{"containers": {"cna": {"affected": [{"product": "tensorflow", "vendor": "tensorflow", "versions": [{"status": "affected", "version": ">= 2.5.0, < 2.5.1"}, {"status": "affected", "version": ">= 2.4.0, < 2.4.3"}, {"status": "affected", "version": "< 2.3.4"}]}], "descriptions": [{"lang": "en", "value": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range."}], "metrics": [{"cvssV3_1": {"attackComplexity": "LOW", "attackVector": "LOCAL", "availabilityImpact": "NONE", "baseScore": 7.1, "baseSeverity": "HIGH", "confidentialityImpact": "HIGH", "integrityImpact": "HIGH", "privilegesRequired": "LOW", "scope": "UNCHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N", "version": "3.1"}}], "problemTypes": [{"descriptions": [{"cweId": "CWE-125", "description": "CWE-125: Out-of-bounds Read", "lang": "en", "type": "CWE"}]}], "providerMetadata": {"dateUpdated": "2021-08-12T22:20:16", "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa", "shortName": "GitHub_M"}, "references": [{"tags": ["x_refsource_CONFIRM"], "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp"}, {"tags": ["x_refsource_MISC"], "url": "https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12"}], "source": {"advisory": "GHSA-g8wg-cjwc-xhhp", "discovery": "UNKNOWN"}, "title": "Heap OOB in nested `tf.map_fn` with `RaggedTensor`s in TensorFlow", "x_legacyV4Record": {"CVE_data_meta": {"ASSIGNER": "security-advisories@github.com", "ID": "CVE-2021-37679", "STATE": "PUBLIC", "TITLE": "Heap OOB in nested `tf.map_fn` with `RaggedTensor`s in TensorFlow"}, "affects": {"vendor": {"vendor_data": [{"product": {"product_data": [{"product_name": "tensorflow", "version": {"version_data": [{"version_value": ">= 2.5.0, < 2.5.1"}, {"version_value": ">= 2.4.0, < 2.4.3"}, {"version_value": "< 2.3.4"}]}}]}, "vendor_name": "tensorflow"}]}}, "data_format": "MITRE", "data_type": "CVE", "data_version": "4.0", "description": {"description_data": [{"lang": "eng", "value": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range."}]}, "impact": {"cvss": {"attackComplexity": "LOW", "attackVector": "LOCAL", "availabilityImpact": "NONE", "baseScore": 7.1, "baseSeverity": "HIGH", "confidentialityImpact": "HIGH", "integrityImpact": "HIGH", "privilegesRequired": "LOW", "scope": "UNCHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N", "version": "3.1"}}, "problemtype": {"problemtype_data": [{"description": [{"lang": "eng", "value": "CWE-125: Out-of-bounds Read"}]}]}, "references": {"reference_data": [{"name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp", "refsource": "CONFIRM", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp"}, {"name": "https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12", "refsource": "MISC", "url": "https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12"}]}, "source": {"advisory": "GHSA-g8wg-cjwc-xhhp", "discovery": "UNKNOWN"}}}}, "cveMetadata": {"assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa", "assignerShortName": "GitHub_M", "cveId": "CVE-2021-37679", "datePublished": "2021-08-12T22:20:16", "dateReserved": "2021-07-29T00:00:00", "dateUpdated": "2021-08-12T22:20:16", "state": "PUBLISHED"}, "dataType": "CVE_RECORD", "dataVersion": "5.0"}

{"configurations": [{"nodes": [{"cpeMatch": [{"criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*", "matchCriteriaId": "0F83C081-51CC-415F-A8C0-0A44C75E2CD6", "versionEndExcluding": "2.3.4", "versionStartIncluding": "2.3.0", "vulnerable": true}, {"criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*", "matchCriteriaId": "BD3F2BF8-EBA9-42BF-8F9B-D918B880B15A", "versionEndExcluding": "2.4.3", "versionStartIncluding": "2.4.0", "vulnerable": true}, {"criteria": "cpe:2.3:a:google:tensorflow:2.5.0:*:*:*:*:*:*:*", "matchCriteriaId": "D03E99A7-4E3D-427D-A156-C0713E9FB02A", "vulnerable": true}, {"criteria": "cpe:2.3:a:google:tensorflow:2.6.0:rc0:*:*:*:*:*:*", "matchCriteriaId": "70FA6E48-6C57-40CA-809F-4E3D07CBF348", "vulnerable": true}, {"criteria": "cpe:2.3:a:google:tensorflow:2.6.0:rc1:*:*:*:*:*:*", "matchCriteriaId": "42187561-E491-434D-828C-F36701446634", "vulnerable": true}, {"criteria": "cpe:2.3:a:google:tensorflow:2.6.0:rc2:*:*:*:*:*:*", "matchCriteriaId": "C66B61C8-450A-4C5E-9174-F970D6DEE778", "vulnerable": true}], "negate": false, "operator": "OR"}]}], "descriptions": [{"lang": "en", "value": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range."}, {"lang": "es", "value": "TensorFlow es una plataforma de c\u00f3digo abierto de extremo a extremo para el aprendizaje autom\u00e1tico.&#xa0;En las versiones afectadas, es posible anidar un \"tf.map_fn\" dentro de otra llamada \"tf.map_fn\".&#xa0;Sin embargo, si el tensor de entrada es un \"RaggedTensor\" y no se proporciona una firma de funci\u00f3n, el c\u00f3digo asume que la salida es un tensor completamente especificado y llena el b\u00fafer de salida con contenido no inicializado de la pila.&#xa0;Las salidas \"t\" y\" z\" deben ser id\u00e9nticas, sin embargo, este no es el caso.&#xa0;La \u00faltima fila de \"t\" contiene datos de la pila que se pueden usar para filtrar otra informaci\u00f3n de la memoria.&#xa0;El error radica en la conversi\u00f3n de un tensor \"Variant\" a un\" RaggedTensor\".&#xa0;La [implementaci\u00f3n] (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.&#xa0;cc # L177-L190) no comprueba que todas las formas internas coincidan y esto da como resultado las dimensiones adicionales.&#xa0;La misma implementaci\u00f3n puede resultar en la p\u00e9rdida de datos, si se modifica el tensor de entrada.&#xa0;Hemos solucionado el problema en GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12.&#xa0;La correcci\u00f3n ser\u00e1 incluida en TensorFlow versi\u00f3n 2.6.0.&#xa0;Tambi\u00e9n seleccionaremos este commit en TensorFlow versi\u00f3n 2.5.1, TensorFlow versi\u00f3n 2.4.3 y TensorFlow versi\u00f3n 2.3.4, ya que estos tambi\u00e9n est\u00e1n afectados y a\u00fan se encuentran en el rango admitido."}], "id": "CVE-2021-37679", "lastModified": "2021-08-19T13:55:26.060", "metrics": {"cvssMetricV2": [{"acInsufInfo": false, "baseSeverity": "MEDIUM", "cvssData": {"accessComplexity": "LOW", "accessVector": "LOCAL", "authentication": "NONE", "availabilityImpact": "PARTIAL", "baseScore": 4.6, "confidentialityImpact": "PARTIAL", "integrityImpact": "PARTIAL", "vectorString": "AV:L/AC:L/Au:N/C:P/I:P/A:P", "version": "2.0"}, "exploitabilityScore": 3.9, "impactScore": 6.4, "obtainAllPrivilege": false, "obtainOtherPrivilege": false, "obtainUserPrivilege": false, "source": "nvd@nist.gov", "type": "Primary", "userInteractionRequired": false}], "cvssMetricV31": [{"cvssData": {"attackComplexity": "LOW", "attackVector": "LOCAL", "availabilityImpact": "HIGH", "baseScore": 7.8, "baseSeverity": "HIGH", "confidentialityImpact": "HIGH", "integrityImpact": "HIGH", "privilegesRequired": "LOW", "scope": "UNCHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H", "version": "3.1"}, "exploitabilityScore": 1.8, "impactScore": 5.9, "source": "nvd@nist.gov", "type": "Primary"}, {"cvssData": {"attackComplexity": "LOW", "attackVector": "LOCAL", "availabilityImpact": "NONE", "baseScore": 7.1, "baseSeverity": "HIGH", "confidentialityImpact": "HIGH", "integrityImpact": "HIGH", "privilegesRequired": "LOW", "scope": "UNCHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N", "version": "3.1"}, "exploitabilityScore": 1.8, "impactScore": 5.2, "source": "security-advisories@github.com", "type": "Secondary"}]}, "published": "2021-08-12T23:15:08.287", "references": [{"source": "security-advisories@github.com", "tags": ["Patch", "Third Party Advisory"], "url": "https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12"}, {"source": "security-advisories@github.com", "tags": ["Third Party Advisory"], "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp"}], "sourceIdentifier": "security-advisories@github.com", "vulnStatus": "Analyzed", "weaknesses": [{"description": [{"lang": "en", "value": "CWE-681"}], "source": "nvd@nist.gov", "type": "Primary"}, {"description": [{"lang": "en", "value": "CWE-125"}], "source": "security-advisories@github.com", "type": "Secondary"}]}

{}