rainbutterfly/cve_corpus huggingface dataset:
CVE-2021-29600 Division by zero in TFLite's implementation of `OneHot`
TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `OneHot` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/f61c57bd425878be108ec787f4d96390579fb83e/tensorflow/lite/kernels/one_hot.cc#L68-L72). An attacker can craft a model such that at least one of the dimensions of `indices` would be 0. In turn, the `prefix_dim_size` value would become 0. 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.
Problem types: cwe_id: CWE-369
description: CWE-369: Divide By Zero
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: 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
Published: 2021-05-14T19:21:37.000Z
CVE-2021-29601 Integer overflow in TFLite concatentation
TensorFlow is an end-to-end open source platform for machine learning. The TFLite implementation of concatenation is vulnerable to an integer overflow issue(https://github.com/tensorflow/tensorflow/blob/7b7352a724b690b11bfaae2cd54bc3907daf6285/tensorflow/lite/kernels/concatenation.cc#L70-L76). An attacker can craft a model such that the dimensions of one of the concatenation input overflow the values of `int`. TFLite uses `int` to represent tensor dimensions, whereas TF uses `int64`. Hence, valid TF models can trigger an integer overflow when converted to TFLite format. 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.
Problem types: cwe_id: CWE-190
description: CWE-190: Integer Overflow or Wraparound
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: LOCAL
availabilityImpact: HIGH
baseScore: 6.3
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: HIGH
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:H
version: 3.1
Published: 2021-05-14T19:21:29.000Z
CVE-2021-29602 Division by zero in TFLite's implementation of `DepthwiseConv`
TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `DepthwiseConv` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/depthwise_conv.cc#L287-L288). An attacker can craft a model such that `input`'s fourth dimension would be 0. 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.
Problem types: cwe_id: CWE-369
description: CWE-369: Divide By Zero
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: 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
Published: 2021-05-14T19:21:25.000Z
CVE-2021-29603 Heap OOB write in TFLite
TensorFlow is an end-to-end open source platform for machine learning. A specially crafted TFLite model could trigger an OOB write on heap in the TFLite implementation of `ArgMin`/`ArgMax`(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/arg_min_max.cc#L52-L59). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the condition in the `if` is never true, so code writes past the last valid element of `output_dims->data`. 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.
Problem types: cwe_id: CWE-787
description: CWE-787: Out-of-bounds Write
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: 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
Published: 2021-05-14T19:21:20.000Z
CVE-2021-29604 Division by zero in TFLite's implementation of hashtable lookup
TensorFlow is an end-to-end open source platform for machine learning. The TFLite implementation of hashtable lookup is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/hashtable_lookup.cc#L114-L115) An attacker can craft a model such that `values`'s first dimension would be 0. 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.
Problem types: cwe_id: CWE-369
description: CWE-369: Divide By Zero
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: 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
Published: 2021-05-14T19:21:16.000Z
CVE-2021-29605 Integer overflow in TFLite memory allocation
TensorFlow is an end-to-end open source platform for machine learning. The TFLite code for allocating `TFLiteIntArray`s is vulnerable to an integer overflow issue(https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L24-L27). An attacker can craft a model such that the `size` multiplier is so large that the return value overflows the `int` datatype and becomes negative. In turn, this results in invalid value being given to `malloc`(https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L47-L52). In this case, `ret->size` would dereference an invalid pointer. 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.
Problem types: cwe_id: CWE-190
description: CWE-190: Integer Overflow or Wraparound
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: LOCAL
availabilityImpact: HIGH
baseScore: 7.1
baseSeverity: HIGH
confidentialityImpact: NONE
integrityImpact: HIGH
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H
version: 3.1
Published: 2021-05-14T19:21:12.000Z
CVE-2021-29606 Heap OOB read in TFLite
TensorFlow is an end-to-end open source platform for machine learning. A specially crafted TFLite model could trigger an OOB read on heap in the TFLite implementation of `Split_V`(https://github.com/tensorflow/tensorflow/blob/c59c37e7b2d563967da813fa50fe20b21f4da683/tensorflow/lite/kernels/split_v.cc#L99). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the `SizeOfDimension` function(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/kernel_util.h#L148-L150) will access data outside the bounds of the tensor shape array. 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.
Problem types: cwe_id: CWE-125
description: CWE-125: Out-of-bounds Read
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: LOCAL
availabilityImpact: HIGH
baseScore: 7.1
baseSeverity: HIGH
confidentialityImpact: NONE
integrityImpact: HIGH
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H
version: 3.1
Published: 2021-05-14T19:21:07.000Z
CVE-2021-29607 Incomplete validation in `SparseSparseMinimum`
TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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.
Problem types: cwe_id: CWE-754
description: CWE-754: Improper Check for Unusual or Exceptional Conditions
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: LOCAL
availabilityImpact: HIGH
baseScore: 5.3
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: LOW
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:H
version: 3.1
Published: 2021-05-14T19:21:03.000Z
CVE-2021-29608 Heap OOB and null pointer dereference in `RaggedTensorToTensor`
TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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.
Problem types: cwe_id: CWE-131
description: CWE-131: Incorrect Calculation of Buffer Size
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: LOCAL
availabilityImpact: HIGH
baseScore: 5.3
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: LOW
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:H
version: 3.1
Published: 2021-05-14T19:20:58.000Z
CVE-2021-29609 Incomplete validation in `SparseAdd`
TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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.
Problem types: cwe_id: CWE-665
description: CWE-665: Improper Initialization
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: LOCAL
availabilityImpact: HIGH
baseScore: 5.3
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: LOW
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:H
version: 3.1
Published: 2021-05-14T19:20:52.000Z
CVE-2021-29610 Invalid validation in `QuantizeAndDequantizeV2`
TensorFlow is an end-to-end open source platform for machine learning. The validation in `tf.raw_ops.QuantizeAndDequantizeV2` allows invalid values for `axis` argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses `||` to mix two different conditions. If `axis_ < -1` the condition in `OP_REQUIRES` will still be true, but this value of `axis_` results in heap underflow. This allows attackers to read/write to other data on the heap. 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.
Problem types: cwe_id: CWE-665
description: CWE-665: Improper Initialization
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: LOCAL
availabilityImpact: LOW
baseScore: 3.6
baseSeverity: LOW
confidentialityImpact: NONE
integrityImpact: LOW
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:L
version: 3.1
Published: 2021-05-14T19:20:47.000Z
CVE-2021-29611 Incomplete validation in `SparseReshape`
TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseReshape` results in a denial of service based on a `CHECK`-failure. The implementation(https://github.com/tensorflow/tensorflow/blob/e87b51ce05c3eb172065a6ea5f48415854223285/tensorflow/core/kernels/sparse_reshape_op.cc#L40) has no validation that the input arguments specify a valid sparse tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are the only affected versions.
Problem types: cwe_id: CWE-665
description: CWE-665: Improper Initialization
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: LOCAL
availabilityImpact: LOW
baseScore: 3.6
baseSeverity: LOW
confidentialityImpact: NONE
integrityImpact: LOW
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:L
version: 3.1
Published: 2021-05-14T19:20:43.000Z
CVE-2021-29612 Heap buffer overflow in `BandedTriangularSolve`
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`. The implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty. Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen in this op's implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. 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.
Problem types: cwe_id: CWE-120
description: CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: LOCAL
availabilityImpact: LOW
baseScore: 3.6
baseSeverity: LOW
confidentialityImpact: NONE
integrityImpact: LOW
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:L
version: 3.1
Published: 2021-05-14T19:20:38.000Z
CVE-2021-29613 Incomplete validation in `tf.raw_ops.CTCLoss`
TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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.
Problem types: cwe_id: CWE-665
description: CWE-665: Improper Initialization
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: HIGH
attackVector: LOCAL
availabilityImpact: HIGH
baseScore: 6.3
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: HIGH
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:H
version: 3.1
Published: 2021-05-14T19:20:33.000Z
CVE-2021-29614 Interpreter crash from `tf.io.decode_raw`
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the `fixed_length` value to the size of the type argument. The `fixed_length` argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the `out_data` pointer by `fixed_length * sizeof(T)` bytes whereas it only copied at most `fixed_length` bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory. 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.
Problem types: cwe_id: CWE-665
description: CWE-665: Improper Initialization
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: LOCAL
availabilityImpact: HIGH
baseScore: 7.1
baseSeverity: HIGH
confidentialityImpact: NONE
integrityImpact: HIGH
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H
version: 3.1
Published: 2021-05-14T19:20:28.000Z
CVE-2021-29615 Stack overflow in `ParseAttrValue` with nested tensors
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `ParseAttrValue`(https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/framework/attr_value_util.cc#L397-L453) can be tricked into stack overflow due to recursion by giving in a specially crafted input. 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.
Problem types: cwe_id: CWE-674
description: CWE-674: Uncontrolled Recursion
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: 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
Published: 2021-05-14T19:25:31.000Z
CVE-2021-29616 Null dereference in Grappler's `TrySimplify`
TensorFlow is an end-to-end open source platform for machine learning. The implementation of TrySimplify(https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that result in optimizing a node with no inputs. 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.
Problem types: cwe_id: CWE-476
description: CWE-476: NULL Pointer Dereference
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: 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
Published: 2021-05-14T19:25:27.000Z
CVE-2021-29617 Crash in `tf.strings.substr` due to `CHECK`-fail
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via `CHECK`-fail in `tf.strings.substr` with invalid arguments. 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.
Problem types: cwe_id: CWE-755
description: CWE-755: Improper Handling of Exceptional Conditions
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: 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
Published: 2021-05-14T19:25:23.000Z