Vulnerability-Lookup

GSD-2020-15206

Vulnerability from gsd - Updated: 2023-12-13 01:21

Details

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 service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Aliases

CVE-2020-15206

Aliases

CVE-2020-15206

JSON

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{
  "GSD": {
    "alias": "CVE-2020-15206",
    "description": "In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow\u0027s `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 service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.",
    "id": "GSD-2020-15206",
    "references": [
      "https://www.suse.com/security/cve/CVE-2020-15206.html"
    ]
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
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          "solution": "Upgrade to versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or above.",
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}

CVE-2020-15206 (GCVE-0-2020-15206)

Vulnerability from cvelistv5 – Published: 2020-09-25 18:45 – Updated: 2024-08-04 13:08

Title

Denial of Service in Tensorflow

Summary

Severity

9 (Critical)


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

CWE

CWE-20 - {"CWE-20":"Improper Input Validation"}

Assigner

GitHub_M CNA under the mitre root

CNA scorecard C 72/100 over 7761 records in the last 180 days details

References

4 references

URL	Tags
https://github.com/tensorflow/tensorflow/releases…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/commit/a…	x_refsource_MISC
http://lists.opensuse.org/opensuse-security-annou…	vendor-advisoryx_refsource_SUSE

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: < 1.15.4 Affected: >= 2.0.0, < 2.0.3 Affected: >= 2.1.0, < 2.1.2 Affected: >= 2.2.0, < 2.2.1 Affected: >= 2.3.0, < 2.3.1

Show details on NVD website

JSON

To clipboard

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              "version": "\u003e= 2.0.0, \u003c 2.0.3"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.1.0, \u003c 2.1.2"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.2.0, \u003c 2.2.1"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.3.0, \u003c 2.3.1"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow\u0027s `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 service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1."
        }
      ],
      "metrics": [
        {
          "cvssV3_1": {
            "attackComplexity": "HIGH",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 9,
            "baseSeverity": "CRITICAL",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "scope": "CHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H",
            "version": "3.1"
          }
        }
      ],
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              "cweId": "CWE-20",
              "description": "{\"CWE-20\":\"Improper Input Validation\"}",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2020-10-29T15:06:15.000Z",
        "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
        "shortName": "GitHub_M"
      },
      "references": [
        {
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
        },
        {
          "tags": [
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          "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w5gh-2wr2-pm6g"
        },
        {
          "tags": [
            "x_refsource_MISC"
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          "url": "https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6"
        },
        {
          "name": "openSUSE-SU-2020:1766",
          "tags": [
            "vendor-advisory",
            "x_refsource_SUSE"
          ],
          "url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
        }
      ],
      "source": {
        "advisory": "GHSA-w5gh-2wr2-pm6g",
        "discovery": "UNKNOWN"
      },
      "title": "Denial of Service in Tensorflow",
      "x_legacyV4Record": {
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          "ASSIGNER": "security-advisories@github.com",
          "ID": "CVE-2020-15206",
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        },
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              "value": "In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow\u0027s `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 service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1."
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  "cveMetadata": {
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    "assignerShortName": "GitHub_M",
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    "datePublished": "2020-09-25T18:45:51.000Z",
    "dateReserved": "2020-06-25T00:00:00.000Z",
    "dateUpdated": "2024-08-04T13:08:22.955Z",
    "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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