Archive
25 September 2020
Vulnerabilities first published on this day (UTC), newest first.
112 results · Feed
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High 8.1
Out of bounds access in tensorflow-lite
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_i…
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Medium 4.0
Denial of service in tensorflow-lite
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine …
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High 8.1
Out of bounds write in tensorflow-lite
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor…
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Medium 4.8
Heap buffer overflow in Tensorflow
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning …
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Critical 9.0
Integer truncation in Shard API usage
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 taki…
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High 7.5
Denial of Service in Tensorflow
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is…
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Medium 5.3
Segfault in Tensorflow
In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling `tf.raw_ops.GetSessionHandle` or `tf.raw_ops.GetSessionHandleV2` results in a null pointer dereference In linked snippet, in eager m…
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Critical 9.0
Data leak in Tensorflow
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…
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Critical 9.0
Denial of Service in Tensorflow
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of ser…
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High 8.7
Segfault and data corruption in tensorflow-lite
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 …
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High 7.4
Data corruption in tensorflow-lite
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of t…
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Medium 5.9
Null pointer dereference in tensorflow-lite
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to conver…
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Medium 6.5
Segmentation fault in tensorflow-lite
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We …
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Medium 4.8
Out of bounds access in tensorflow-lite
In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tens…
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Medium 5.3
Undefined behavior in Tensorflow
In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to `dlpack.to_dlpack` the expected validations will cause variables to bind to `nullptr` while setting a `status` variable to the error condition. However, this `status` argum…
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Medium 4.3
Memory leak in Tensorflow
In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to `dlpack.to_dlpack` there is a memory leak following an expected validation failure. The issue occurs because the `status` argument during validation failures is not properly …
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High 7.1
Memory corruption in Tensorflow
In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, ther…
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Medium 5.3
Denial of Service in Tensorflow
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, o…
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High 8.5
Heap buffer overflow in Tensorflow
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern. It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in…
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High 8.5
Heap buffer overflow in Tensorflow
In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully speci…
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Medium 6.3
Denial of Service in Tensorflow
In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix be…
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Medium 5.4
Heap buffer overflow in Tensorflow
In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has the same shape as the `values` one. The…
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Medium 5.9
Denial of Service in Tensorflow
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the `splits` tensor has the minimum required number of elements. Code uses this…
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Medium 5.9
Segfault in Tensorflow
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning …
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Medium 5.3
Segfault in Tensorflow
In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `tf.raw_ops.Switch` operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the ot…
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Unscored —
An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to directory tra…
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Unscored —
An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to Cross-Site Sc…
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Unscored —
An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to SQL Injection…
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Unscored —
An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to Cross-Site Sc…
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Unscored —
An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to directory tra…
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Unscored —
An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to directory tra…
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Unscored —
GE Reason S20 Ethernet Switch
The affected Reason S20 Ethernet Switch is vulnerable to cross-site scripting (XSS), which may allow an attacker to trick application users into performing critical application actions that include, but are not limited to, adding and updating accounts.
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Unscored —
An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to SQL Injection…
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Unscored —
An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable if any links and…
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Unscored —
An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to Cross-Site Sc…
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Medium 6.1
IBM InfoSphere Information Server 11.7 could allow a remote attacker to hijack the clicking action of the victim. By pe…
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Medium 5.3
IBM Business Automation Workflow 18.0, 19.0, and 20.0 and IBM Business Process Manager 8.0, 8.5, and 8.6 could allow a …
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Unscored —
An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to Cross-Site Sc…
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Unscored —
An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to Cross-Site Sc…
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Unscored —
An issue was discovered in Observium Professional, Enterprise & Community 20.8.10631. It is vulnerable to Cross-Site Sc…