Vulnerability-Lookup

GSD-2021-29521

Vulnerability from gsd - Updated: 2023-12-13 01:23

Details

TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., `std::vector<absl::flat_hash_map<int64,T>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. 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.

Aliases

CVE-2021-29521

Aliases

CVE-2021-29521

JSON

To clipboard

{
  "GSD": {
    "alias": "CVE-2021-29521",
    "description": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` (i.e., `std::vector\u003cabsl::flat_hash_map\u003cint64,T\u003e\u003e`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. 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.",
    "id": "GSD-2021-29521",
    "references": [
      "https://www.suse.com/security/cve/CVE-2021-29521.html",
      "https://security.archlinux.org/CVE-2021-29521"
    ]
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
    "osvSchema": {
      "aliases": [
        "CVE-2021-29521"
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      "details": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` (i.e., `std::vector\u003cabsl::flat_hash_map\u003cint64,T\u003e\u003e`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. 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.",
      "id": "GSD-2021-29521",
      "modified": "2023-12-13T01:23:36.776284Z",
      "schema_version": "1.4.0"
    }
  },
  "namespaces": {
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        "ID": "CVE-2021-29521",
        "STATE": "PUBLIC",
        "TITLE": "Segfault in SparseCountSparseOutput"
      },
      "affects": {
        "vendor": {
          "vendor_data": [
            {
              "product": {
                "product_data": [
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                    "product_name": "tensorflow",
                    "version": {
                      "version_data": [
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                          "version_value": "\u003c 2.3.3"
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            "value": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` (i.e., `std::vector\u003cabsl::flat_hash_map\u003cint64,T\u003e\u003e`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. 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."
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          "availabilityImpact": "LOW",
          "baseScore": 2.5,
          "baseSeverity": "LOW",
          "confidentialityImpact": "NONE",
          "integrityImpact": "NONE",
          "privilegesRequired": "LOW",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
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      "source": {
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        "discovery": "UNKNOWN"
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          "affected_versions": "All versions starting from 2.3.0 before 2.3.3, all versions starting from 2.4.0 before 2.4.2",
          "cvss_v2": "AV:L/AC:L/Au:N/C:N/I:N/A:P",
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            "CWE-937"
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          "package_slug": "pypi/tensorflow-cpu",
          "pubdate": "2021-05-21",
          "solution": "Upgrade to versions 2.3.3, 2.4.2 or above.",
          "title": "Incorrect Calculation of Buffer Size",
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          ],
          "date": "2021-05-21",
          "description": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape  solves this issue. 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.",
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          "package_slug": "pypi/tensorflow-gpu",
          "pubdate": "2021-05-21",
          "solution": "Upgrade to versions 2.3.3, 2.4.2 or above.",
          "title": "Incorrect Calculation of Buffer Size",
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          "cwe_ids": [
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          "date": "2021-05-20",
          "description": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` (i.e., `std::vector\u003cabsl::flat_hash_map\u003cint64,T\u003e\u003e` data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue.",
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          "identifier": "CVE-2021-29521",
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          "package_slug": "pypi/tensorflow",
          "pubdate": "2021-05-14",
          "solution": "Upgrade to version 2.5.0 or above.",
          "title": "Incorrect Calculation of Buffer Size",
          "urls": [
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          "uuid": "97be527f-dce1-4cc2-ae86-e0e4a344df09"
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    "nvd.nist.gov": {
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            "accessVector": "LOCAL",
            "authentication": "NONE",
            "availabilityImpact": "PARTIAL",
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            "vectorString": "AV:L/AC:L/Au:N/C:N/I:N/A:P",
            "version": "2.0"
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          "exploitabilityScore": 3.9,
          "impactScore": 2.9,
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        },
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            "baseScore": 5.5,
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            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
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          "impactScore": 3.6
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      },
      "lastModifiedDate": "2021-05-20T17:19Z",
      "publishedDate": "2021-05-14T20:15Z"
    }
  }
}

CVE-2021-29521 (GCVE-0-2021-29521)

Vulnerability from cvelistv5 – Published: 2021-05-14 19:35 – Updated: 2024-08-03 22:11

Title

Segfault in SparseCountSparseOutput

Summary

Severity

2.5 (Low)


                        
                          CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

CWE

CWE-131 - Incorrect Calculation of Buffer Size

Assigner

GitHub_M CNA under the mitre root

CNA scorecard C 72/100 over 7619 records in the last 180 days details

References

2 references

URL	Tags
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/commit/c…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: < 2.3.3 Affected: >= 2.4.0, < 2.4.2

Show details on NVD website

JSON

To clipboard

{
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          "dateUpdated": "2024-08-03T22:11:05.725Z",
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            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm"
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            "url": "https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5"
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        "title": "CVE Program Container"
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    "cna": {
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          "versions": [
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              "type": "CWE"
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          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2021-05-14T19:35:49.000Z",
        "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
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      },
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          "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm"
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          "url": "https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5"
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      ],
      "source": {
        "advisory": "GHSA-hr84-fqvp-48mm",
        "discovery": "UNKNOWN"
      },
      "title": "Segfault in SparseCountSparseOutput",
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          "ID": "CVE-2021-29521",
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          "TITLE": "Segfault in SparseCountSparseOutput"
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                      "product_name": "tensorflow",
                      "version": {
                        "version_data": [
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                            "version_value": "\u003c 2.3.3"
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                            "version_value": "\u003e= 2.4.0, \u003c 2.4.2"
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              "value": "TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap\u003cT\u003e` (i.e., `std::vector\u003cabsl::flat_hash_map\u003cint64,T\u003e\u003e`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. 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."
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            "attackVector": "LOCAL",
            "availabilityImpact": "LOW",
            "baseScore": 2.5,
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    "dateReserved": "2021-03-30T00:00:00.000Z",
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    "state": "PUBLISHED"
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Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.

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