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through 2.5.0 allows attackers to overwrite arbitrary files via a crafted archive when tf.keras.utils.get_file is used with extract=True. NOTE: the vendor's position is that tf.keras.utils.get_file is not intended for untrusted archives","2021-06-30",9.1,"v3.1","Critical",0.01864,{"cveId":319,"description":320,"publishedDate":321,"cvssScore":322,"cvssVersion":315,"severity":316,"isKev":16,"epssScore":323},"CVE-2020-15202","In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read\u002Fwrite outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.","2020-09-25",9,0.01287,{"cveId":325,"description":326,"publishedDate":321,"cvssScore":322,"cvssVersion":315,"severity":316,"isKev":16,"epssScore":327},"CVE-2020-15207","In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the `DCHECK` does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and\u002For data corruption. The issue is patched in commit 2d88f470dea2671b430884260f3626b1fe99830a, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.",0.01243,{"cveId":329,"description":330,"publishedDate":331,"cvssScore":332,"cvssVersion":315,"severity":333,"isKev":16,"epssScore":334},"CVE-2022-23559","Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both `embedding_size` and `lookup_size` are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read\u002Fwrite. Users are advised to upgrade to a patched version.","2022-02-04",8.8,"High",0.01183,{"cveId":336,"description":337,"publishedDate":331,"cvssScore":338,"cvssVersion":315,"severity":339,"isKev":16,"epssScore":340},"CVE-2022-23581","Tensorflow is an Open Source Machine Learning Framework. The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a `SavedModel` such that `IsSimplifiableReshape` would trigger `CHECK` failures. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.",6.5,"Medium",0.0116,{"cveId":342,"description":343,"publishedDate":344,"cvssScore":345,"cvssVersion":315,"severity":333,"isKev":16,"epssScore":346},"CVE-2022-21728","Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `ReverseSequence` does not fully validate the value of `batch_dim` and can result in a heap OOB read. There is a check to make sure the value of `batch_dim` does not go over the rank of the input, but there is no check for negative values. Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then the implementation of `Dim` would access elements before the start of an array. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.","2022-02-03",8.1,0.01134,{"cveId":348,"description":349,"publishedDate":344,"cvssScore":338,"cvssVersion":315,"severity":339,"isKev":16,"epssScore":350},"CVE-2022-23567","Tensorflow is an Open Source Machine Learning Framework. The implementations of `Sparse*Cwise*` ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or `CHECK`-fails when building new `TensorShape` objects (so, assert failures based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.",0.01106,{"cveId":352,"description":353,"publishedDate":331,"cvssScore":338,"cvssVersion":315,"severity":339,"isKev":16,"epssScore":350},"CVE-2022-23589","Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a `SavedModel` file (fixing the first one would trigger the same dereference in the second place). First, during constant folding, the `GraphDef` might not have the required nodes for the binary operation. If a node is missing, the correposning `mul_*child` would be null, and the dereference in the subsequent line would be incorrect. We have a similar issue during `IsIdentityConsumingSwitch`. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.",{"cveId":355,"description":356,"publishedDate":321,"cvssScore":357,"cvssVersion":315,"severity":316,"isKev":16,"epssScore":358},"CVE-2020-15205","In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.",9.8,0.01029,{"cveId":360,"description":361,"publishedDate":321,"cvssScore":362,"cvssVersion":315,"severity":339,"isKev":16,"epssScore":363},"CVE-2020-15194","In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.\"",5.3,0.01026,[],{"title":366,"metaDescription":367,"keywords":368,"structuredData":369},"Tensorflow by Google - Security Vulnerabilities & Analysis | EdgeProtect","Comprehensive security analysis for Tensorflow by Google covering 94 versions with 429 tracked vulnerabilities. Real-time threat intelligence, CVE tracking, and security recommendations.","Tensorflow vulnerabilities, Tensorflow security, Tensorflow CVE, Google Tensorflow, Tensorflow threat intelligence, Google security, product security analysis, cybersecurity threats, vulnerability management, CVE tracking",{"@context":370,"@type":371,"name":372,"description":367,"url":373,"manufacturer":374,"applicationCategory":378,"softwareVersion":379,"mainEntity":380},"https:\u002F\u002Fschema.org","SoftwareApplication","Tensorflow","https:\u002F\u002Fedgeprotect.io\u002Fvulnerabilities\u002Ftechnologies\u002Fvendors\u002Fgoogle\u002Ftensorflow",{"@type":375,"name":376,"url":377},"Organization","Google","https:\u002F\u002Fedgeprotect.io\u002Fvendors\u002Fgoogle","Security Software","94 versions tracked",{"@type":381,"name":382,"description":383,"vulnerabilityCount":10,"dateModified":12},"SecurityVulnerability","Tensorflow Security Vulnerabilities","Security vulnerabilities affecting Tensorflow by Google",{"totalVersions":9,"totalVulnerabilities":10,"kevVulnerabilities":11,"lastUpdated":12,"generatedAt":385,"apiVersion":386},"2026-10-28T15:55:02.041Z","3.0",{"rows":388,"total":11,"capped":16,"failed":11},[],[]]