{"api_version":"1","generated_at":"2026-07-23T13:34:24+00:00","cve":"CVE-2021-29547","urls":{"html":"https://cve.report/CVE-2021-29547","api":"https://cve.report/api/cve/CVE-2021-29547.json","docs":"https://cve.report/api","cve_org":"https://www.cve.org/CVERecord?id=CVE-2021-29547","nvd":"https://nvd.nist.gov/vuln/detail/CVE-2021-29547"},"summary":{"title":"CVE-2021-29547","description":"TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.","state":"PUBLIC","assigner":"security-advisories@github.com","published_at":"2021-05-14 20:15:00","updated_at":"2021-07-27 17:25:00"},"problem_types":["CWE-125"],"metrics":[],"references":[{"url":"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj","name":"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj","refsource":"CONFIRM","tags":[],"title":"Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization` · Advisory · tensorflow/tensorflow · GitHub","mime":"text/html","httpstatus":"200","archivestatus":"404"},{"url":"https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b","name":"https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b","refsource":"MISC","tags":[],"title":"Add missing validation in `QuantizedBatchNormWithGlobalNormalization` · tensorflow/tensorflow@d6ed5bc · GitHub","mime":"text/html","httpstatus":"200","archivestatus":"503"},{"url":"https://www.cve.org/CVERecord?id=CVE-2021-29547","name":"CVE Program record","refsource":"CVE.ORG","tags":["canonical"]},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2021-29547","name":"NVD vulnerability detail","refsource":"NVD","tags":["canonical","analysis"]}],"affected":[],"timeline":[],"solutions":[],"workarounds":[],"exploits":[],"credits":[],"nvd_cpes":[{"cve_year":"2021","cve_id":"29547","vulnerable":"1","versionEndIncluding":"","cpe1":"cpe","cpe2":"2.3","cpe3":"a","cpe4":"google","cpe5":"tensorflow","cpe6":"*","cpe7":"*","cpe8":"*","cpe9":"*","cpe10":"*","cpe11":"*","cpe12":"*","cpe13":"*"},{"cve_year":"2021","cve_id":"29547","vulnerable":"1","versionEndIncluding":"2.1.4","cpe1":"cpe","cpe2":"2.3","cpe3":"a","cpe4":"google","cpe5":"tensorflow","cpe6":"*","cpe7":"*","cpe8":"*","cpe9":"*","cpe10":"*","cpe11":"*","cpe12":"*","cpe13":"*"},{"cve_year":"2021","cve_id":"29547","vulnerable":"1","versionEndIncluding":"2.2.3","cpe1":"cpe","cpe2":"2.3","cpe3":"a","cpe4":"google","cpe5":"tensorflow","cpe6":"*","cpe7":"*","cpe8":"*","cpe9":"*","cpe10":"*","cpe11":"*","cpe12":"*","cpe13":"*"},{"cve_year":"2021","cve_id":"29547","vulnerable":"1","versionEndIncluding":"2.3.3","cpe1":"cpe","cpe2":"2.3","cpe3":"a","cpe4":"google","cpe5":"tensorflow","cpe6":"*","cpe7":"*","cpe8":"*","cpe9":"*","cpe10":"*","cpe11":"*","cpe12":"*","cpe13":"*"},{"cve_year":"2021","cve_id":"29547","vulnerable":"1","versionEndIncluding":"2.4.2","cpe1":"cpe","cpe2":"2.3","cpe3":"a","cpe4":"google","cpe5":"tensorflow","cpe6":"*","cpe7":"*","cpe8":"*","cpe9":"*","cpe10":"*","cpe11":"*","cpe12":"*","cpe13":"*"}],"vendor_comments":[],"enrichments":{"kev":null,"epss":null,"legacy_qids":[{"cve":"CVE-2021-29547","qid":"982502","title":"Python (pip) Security Update for tensorflow-gpu (GHSA-4fg4-p75j-w5xj)"}]},"source_records":{"cve_program":{"CVE_data_meta":{"ASSIGNER":"security-advisories@github.com","ID":"CVE-2021-29547","STATE":"PUBLIC","TITLE":"Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization`"},"affects":{"vendor":{"vendor_data":[{"product":{"product_data":[{"product_name":"tensorflow","version":{"version_data":[{"version_value":"< 2.1.4"},{"version_value":">= 2.2.0, < 2.2.3"},{"version_value":">= 2.3.0, < 2.3.3"},{"version_value":">= 2.4.0, < 2.4.2"}]}}]},"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. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range."}]},"impact":{"cvss":{"attackComplexity":"HIGH","attackVector":"LOCAL","availabilityImpact":"LOW","baseScore":2.5,"baseSeverity":"LOW","confidentialityImpact":"NONE","integrityImpact":"NONE","privilegesRequired":"LOW","scope":"UNCHANGED","userInteraction":"NONE","vectorString":"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L","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/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b","refsource":"MISC","url":"https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b"},{"name":"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj","refsource":"CONFIRM","url":"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj"}]},"source":{"advisory":"GHSA-4fg4-p75j-w5xj","discovery":"UNKNOWN"}},"nvd":{"publishedDate":"2021-05-14 20:15:00","lastModifiedDate":"2021-07-27 17:25:00","problem_types":["CWE-125"],"metrics":{"baseMetricV3":{"cvssV3":{"version":"3.1","vectorString":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","attackVector":"LOCAL","attackComplexity":"LOW","privilegesRequired":"LOW","userInteraction":"NONE","scope":"UNCHANGED","confidentialityImpact":"NONE","integrityImpact":"NONE","availabilityImpact":"HIGH","baseScore":5.5,"baseSeverity":"MEDIUM"},"exploitabilityScore":1.8,"impactScore":3.6},"baseMetricV2":{"cvssV2":{"version":"2.0","vectorString":"AV:L/AC:L/Au:N/C:N/I:N/A:P","accessVector":"LOCAL","accessComplexity":"LOW","authentication":"NONE","confidentialityImpact":"NONE","integrityImpact":"NONE","availabilityImpact":"PARTIAL","baseScore":2.1},"severity":"LOW","exploitabilityScore":3.9,"impactScore":2.9,"acInsufInfo":false,"obtainAllPrivilege":false,"obtainUserPrivilege":false,"obtainOtherPrivilege":false,"userInteractionRequired":false}},"configurations":{"CVE_data_version":"4.0","nodes":[{"operator":"OR","children":[],"cpe_match":[{"vulnerable":true,"cpe23Uri":"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*","versionStartIncluding":"2.4.0","versionEndExcluding":"2.4.2","cpe_name":[]},{"vulnerable":true,"cpe23Uri":"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*","versionStartIncluding":"2.3.0","versionEndExcluding":"2.3.3","cpe_name":[]},{"vulnerable":true,"cpe23Uri":"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*","versionStartIncluding":"2.2.0","versionEndExcluding":"2.2.3","cpe_name":[]},{"vulnerable":true,"cpe23Uri":"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*","versionEndExcluding":"2.1.4","cpe_name":[]}]}]}},"legacy_mitre":{"record":{"CveYear":"2021","CveId":"29547","Ordinal":"204751","Title":"CVE-2021-29547","CVE":"CVE-2021-29547","Year":"2021"},"notes":[{"CveYear":"2021","CveId":"29547","Ordinal":"1","NoteData":"TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.","Type":"Description","Title":null},{"CveYear":"2021","CveId":"29547","Ordinal":"2","NoteData":"2021-05-14","Type":"Other","Title":"Published"},{"CveYear":"2021","CveId":"29547","Ordinal":"3","NoteData":"2021-05-14","Type":"Other","Title":"Modified"}]}}}