CWE-125

Out-of-bounds Read

The product reads data past the end, or before the beginning, of the intended buffer.

CVE-2021-37203 (GCVE-0-2021-37203)

Vulnerability from cvelistv5 – Published: 2021-09-14 10:47 – Updated: 2024-08-04 01:16
VLAI
Summary
A vulnerability has been identified in NX 1980 Series (All versions < V1984), Solid Edge SE2021 (All versions < SE2021MP8). The plmxmlAdapterIFC.dll contains an out-of-bounds read while parsing user supplied IFC files which could result in a read past the end of an allocated buffer. This could allow an attacker to cause a denial-of-service condition or read sensitive information from memory locations.
Severity
No CVSS data available.
CWE
Assigner
References
Impacted products
Vendor Product Version
Siemens NX 1980 Series Affected: All versions < V1984
Create a notification for this product.
Siemens Solid Edge SE2021 Affected: All versions < SE2021MP8
Create a notification for this product.
Show details on NVD website

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CVE-2021-3743 (GCVE-0-2021-3743)

Vulnerability from cvelistv5 – Published: 2022-03-04 15:52 – Updated: 2024-08-03 17:01
VLAI
Summary
An out-of-bounds (OOB) memory read flaw was found in the Qualcomm IPC router protocol in the Linux kernel. A missing sanity check allows a local attacker to gain access to out-of-bounds memory, leading to a system crash or a leak of internal kernel information. The highest threat from this vulnerability is to system availability.
Severity
No CVSS data available.
CWE
Assigner
Impacted products
Vendor Product Version
n/a Kernel Affected: Affects linux kernel v5.14.0-rc6 and above.
Show details on NVD website

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CVE-2021-3753 (GCVE-0-2021-3753)

Vulnerability from cvelistv5 – Published: 2022-02-16 00:00 – Updated: 2024-08-03 17:09
VLAI
Summary
A race problem was seen in the vt_k_ioctl in drivers/tty/vt/vt_ioctl.c in the Linux kernel, which may cause an out of bounds read in vt as the write access to vc_mode is not protected by lock-in vt_ioctl (KDSETMDE). The highest threat from this vulnerability is to data confidentiality.
Severity
No CVSS data available.
CWE
Assigner
Impacted products
Vendor Product Version
n/a kernel Affected: Linux kernel 5.15-rc1
Show details on NVD website

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CVE-2021-37618 (GCVE-0-2021-37618)

Vulnerability from cvelistv5 – Published: 2021-08-09 00:00 – Updated: 2024-08-04 01:23
VLAI
Title
Out-of-bounds read in Exiv2::Jp2Image::printStructure
Summary
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CWE
Assigner
Impacted products
Vendor Product Version
Exiv2 exiv2 Affected: <= 0.27.4
Create a notification for this product.
Show details on NVD website

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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
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.
CWE
Assigner
Impacted products
Vendor Product Version
Exiv2 exiv2 Affected: <= 0.27.4
Create a notification for this product.
Show details on NVD website

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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
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.
CWE
Assigner
Impacted products
Vendor Product Version
Exiv2 exiv2 Affected: <= 0.27.4
Create a notification for this product.
Show details on NVD website

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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
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.
CWE
Assigner
References
Impacted products
Vendor Product Version
tensorflow tensorflow Affected: >= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3
Affected: < 2.3.4
Create a notification for this product.
Show details on NVD website

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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
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.
CWE
Assigner
References
Impacted products
Vendor Product Version
tensorflow tensorflow Affected: >= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3
Affected: < 2.3.4
Create a notification for this product.
Show details on NVD website

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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
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.
CWE
Assigner
References
Impacted products
Vendor Product Version
tensorflow tensorflow Affected: >= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3
Affected: < 2.3.4
Create a notification for this product.
Show details on NVD website

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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
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.
CWE
Assigner
References
Impacted products
Vendor Product Version
tensorflow tensorflow Affected: >= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3
Affected: < 2.3.4
Create a notification for this product.
Show details on NVD website

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            {
              "lang": "eng",
              "value": "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."
            }
          ]
        },
        "impact": {
          "cvss": {
            "attackComplexity": "LOW",
            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 7.3,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "LOW",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:H",
            "version": "3.1"
          }
        },
        "problemtype": {
          "problemtype_data": [
            {
              "description": [
                {
                  "lang": "eng",
                  "value": "CWE-125: Out-of-bounds Read"
                }
              ]
            }
          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2r8p-fg3c-wcj4",
              "refsource": "CONFIRM",
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2r8p-fg3c-wcj4"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/bc9c546ce7015c57c2f15c168b3d9201de679a1d",
              "refsource": "MISC",
              "url": "https://github.com/tensorflow/tensorflow/commit/bc9c546ce7015c57c2f15c168b3d9201de679a1d"
            }
          ]
        },
        "source": {
          "advisory": "GHSA-2r8p-fg3c-wcj4",
          "discovery": "UNKNOWN"
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2021-37654",
    "datePublished": "2021-08-12T20:30:23.000Z",
    "dateReserved": "2021-07-29T00:00:00.000Z",
    "dateUpdated": "2024-08-04T01:23:01.509Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}

Mitigation ID: MIT-5

Phase: Implementation

Strategy: Input Validation

Description:

  • 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

Phase: Architecture and Design

Strategy: Language Selection

Description:

  • 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.

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