Common Weakness Enumeration
CWE-125
AllowedOut-of-bounds Read
Abstraction: Base · Status: Draft
The product reads data past the end, or before the beginning, of the intended buffer.
11347 vulnerabilities reference this CWE, most recent first.
CVE-2021-37670 (GCVE-0-2021-37670)
Vulnerability from cvelistv5 – Published: 2021-08-12 22:25 – Updated: 2024-08-04 01:23
VLAI
EPSS
VEX
Title
Heap OOB in `UpperBound` and `LowerBound` in TensorFlow
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.UpperBound`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/searchsorted_op.cc#L85-L104) does not validate the rank of `sorted_input` argument. A similar issue occurs in `tf.raw_ops.LowerBound`. We have patched the issue in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Severity
5.5 (Medium)
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/4… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
>= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3 Affected: < 2.3.4 |
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CVE-2021-37664 (GCVE-0-2021-37664)
Vulnerability from cvelistv5 – Published: 2021-08-12 20:25 – Updated: 2024-08-04 01:23
VLAI
EPSS
VEX
Title
Heap OOB in boosted trees in TensorFlow
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Severity
7.3 (High)
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/e… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
>= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3 Affected: < 2.3.4 |
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CVE-2021-37659 (GCVE-0-2021-37659)
Vulnerability from cvelistv5 – Published: 2021-08-12 20:25 – Updated: 2024-08-04 01:23
VLAI
EPSS
VEX
Title
Out of bounds read via null pointer dereference in TensorFlow
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Severity
7.3 (High)
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
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Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
>= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3 Affected: < 2.3.4 |
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CVE-2021-37655 (GCVE-0-2021-37655)
Vulnerability from cvelistv5 – Published: 2021-08-12 20:25 – Updated: 2024-08-04 01:23
VLAI
EPSS
VEX
Title
Heap OOB in `ResourceScatterUpdate` in TensorFlow
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. We have patched the issue in GitHub commit 01cff3f986259d661103412a20745928c727326f. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Severity
7.3 (High)
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
2 references
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|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
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Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
>= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3 Affected: < 2.3.4 |
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CVE-2021-37654 (GCVE-0-2021-37654)
Vulnerability from cvelistv5 – Published: 2021-08-12 20:30 – Updated: 2024-08-04 01:23
VLAI
EPSS
VEX
Title
Heap OOB and CHECK fail in `ResourceGather` in TensorFlow
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a crash via a `CHECK`-fail in debug builds of TensorFlow using `tf.raw_ops.ResourceGather` or a read from outside the bounds of heap allocated data in the same API in a release build. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L660-L668) does not check that the `batch_dims` value that the user supplies is less than the rank of the input tensor. Since the implementation uses several for loops over the dimensions of `tensor`, this results in reading data from outside the bounds of heap allocated buffer backing the tensor. We have patched the issue in GitHub commit bc9c546ce7015c57c2f15c168b3d9201de679a1d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Severity
7.3 (High)
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/b… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
>= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3 Affected: < 2.3.4 |
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CVE-2021-37651 (GCVE-0-2021-37651)
Vulnerability from cvelistv5 – Published: 2021-08-12 21:00 – Updated: 2024-08-04 01:23
VLAI
EPSS
VEX
Title
Heap buffer overflow in `FractionalAvgPoolGrad` in TensorFlow
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Severity
7.1 (High)
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/0… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
>= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3 Affected: < 2.3.4 |
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CVE-2021-37641 (GCVE-0-2021-37641)
Vulnerability from cvelistv5 – Published: 2021-08-12 20:30 – Updated: 2024-08-04 01:23
VLAI
EPSS
VEX
Title
Heap OOB in `RaggedGather` in TensorFlow
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Severity
7.3 (High)
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/a… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
>= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3 Affected: < 2.3.4 |
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CVE-2021-37635 (GCVE-0-2021-37635)
Vulnerability from cvelistv5 – Published: 2021-08-12 20:30 – Updated: 2024-08-04 01:23
VLAI
EPSS
VEX
Title
Heap out of bounds access in sparse reduction operations in TensorFlow
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Severity
7.3 (High)
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/8… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
>= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3 Affected: < 2.3.4 |
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CVE-2021-37620 (GCVE-0-2021-37620)
Vulnerability from cvelistv5 – Published: 2021-08-09 00:00 – Updated: 2024-08-04 01:23
VLAI
EPSS
VEX
Title
Out-of-bounds read in XmpTextValue::read()
Summary
Exiv2 is a command-line utility and C++ library for reading, writing, deleting, and modifying the metadata of image files. An out-of-bounds read was found in Exiv2 versions v0.27.4 and earlier. The out-of-bounds read is triggered when Exiv2 is used to read the metadata of a crafted image file. An attacker could potentially exploit the vulnerability to cause a denial of service, if they can trick the victim into running Exiv2 on a crafted image file. The bug is fixed in version v0.27.5.
Severity
4.7 (Medium)
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/Exiv2/exiv2/security/advisorie… | |
| https://github.com/Exiv2/exiv2/pull/1769 | |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisory |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisory |
| https://lists.debian.org/debian-lts-announce/2023… | mailing-list |
| https://security.gentoo.org/glsa/202312-06 | vendor-advisory |
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CVE-2021-37619 (GCVE-0-2021-37619)
Vulnerability from cvelistv5 – Published: 2021-08-09 00:00 – Updated: 2024-08-04 01:23
VLAI
EPSS
VEX
Title
Out-of-bounds read in Exiv2::Jp2Image::encodeJp2Header
Summary
Exiv2 is a command-line utility and C++ library for reading, writing, deleting, and modifying the metadata of image files. An out-of-bounds read was found in Exiv2 versions v0.27.4 and earlier. The out-of-bounds read is triggered when Exiv2 is used to write metadata into a crafted image file. An attacker could potentially exploit the vulnerability to cause a denial of service by crashing Exiv2, if they can trick the victim into running Exiv2 on a crafted image file. Note that this bug is only triggered when writing the metadata, which is a less frequently used Exiv2 operation than reading the metadata. For example, to trigger the bug in the Exiv2 command-line application, you need to add an extra command-line argument such as insert. The bug is fixed in version v0.27.5.
Severity
4.7 (Medium)
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://github.com/Exiv2/exiv2/security/advisorie… | |
| https://github.com/Exiv2/exiv2/pull/1752 | |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisory |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisory |
| https://security.gentoo.org/glsa/202312-06 | vendor-advisory |
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Mitigation MIT-5
Implementation
Strategy: Input Validation
- Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
- When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue."
- Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
- To reduce the likelihood of introducing an out-of-bounds read, ensure that you validate and ensure correct calculations for any length argument, buffer size calculation, or offset. Be especially careful of relying on a sentinel (i.e. special character such as NUL) in untrusted inputs.
Mitigation
Architecture and Design
Strategy: Language Selection
Use a language that provides appropriate memory abstractions.
CAPEC-540: Overread Buffers
An adversary attacks a target by providing input that causes an application to read beyond the boundary of a defined buffer. This typically occurs when a value influencing where to start or stop reading is set to reflect positions outside of the valid memory location of the buffer. This type of attack may result in exposure of sensitive information, a system crash, or arbitrary code execution.