{"id":"CVE-2020-26271","aliases":["BIT-tensorflow-2020-26271","EUVD-2020-0218","GHSA-q263-fvxm-m5mw","PYSEC-2020-257","PYSEC-2020-302","PYSEC-2020-337","openSUSE-SU-2022:10014-1"],"status":"published","title":"Heap out of bounds access in MakeEdge in TensorFlow","description":"In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.","severity":{"version":"3.1","score":4.4,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L","level":"medium","assessor":"cna","from":"cve"},"epss":{"day":"2026-11-11","score":0.00216,"percentile":0.12162,"model":"v2026.06.15"},"facts":{"in_kev":false,"known_exploited":false,"public_exploit":false,"fix_available":true},"cwes":["CWE-125","CWE-908"],"published":"2020-12-10T19:07:34Z","modified":"2026-10-20T14:10:56Z","fixes":[{"ecosystem":"PyPI","package":"tensorflow","fixed_in":["1.15.5","2.0.4","2.1.3","2.2.2","2.3.2"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-cpu","fixed_in":["1.15.5","2.0.4","2.1.3","2.2.2","2.3.2"],"sources":["ghsa","osv"]},{"ecosystem":"PyPI","package":"tensorflow-gpu","fixed_in":["1.15.5","2.0.4","2.1.3","2.2.2","2.3.2"],"sources":["ghsa","osv"]},{"ecosystem":"Bitnami","package":"tensorflow","fixed_in":["1.15.5","2.0.4","2.1.3","2.2.2","2.3.2"],"sources":["osv"]}],"affected":[{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"1.15.5"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.0.0"},{"fixed":"2.0.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.1.0"},{"fixed":"2.1.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.2.0"},{"fixed":"2.2.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"1.15.5"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.0.0"},{"fixed":"2.0.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.1.0"},{"fixed":"2.1.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.2.0"},{"fixed":"2.2.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"1.15.5"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.0.0"},{"fixed":"2.0.4"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.1.0"},{"fixed":"2.1.3"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.2.0"},{"fixed":"2.2.2"}]}]},{"source":"ghsa","ecosystem":"PyPI","package":"tensorflow-gpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.2"}]}]},{"source":"osv","ecosystem":"Bitnami","package":"tensorflow","ranges":[{"type":"SEMVER","events":[{"introduced":"0"},{"fixed":"1.15.5"},{"introduced":"2.0.0"},{"fixed":"2.0.4"},{"introduced":"2.1.0"},{"fixed":"2.1.3"},{"introduced":"2.2.0"},{"fixed":"2.2.2"},{"introduced":"2.3.0"},{"fixed":"2.3.2"}]}]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"1.15.5"}]}],"versions":["0.12.0","0.12.1","1.0.0","1.0.1","1.1.0","1.10.0","1.10.1","1.11.0","1.12.0","1.12.2","1.12.3","1.13.1","1.13.2","1.14.0","1.15.0","1.15.2","1.15.3","1.15.4","1.2.0","1.2.1","1.3.0","1.4.0","1.4.1","1.5.0","1.5.1","1.6.0","1.7.0","1.7.1","1.8.0","1.9.0"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.0.0"},{"fixed":"2.0.4"}]}],"versions":["2.0.0","2.0.1","2.0.2","2.0.3"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.1.0"},{"fixed":"2.1.3"}]}],"versions":["2.1.0","2.1.1","2.1.2"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.2.0"},{"fixed":"2.2.2"}]}],"versions":["2.2.0","2.2.1"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.3.0"},{"fixed":"2.3.2"}]}],"versions":["2.3.0","2.3.1"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"1.15.5"}]}],"versions":["1.15.0"]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.0.0"},{"fixed":"2.0.4"}]}]},{"source":"osv","ecosystem":"PyPI","package":"tensorflow-cpu","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"2.1.0"}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Advisory","advisory","fix","web"]},{"url":"https://nvd.nist.gov/vuln/detail/CVE-2020-26271","tags":["web","advisory"]},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-302.yaml","tags":["web"]},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-337.yaml","tags":["web"]},{"url":"https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-257.yaml","tags":["web"]},{"url":"https://github.com/advisories/GHSA-q263-fvxm-m5mw"},{"url":"https://lists.opensuse.org/archives/list/security-announce@lists.opensuse.org/thread/U25ZU2T5T4LOLSIYIMGC5PLU4PQQMJE5/","tags":["advisory"]},{"url":"https://bugzilla.suse.com/1173128","tags":["report"]},{"url":"https://bugzilla.suse.com/1173314","tags":["report"]},{"url":"https://bugzilla.suse.com/1178287","tags":["report"]},{"url":"https://bugzilla.suse.com/1178564","tags":["report"]},{"url":"https://bugzilla.suse.com/1179455","tags":["report"]},{"url":"https://bugzilla.suse.com/1181864","tags":["report"]},{"url":"https://bugzilla.suse.com/1186860","tags":["report"]},{"url":"https://bugzilla.suse.com/1189423","tags":["report"]},{"url":"https://www.suse.com/security/cve/CVE-2020-26266","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-26267","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-26268","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-26270","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2020-26271","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37635","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37636","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37637","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37638","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37639","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37640","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37641","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37642","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37643","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37644","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37645","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37646","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37647","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37648","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37649","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37650","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37651","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37652","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37653","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37654","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37655","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37656","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37657","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37658","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37659","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37660","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37661","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37662","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37663","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37664","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37665","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37666","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37667","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37668","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37669","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37670","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37671","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37672","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37673","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37674","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37675","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37676","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37677","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37678","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37679","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-37680","tags":["web"]},{"url":"https://www.suse.com/security/cve/CVE-2021-3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out of bounds access in MakeEdge in TensorFlow","description":"In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.","status":"published","published":"2020-12-10T22:10:14Z","modified":"2024-08-04T15:56:04.342Z","cvss":[{"version":"3.1","score":4.4,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L","level":"medium","assessor":"cna"}],"cwes":["CWE-125"],"updated_at":"2026-11-02T14:35:46.129376Z"}],"euvd":[{"id":"EUVD-2020-0218","description":"In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.","status":"published","published":"2020-12-10T22:10:14Z","modified":"2024-08-04T15:56:04Z","cvss":[{"version":"3.1","score":4.4,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L","level":"medium"}],"updated_at":"2026-11-03T03:07:15.15938Z"}],"ghsa":[{"id":"GHSA-q263-fvxm-m5mw","title":"Heap out of bounds access in MakeEdge in TensorFlow","description":"### Impact\nUnder certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The [`MakeEdge` function](https://github.com/tensorflow/tensorflow/blob/3616708cb866365301d8e67b43b32b46d94b08a0/tensorflow/core/common_runtime/graph_constructor.cc#L1426-L1438) creates an edge between one output tensor of the `src` node (given by `output_index`) and the input slot of the `dst` node (given by `input_index`). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding `DataType` values and comparing these for equality:\n\n```cc\n  DataType src_out = src-\u003eoutput_type(output_index);\n  DataType dst_in = dst-\u003einput_type(input_index);\n  //...\n```\n\nHowever, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays.\n\nIn most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.\n\n### Patches\nWe have patched the issue in GitHub commit [0cc38aaa4064fd9e79101994ce9872c6d91f816b](https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.\n\nSince this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.","status":"published","published":"2020-12-10T19:07:34Z","modified":"2024-10-30T21:25:06Z","cvss":[{"version":"4.0","score":4.8,"vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N","level":"medium"},{"version":"3.1","score":4.4,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L","level":"medium"}],"cwes":["CWE-125","CWE-908"],"updated_at":"2026-11-02T14:38:14.769863Z"}],"nvd":[{"id":"CVE-2020-26271","description":"In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.","status":"published","published":"2020-12-10T22:15:12.077Z","modified":"2026-06-17T03:07:58.14Z","cvss":[{"version":"3.1","score":4.4,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L","level":"medium","assessor":"secondary"},{"version":"3.1","score":3.3,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N","level":"low","assessor":"primary"},{"version":"2.0","score":2.1,"vector":"AV:L/AC:L/Au:N/C:P/I:N/A:N","level":"low","assessor":"primary"}],"cwes":["CWE-125","CWE-908"],"updated_at":"2026-11-02T14:06:27.10541Z"}],"osv":[{"id":"CVE-2020-26271","description":"In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.","status":"published","published":"2020-12-10T22:15:12.077Z","modified":"2026-10-20T14:10:56.245848833Z","cvss":[{"version":"3.1","score":3.3,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N","level":"low"}],"updated_at":"2026-11-02T15:44:16.248747Z"},{"id":"GHSA-q263-fvxm-m5mw","title":"Heap out of bounds access in MakeEdge in TensorFlow","description":"### Impact\nUnder certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The [`MakeEdge` function](https://github.com/tensorflow/tensorflow/blob/3616708cb866365301d8e67b43b32b46d94b08a0/tensorflow/core/common_runtime/graph_constructor.cc#L1426-L1438) creates an edge between one output tensor of the `src` node (given by `output_index`) and the input slot of the `dst` node (given by `input_index`). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding `DataType` values and comparing these for equality:\n\n```cc\n  DataType src_out = src-\u003eoutput_type(output_index);\n  DataType dst_in = dst-\u003einput_type(input_index);\n  //...\n```\n\nHowever, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays.\n\nIn most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.\n\n### Patches\nWe have patched the issue in GitHub commit [0cc38aaa4064fd9e79101994ce9872c6d91f816b](https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.\n\nSince this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.","status":"published","published":"2020-12-10T19:07:34Z","modified":"2026-07-08T06:00:06.054336351Z","cvss":[{"version":"3.1","score":4.4,"vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L","level":"medium"},{"version":"4.0","score":4.8,"vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N","level":"medium"}],"cwes":["CWE-125","CWE-908"],"updated_at":"2026-11-02T15:46:53.700556Z"},{"id":"BIT-tensorflow-2020-26271","title":"Heap out of bounds access in MakeEdge in TensorFlow","description":"In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.","status":"published","published":"2024-03-06T11:20:12.357Z","modified":"2026-09-10T16:01:47.572982084Z","updated_at":"2026-11-02T15:28:35.51001Z"},{"id":"openSUSE-SU-2022:10014-1","title":"Security update for tensorflow2","description":"\nThis update for tensorflow fixes the following issues:\n\n\nUpdate to TF2 2.6.0 which fixes multiple CVEs (boo#1189423).\n\n- Introduction of bazel6.3 and basel-skylib1.0.3 as build\n  dependencies.\n  The latter has been adapted to all a version in its package\n  name (if %set_ver_suffix is set to 1). This allows multiple\n  versions to exist for one product (not installed).\n  NOTE: basel-skylib1.0.3 does not exist in oS:Factory:\n  basel-skylib in oS:Factory - the base version - is 1.0.3.\n","status":"published","published":"2022-06-18T16:01:26Z","modified":"2026-02-04T02:20:34.480131Z","updated_at":"2026-11-02T16:58:32.653274Z"},{"id":"PYSEC-2020-257","description":"In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.","status":"published","published":"2020-12-10T22:15:00Z","modified":"2023-12-06T01:00:27.928692Z","updated_at":"2026-11-02T15:52:52.192131Z"},{"id":"PYSEC-2020-302","description":"In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.","status":"published","published":"2020-12-10T22:15:00Z","modified":"2023-12-06T01:00:27.928692Z","updated_at":"2026-11-02T15:52:52.682031Z"},{"id":"PYSEC-2020-337","description":"In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.","status":"published","published":"2020-12-10T22:15:00Z","modified":"2023-12-06T01:00:27.928692Z","updated_at":"2026-11-02T15:52:52.682031Z"}]}}