CVE-2025-9906 (GCVE-0-2025-9906)
Vulnerability from cvelistv5
Published
2025-09-19 08:15
Modified
2025-09-20 03:55
Severity ?
VLAI Severity ?
EPSS score ?
CWE
- CWE-502 - Deserialization of Untrusted Data
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.
References
▼ | URL | Tags | |
---|---|---|---|
cve-coordination@google.com | https://github.com/keras-team/keras/pull/21429 | Issue Tracking |
Impacted products
Vendor | Product | Version | ||
---|---|---|---|---|
Keras-team | Keras |
Version: 3.0.0 ≤ |
{ "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" } } }
Loading…
Loading…
Sightings
Author | Source | Type | Date |
---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
- Confirmed: The vulnerability is confirmed from an analyst perspective.
- Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
- Patched: This vulnerability was successfully patched by the user reporting the sighting.
- Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
- Not confirmed: The user expresses doubt about the veracity of the vulnerability.
- Not patched: This vulnerability was not successfully patched by the user reporting the sighting.
Loading…