TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.ReverseSequence` allows for stack overflow and/or `CHECK`-fail based denial of service. The implementation(https://github.com/tensorflow/tensorflow/blob/5b3b071975e01f0d250c928b2a8f901cd53b90a7/tensorflow/core/kernels/reverse_sequence_op.cc#L114-L118) fails to validate that `seq_dim` and `batch_dim` arguments are valid. Negative values for `seq_dim` can result in stack overflow or `CHECK`-failure, depending on the version of Eigen code used to implement the operation. Similar behavior can be exhibited by invalid values of `batch_dim`. 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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. 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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid. Whereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access. 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.

A vulnerability classified as problematic has been found in Radare2 5.9.9. Affected is the function r_cons_pal_init in the library /libr/cons/pal.c of the component radiff2. The manipulation leads to memory corruption. The attack needs to be approached locally. The complexity of an attack is rather high. The exploitability is told to be difficult. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The patch is identified as 5705d99cc1f23f36f9a84aab26d1724010b97798. It is recommended to apply a patch to fix this issue. The documentation explains that the parameter -T is experimental and "crashy". Further analysis has shown "the race is not a real problem unless you use asan". A new warning has been added.

A vulnerability was found in Radare2 5.9.9. It has been rated as problematic. This issue affects the function r_cons_is_breaked in the library /libr/cons/cons.c of the component radiff2. The manipulation of the argument -T leads to memory corruption. It is possible to launch the attack on the local host. The complexity of an attack is rather high. The exploitation is known to be difficult. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The identifier of the patch is 5705d99cc1f23f36f9a84aab26d1724010b97798. It is recommended to apply a patch to fix this issue. The documentation explains that the parameter -T is experimental and "crashy". Further analysis has shown "the race is not a real problem unless you use asan". An additional warning regarding threading support has been added.

A vulnerability, which was classified as problematic, has been found in Radare2 5.9.9. Affected by this issue is the function r_cons_flush in the library /libr/cons/cons.c of the component radiff2. The manipulation of the argument -T leads to use after free. Local access is required to approach this attack. The complexity of an attack is rather high. The exploitation is known to be difficult. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The name of the patch is 5705d99cc1f23f36f9a84aab26d1724010b97798. It is recommended to apply a patch to fix this issue. The documentation explains that the parameter -T is experimental and "crashy". Further analysis has shown "the race is not a real problem unless you use asan". A new warning has been added.

A vulnerability, which was classified as problematic, was found in Radare2 5.9.9. This affects the function r_cons_pal_init in the library /libr/cons/pal.c of the component radiff2. The manipulation of the argument -T leads to memory corruption. Attacking locally is a requirement. The complexity of an attack is rather high. The exploitability is told to be difficult. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The identifier of the patch is 5705d99cc1f23f36f9a84aab26d1724010b97798. It is recommended to apply a patch to fix this issue. The documentation explains that the parameter -T is experimental and "crashy". Further analysis has shown "the race is not a real problem unless you use asan". A new warning has been added.

A vulnerability has been found in Radare2 5.9.9 and classified as problematic. This vulnerability affects the function r_cons_rainbow_free in the library /libr/cons/pal.c of the component radiff2. The manipulation of the argument -T leads to memory corruption. It is possible to launch the attack on the local host. The complexity of an attack is rather high. The exploitation appears to be difficult. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The patch is identified as 5705d99cc1f23f36f9a84aab26d1724010b97798. It is recommended to apply a patch to fix this issue. The documentation explains that the parameter -T is experimental and "crashy". Further analysis has shown "the race is not a real problem unless you use asan". A new warning has been added.

A vulnerability classified as problematic was found in Radare2 5.9.9. Affected by this vulnerability is the function cons_stack_load in the library /libr/cons/cons.c of the component radiff2. The manipulation of the argument -T leads to memory corruption. An attack has to be approached locally. The complexity of an attack is rather high. The exploitation appears to be difficult. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The patch is named 5705d99cc1f23f36f9a84aab26d1724010b97798. It is recommended to apply a patch to fix this issue. The documentation explains that the parameter -T is experimental and "crashy". Further analysis has shown "the race is not a real problem unless you use asan". A new warning has been added.

A vulnerability was found in Radare2 5.9.9 and classified as problematic. This issue affects the function r_cons_context_break_pop in the library /libr/cons/cons.c of the component radiff2. The manipulation of the argument -T leads to memory corruption. The attack needs to be approached locally. The complexity of an attack is rather high. The exploitation is known to be difficult. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The patch is named 5705d99cc1f23f36f9a84aab26d1724010b97798. It is recommended to apply a patch to fix this issue. The documentation explains that the parameter -T is experimental and "crashy". Further analysis has shown "the race is not a real problem unless you use asan". A new warning has been added.

A flaw has been found in ChaiScript up to 6.1.0. This affects the function chaiscript::Type_Info::bare_equal of the file include/chaiscript/dispatchkit/type_info.hpp. This manipulation causes use after free. The attack requires local access. The attack's complexity is rated as high. The exploitability is reported as difficult. The exploit has been published and may be used. The project was informed of the problem early through an issue report but has not responded yet.

A vulnerability was found in Radare2 5.9.9. It has been classified as problematic. Affected is the function r_cons_pal_init in the library /libr/cons/pal.c of the component radiff2. The manipulation of the argument -T leads to memory corruption. An attack has to be approached locally. The complexity of an attack is rather high. The exploitability is told to be difficult. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The name of the patch is 5705d99cc1f23f36f9a84aab26d1724010b97798. It is recommended to apply a patch to fix this issue. The documentation explains that the parameter -T is experimental and "crashy". Further analysis has shown "the race is not a real problem unless you use asan". A new warning has been added.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. 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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully. Since the constructor(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values. 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.