOT_DEFINED\",\"modifiedVulnIntegrityImpact\":\"NOT_DEFINED\",\"modifiedVulnAvailabilityImpact\":\"NOT_DEFINED\",\"modifiedSubConfidentialityImpact\":\"NOT_DEFINED\",\"modifiedSubIntegrityImpact\":\"NOT_DEFINED\",\"modifiedSubAvailabilityImpact\":\"NOT_DEFINED\",\"Safety\":\"NOT_DEFINED\",\"Automatable\":\"YES\",\"Recovery\":\"AUTOMATIC\",\"valueDensity\":\"NOT_DEFINED\",\"vulnerabilityResponseEffort\":\"NOT_DEFINED\",\"providerUrgency\":\"NOT_DEFINED\"}}],\"cvssMetricV31\":[{\"source\":\"nvd@nist.gov\",\"type\":\"Primary\",\"cvssData\":{\"version\":\"3.1\",\"vectorString\":\"CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H\",\"baseScore\":7.3,\"baseSeverity\":\"HIGH\",\"attackVector\":\"LOCAL\",\"attackComplexity\":\"LOW\",\"privilegesRequired\":\"LOW\",\"userInteraction\":\"REQUIRED\",\"scope\":\"UNCHANGED\",\"confidentialityImpact\":\"HIGH\",\"integrityImpact\":\"HIGH\",\"availabilityImpact\":\"HIGH\"},\"exploitabilityScore\":1.3,\"impactScore\":5.9}]},\"weaknesses\":[{\"source\":\"cve-coordination@google.com\",\"type\":\"Secondary\",\"description\":[{\"lang\":\"en\",\"value\":\"CWE-502\"}]}],\"configurations\":[{\"nodes\":[{\"operator\":\"OR\",\"negate\":false,\"cpeMatch\":[{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:keras:keras:*:*:*:*:*:*:*:*\",\"versionStartIncluding\":\"3.0.0\",\"versionEndExcluding\":\"3.11.0\",\"matchCriteriaId\":\"573E88B6-532F-4616-AC15-659DD4DF72F8\"}]}]}],\"references\":[{\"url\":\"https://github.com/keras-team/keras/pull/21429\",\"source\":\"cve-coordination@google.com\",\"tags\":[\"Issue Tracking\"]}]}}",
    "vulnrichment": {
      "containers": "{\"adp\": [{\"title\": \"CISA ADP Vulnrichment\", \"metrics\": [{\"other\": {\"type\": \"ssvc\", \"content\": {\"id\": \"CVE-2025-9906\", \"role\": \"CISA Coordinator\", \"options\": [{\"Exploitation\": \"none\"}, {\"Automatable\": \"no\"}, {\"Technical Impact\": \"total\"}], \"version\": \"2.0.3\", \"timestamp\": \"2025-09-19T11:49:12.992944Z\"}}}], \"providerMetadata\": {\"orgId\": \"134c704f-9b21-4f2e-91b3-4a467353bcc0\", \"shortName\": \"CISA-ADP\", \"dateUpdated\": \"2025-09-19T11:51:40.833Z\"}}], \"cna\": {\"title\": \"Arbitrary Code execution in Keras Safe Mode\", \"source\": {\"discovery\": \"EXTERNAL\"}, \"credits\": [{\"lang\": \"en\", \"type\": \"finder\", \"value\": \"Gabriele Digregorio\"}], \"impacts\": [{\"capecId\": \"CAPEC-242\", \"descriptions\": [{\"lang\": \"en\", \"value\": \"CAPEC-242 Code Injection\"}]}], \"metrics\": [{\"format\": \"CVSS\", \"cvssV4_0\": {\"Safety\": \"NOT_DEFINED\", \"version\": \"4.0\", \"Recovery\": \"AUTOMATIC\", \"baseScore\": 8.6, \"Automatable\": \"YES\", \"attackVector\": \"LOCAL\", \"baseSeverity\": \"HIGH\", \"valueDensity\": \"NOT_DEFINED\", \"vectorString\": \"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:P/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/AU:Y/R:A\", \"providerUrgency\": \"NOT_DEFINED\", \"userInteraction\": \"PASSIVE\", \"attackComplexity\": \"LOW\", \"attackRequirements\": \"NONE\", \"privilegesRequired\": \"LOW\", \"subIntegrityImpact\": \"HIGH\", \"vulnIntegrityImpact\": \"HIGH\", \"subAvailabilityImpact\": \"HIGH\", \"vulnAvailabilityImpact\": \"HIGH\", \"subConfidentialityImpact\": \"HIGH\", \"vulnConfidentialityImpact\": \"HIGH\", \"vulnerabilityResponseEffort\": \"NOT_DEFINED\"}, \"scenarios\": [{\"lang\": \"en\", \"value\": \"GENERAL\"}]}], \"affected\": [{\"repo\": \"https://github.com/keras-team/keras\", \"vendor\": \"Keras-team\", \"product\": \"Keras\", \"versions\": [{\"status\": \"affected\", \"version\": \"3.0.0\", \"lessThan\": \"3.11.0\", \"versionType\": \"semver\"}], \"defaultStatus\": \"unaffected\"}], \"datePublic\": \"2025-06-29T09:00:00.000Z\", \"references\": [{\"url\": \"https://github.com/keras-team/keras/pull/21429\", \"tags\": [\"patch\"]}], \"x_generator\": {\"engine\": \"Vulnogram 0.2.0\"}, \"descriptions\": [{\"lang\": \"en\", \"value\": \"The Keras Model.load_model\\u00a0method can be exploited to achieve arbitrary code execution, even with safe_mode=True.\\n\\nOne can create a specially crafted .keras\\u00a0model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed. This is achieved by crafting a special config.json\\u00a0(a file within the .keras\\u00a0archive) that will invoke keras.config.enable_unsafe_deserialization()\\u00a0to disable safe mode. Once safe mode is disable, one can use the Lambda\\u00a0layer feature of keras, which allows arbitrary Python code in the form of pickled code. Both can appear in the same archive. Simply the keras.config.enable_unsafe_deserialization()\\u00a0needs to appear first in the archive and the Lambda\\u00a0with arbitrary code needs to be second.\", \"supportingMedia\": [{\"type\": \"text/html\", \"value\": \"\u003cp\u003eThe Keras \u003ccode\u003eModel.load_model\u003c/code\u003e\u0026nbsp;method can be exploited to achieve arbitrary code execution, even with \u003ccode\u003esafe_mode=True\u003c/code\u003e.\u003c/p\u003e\u003cp\u003eOne can create a specially crafted \u003ccode\u003e.keras\u003c/code\u003e\u0026nbsp;model archive that, when loaded via \u003ccode\u003eModel.load_model\u003c/code\u003e, will trigger arbitrary code to be executed. This is achieved by crafting a special \u003ccode\u003econfig.json\u003c/code\u003e\u0026nbsp;(a file within the \u003ccode\u003e.keras\u003c/code\u003e\u0026nbsp;archive) that will invoke \u003ccode\u003ekeras.config.enable_unsafe_deserialization()\u003c/code\u003e\u0026nbsp;to disable safe mode. Once safe mode is disable, one can use the \u003ccode\u003eLambda\u003c/code\u003e\u0026nbsp;layer feature of keras, which allows arbitrary Python code in the form of pickled code. Both can appear in the same archive. Simply the \u003ccode\u003ekeras.config.enable_unsafe_deserialization()\u003c/code\u003e\u0026nbsp;needs to appear first in the archive and the \u003ccode\u003eLambda\u003c/code\u003e\u0026nbsp;with arbitrary code needs to be second.\u003c/p\u003e\u003cbr\u003e\", \"base64\": false}]}], \"problemTypes\": [{\"descriptions\": [{\"lang\": \"en\", \"type\": \"CWE\", \"cweId\": \"CWE-502\", \"description\": \"CWE-502 Deserialization of Untrusted Data\"}]}], \"providerMetadata\": {\"orgId\": \"14ed7db2-1595-443d-9d34-6215bf890778\", \"shortName\": \"Google\", \"dateUpdated\": \"2025-09-19T08:15:04.349Z\"}}}",
      "cveMetadata": "{\"cveId\": \"CVE-2025-9906\", \"state\": \"PUBLISHED\", \"dateUpdated\": \"2025-09-20T03:55:41.698Z\", \"dateReserved\": \"2025-09-03T07:27:23.895Z\", \"assignerOrgId\": \"14ed7db2-1595-443d-9d34-6215bf890778\", \"datePublished\": \"2025-09-19T08:15:04.349Z\", \"assignerShortName\": \"Google\"}",
      "dataType": "CVE_RECORD",
      "dataVersion": "5.1"
    }
  }
}

FKIE_CVE-2025-9906

Vulnerability from fkie_nvd - Published: 2025-09-19 09:15 - Updated: 2025-09-23 16:53

Severity ?

7.3 (High) - CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H

Summary

References

	URL	Tags
	cve-coordination@google.com	https://github.com/keras-team/keras/pull/21429	Issue Tracking

Impacted products

	Vendor	Product	Version
	keras	keras	*

JSON

To clipboard

{
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:keras:keras:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "573E88B6-532F-4616-AC15-659DD4DF72F8",
              "versionEndExcluding": "3.11.0",
              "versionStartIncluding": "3.0.0",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "The Keras Model.load_model\u00a0method can be exploited to achieve arbitrary code execution, even with safe_mode=True.\n\nOne can create a specially crafted .keras\u00a0model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed. This is achieved by crafting a special config.json\u00a0(a file within the .keras\u00a0archive) that will invoke keras.config.enable_unsafe_deserialization()\u00a0to disable safe mode. Once safe mode is disable, one can use the Lambda\u00a0layer feature of keras, which allows arbitrary Python code in the form of pickled code. Both can appear in the same archive. Simply the keras.config.enable_unsafe_deserialization()\u00a0needs to appear first in the archive and the Lambda\u00a0with arbitrary code needs to be second."
    }
  ],
  "id": "CVE-2025-9906",
  "lastModified": "2025-09-23T16:53:28.170",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "LOCAL",
          "availabilityImpact": "HIGH",
          "baseScore": 7.3,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "LOW",
          "scope": "UNCHANGED",
          "userInteraction": "REQUIRED",
          "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 1.3,
        "impactScore": 5.9,
        "source": "nvd@nist.gov",
        "type": "Primary"
      }
    ],
    "cvssMetricV40": [
      {
        "cvssData": {
          "Automatable": "YES",
          "Recovery": "AUTOMATIC",
          "Safety": "NOT_DEFINED",
          "attackComplexity": "LOW",
          "attackRequirements": "NONE",
          "attackVector": "LOCAL",
          "availabilityRequirement": "NOT_DEFINED",
          "baseScore": 8.6,
          "baseSeverity": "HIGH",
          "confidentialityRequirement": "NOT_DEFINED",
          "exploitMaturity": "NOT_DEFINED",
          "integrityRequirement": "NOT_DEFINED",
          "modifiedAttackComplexity": "NOT_DEFINED",
          "modifiedAttackRequirements": "NOT_DEFINED",
          "modifiedAttackVector": "NOT_DEFINED",
          "modifiedPrivilegesRequired": "NOT_DEFINED",
          "modifiedSubAvailabilityImpact": "NOT_DEFINED",
          "modifiedSubConfidentialityImpact": "NOT_DEFINED",
          "modifiedSubIntegrityImpact": "NOT_DEFINED",
          "modifiedUserInteraction": "NOT_DEFINED",
          "modifiedVulnAvailabilityImpact": "NOT_DEFINED",
          "modifiedVulnConfidentialityImpact": "NOT_DEFINED",
          "modifiedVulnIntegrityImpact": "NOT_DEFINED",
          "privilegesRequired": "LOW",
          "providerUrgency": "NOT_DEFINED",
          "subAvailabilityImpact": "HIGH",
          "subConfidentialityImpact": "HIGH",
          "subIntegrityImpact": "HIGH",
          "userInteraction": "PASSIVE",
          "valueDensity": "NOT_DEFINED",
          "vectorString": "CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:P/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:Y/R:A/V:X/RE:X/U:X",
          "version": "4.0",
          "vulnAvailabilityImpact": "HIGH",
          "vulnConfidentialityImpact": "HIGH",
          "vulnIntegrityImpact": "HIGH",
          "vulnerabilityResponseEffort": "NOT_DEFINED"
        },
        "source": "cve-coordination@google.com",
        "type": "Secondary"
      }
    ]
  },
  "published": "2025-09-19T09:15:36.353",
  "references": [
    {
      "source": "cve-coordination@google.com",
      "tags": [
        "Issue Tracking"
      ],
      "url": "https://github.com/keras-team/keras/pull/21429"
    }
  ],
  "sourceIdentifier": "cve-coordination@google.com",
  "vulnStatus": "Analyzed",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-502"
        }
      ],
      "source": "cve-coordination@google.com",
      "type": "Secondary"
    }
  ]
}

MSRC_CVE-2025-9906

Vulnerability from csaf_microsoft - Published: 2025-09-02 00:00 - Updated: 2025-09-20 01:03

Summary

Arbitrary Code execution in Keras Safe Mode

Notes

Additional Resources

To determine the support lifecycle for your software, see the Microsoft Support Lifecycle: https://support.microsoft.com/lifecycle

Disclaimer

The information provided in the Microsoft Knowledge Base is provided \"as is\" without warranty of any kind. Microsoft disclaims all warranties, either express or implied, including the warranties of merchantability and fitness for a particular purpose. In no event shall Microsoft Corporation or its suppliers be liable for any damages whatsoever including direct, indirect, incidental, consequential, loss of business profits or special damages, even if Microsoft Corporation or its suppliers have been advised of the possibility of such damages. Some states do not allow the exclusion or limitation of liability for consequential or incidental damages so the foregoing limitation may not apply.

JSON

To clipboard

{
  "document": {
    "category": "csaf_vex",
    "csaf_version": "2.0",
    "distribution": {
      "text": "Public",
      "tlp": {
        "label": "WHITE",
        "url": "https://www.first.org/tlp/"
      }
    },
    "lang": "en-US",
    "notes": [
      {
        "category": "general",
        "text": "To determine the support lifecycle for your software, see the Microsoft Support Lifecycle: https://support.microsoft.com/lifecycle",
        "title": "Additional Resources"
      },
      {
        "category": "legal_disclaimer",
        "text": "The information provided in the Microsoft Knowledge Base is provided \\\"as is\\\" without warranty of any kind. Microsoft disclaims all warranties, either express or implied, including the warranties of merchantability and fitness for a particular purpose. In no event shall Microsoft Corporation or its suppliers be liable for any damages whatsoever including direct, indirect, incidental, consequential, loss of business profits or special damages, even if Microsoft Corporation or its suppliers have been advised of the possibility of such damages. Some states do not allow the exclusion or limitation of liability for consequential or incidental damages so the foregoing limitation may not apply.",
        "title": "Disclaimer"
      }
    ],
    "publisher": {
      "category": "vendor",
      "contact_details": "secure@microsoft.com",
      "name": "Microsoft Security Response Center",
      "namespace": "https://msrc.microsoft.com"
    },
    "references": [
      {
        "category": "self",
        "summary": "CVE-2025-9906 Arbitrary Code execution in Keras Safe Mode - VEX",
        "url": "https://msrc.microsoft.com/csaf/vex/2025/msrc_cve-2025-9906.json"
      },
      {
        "category": "external",
        "summary": "Microsoft Support Lifecycle",
        "url": "https://support.microsoft.com/lifecycle"
      },
      {
        "category": "external",
        "summary": "Common Vulnerability Scoring System",
        "url": "https://www.first.org/cvss"
      }
    ],
    "title": "Arbitrary Code execution in Keras Safe Mode",
    "tracking": {
      "current_release_date": "2025-09-20T01:03:50.000Z",
      "generator": {
        "date": "2025-10-20T03:48:57.169Z",
        "engine": {
          "name": "MSRC Generator",
          "version": "1.0"
        }
      },
      "id": "msrc_CVE-2025-9906",
      "initial_release_date": "2025-09-02T00:00:00.000Z",
      "revision_history": [
        {
          "date": "2025-09-20T01:03:50.000Z",
          "legacy_version": "1",
          "number": "1",
          "summary": "Information published."
        }
      ],
      "status": "final",
      "version": "1"
    }
  },
  "product_tree": {
    "branches": [
      {
        "branches": [
          {
            "branches": [
              {
                "category": "product_version",
                "name": "3.0",
                "product": {
                  "name": "Azure Linux 3.0",
                  "product_id": "17084"
                }
              }
            ],
            "category": "product_name",
            "name": "Azure Linux"
          },
          {
            "branches": [
              {
                "category": "product_version_range",
                "name": "\u003cazl3 keras 3.3.3-3",
                "product": {
                  "name": "\u003cazl3 keras 3.3.3-3",
                  "product_id": "1"
                }
              },
              {
                "category": "product_version",
                "name": "azl3 keras 3.3.3-3",
                "product": {
                  "name": "azl3 keras 3.3.3-3",
                  "product_id": "20434"
                }
              }
            ],
            "category": "product_name",
            "name": "keras"
          }
        ],
        "category": "vendor",
        "name": "Microsoft"
      }
    ],
    "relationships": [
      {
        "category": "default_component_of",
        "full_product_name": {
          "name": "\u003cazl3 keras 3.3.3-3 as a component of Azure Linux 3.0",
          "product_id": "17084-1"
        },
        "product_reference": "1",
        "relates_to_product_reference": "17084"
      },
      {
        "category": "default_component_of",
        "full_product_name": {
          "name": "azl3 keras 3.3.3-3 as a component of Azure Linux 3.0",
          "product_id": "20434-17084"
        },
        "product_reference": "20434",
        "relates_to_product_reference": "17084"
      }
    ]
  },
  "vulnerabilities": [
    {
      "cve": "CVE-2025-9906",
      "cwe": {
        "id": "CWE-502",
        "name": "Deserialization of Untrusted Data"
      },
      "notes": [
        {
          "category": "general",
          "text": "Google",
          "title": "Assigning CNA"
        }
      ],
      "product_status": {
        "fixed": [
          "20434-17084"
        ],
        "known_affected": [
          "17084-1"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "CVE-2025-9906 Arbitrary Code execution in Keras Safe Mode - VEX",
          "url": "https://msrc.microsoft.com/csaf/vex/2025/msrc_cve-2025-9906.json"
        }
      ],
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2025-09-20T01:03:50.000Z",
          "details": "3.3.3-4:Security Update:https://learn.microsoft.com/en-us/azure/azure-linux/tutorial-azure-linux-upgrade",
          "product_ids": [
            "17084-1"
          ],
          "url": "https://learn.microsoft.com/en-us/azure/azure-linux/tutorial-azure-linux-upgrade"
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 7.3,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "environmentalsScore": 0.0,
            "integrityImpact": "HIGH",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "temporalScore": 7.3,
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H",
            "version": "3.1"
          },
          "products": [
            "17084-1"
          ]
        }
      ],
      "title": "Arbitrary Code execution in Keras Safe Mode"
    }
  ]
}

GHSA-36FQ-JGMW-4R9C

Vulnerability from github – Published: 2025-09-19 09:31 – Updated: 2025-09-23 21:30

Summary

Keras is vulnerable to Deserialization of Untrusted Data

Details

Arbitrary Code Execution in Keras

Keras versions prior to 3.11.0 allow for arbitrary code execution when loading a crafted .keras model archive, even when safe_mode=True.

The issue arises because the archive’s config.json is parsed before layer deserialization. This can invoke keras.config.enable_unsafe_deserialization(), effectively disabling safe mode from within the loading process itself. An attacker can place this call first in the archive and then include a Lambda layer whose function is deserialized from a pickle, leading to the execution of attacker-controlled Python code as soon as a victim loads the model file.

Exploitation requires a user to open an untrusted model; no additional privileges are needed. The fix in version 3.11.0 enforces safe-mode semantics before reading any user-controlled configuration and prevents the toggling of unsafe deserialization via the config file.

Affected versions: < 3.11.0 Patched version: 3.11.0

It is recommended to upgrade to version 3.11.0 or later and to avoid opening untrusted model files.

Severity ?

7.3 (High)


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

8.7 (High)


                  
                    CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "keras"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "3.11.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2025-9906"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-502"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2025-09-19T17:16:44Z",
    "nvd_published_at": "2025-09-19T09:15:36Z",
    "severity": "HIGH"
  },
  "details": "### Arbitrary Code Execution in Keras\n\nKeras versions prior to 3.11.0 allow for arbitrary code execution when loading a crafted `.keras` model archive, even when `safe_mode=True`.\n\nThe issue arises because the archive\u2019s `config.json` is parsed before layer deserialization. This can invoke `keras.config.enable_unsafe_deserialization()`, effectively disabling safe mode from within the loading process itself. An attacker can place this call first in the archive and then include a `Lambda` layer whose function is deserialized from a pickle, leading to the execution of attacker-controlled Python code as soon as a victim loads the model file.\n\nExploitation requires a user to open an untrusted model; no additional privileges are needed. The fix in version 3.11.0 enforces safe-mode semantics *before* reading any user-controlled configuration and prevents the toggling of unsafe deserialization via the config file.\n\n**Affected versions:** \u003c 3.11.0\n**Patched version:** 3.11.0\n\nIt is recommended to upgrade to version 3.11.0 or later and to avoid opening untrusted model files.",
  "id": "GHSA-36fq-jgmw-4r9c",
  "modified": "2025-09-23T21:30:06Z",
  "published": "2025-09-19T09:31:14Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-9906"
    },
    {
      "type": "WEB",
      "url": "https://github.com/keras-team/keras/pull/21429"
    },
    {
      "type": "WEB",
      "url": "https://github.com/keras-team/keras/commit/713172ab56b864e59e2aa79b1a51b0e728bba858"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/keras-team/keras"
    },
    {
      "type": "WEB",
      "url": "https://github.com/keras-team/keras/releases/tag/v3.11.0"
    },
    {
      "type": "WEB",
      "url": "https://osv.dev/vulnerability/CVE-2025-9906"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Keras is vulnerable to Deserialization of Untrusted Data"
}

Sightings

Author	Source	Type	Date

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.

Detection rules are retrieved from Rulezet.

Action not permitted

CVE-2025-9906 (GCVE-0-2025-9906)

FKIE_CVE-2025-9906

MSRC_CVE-2025-9906

GHSA-36FQ-JGMW-4R9C

Arbitrary Code Execution in Keras

Tags

Sightings

Nomenclature