CVE-2021-37647 Vulnerability Analysis & Exploit Details

CVE-2021-37647
Vulnerability Scoring

7.7
/10
Very High Risk

Highly exploitable, CVE-2021-37647 poses a critical security risk that could lead to severe breaches.

Attack Complexity Details

  • Attack Complexity: Low
    Exploits can be performed without significant complexity or special conditions.
  • Attack Vector: Local
    Vulnerability requires local system access.
  • Privileges Required: None
    No privileges are required for exploitation.
  • Scope: Unchanged
    Exploit remains within the originally vulnerable component.
  • User Interaction: None
    No user interaction is necessary for exploitation.

CVE-2021-37647 Details

Status: Modified

Last updated: 🕕 21 Nov 2024, 06:15 UTC
Originally published on: 🕖 12 Aug 2021, 19:15 UTC

Time between publication and last update: 1196 days

CVSS Release: version 3

CVSS3 Source

security-advisories@github.com

CVSS3 Type

Secondary

CVSS3 Vector

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

CVE-2021-37647 Vulnerability Summary

CVE-2021-37647: TensorFlow is an end-to-end open source platform for machine learning. When a user does not supply arguments that determine a valid sparse tensor, `tf.raw_ops.SparseTensorSliceDataset` implementation can be made to dereference a null pointer. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L240-L251) has some argument validation but fails to consider the case when either `indices` or `values` are provided for an empty sparse tensor when the other is not. If `indices` is empty, then [code that performs validation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L260-L261) (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference. If `indices` as provided by the user is empty, then `indices` in the C++ code above is backed by an empty `std::vector`, hence calling `indices->dim_size(0)` results in null pointer dereferencing (same as calling `std::vector::at()` on an empty vector). We have patched the issue in GitHub commit 02cc160e29d20631de3859c6653184e3f876b9d7. 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.

Assessing the Risk of CVE-2021-37647

Access Complexity Graph

The exploitability of CVE-2021-37647 depends on two key factors: attack complexity (the level of effort required to execute an exploit) and privileges required (the access level an attacker needs).

Exploitability Analysis for CVE-2021-37647

With low attack complexity and no required privileges, CVE-2021-37647 is an easy target for cybercriminals. Organizations should prioritize immediate mitigation measures to prevent unauthorized access and data breaches.

Understanding AC and PR

A lower complexity and fewer privilege requirements make exploitation easier. Security teams should evaluate these aspects to determine the urgency of mitigation strategies, such as patch management and access control policies.

Attack Complexity (AC) measures the difficulty in executing an exploit. A high AC means that specific conditions must be met, making an attack more challenging, while a low AC means the vulnerability can be exploited with minimal effort.

Privileges Required (PR) determine the level of system access necessary for an attack. Vulnerabilities requiring no privileges are more accessible to attackers, whereas high privilege requirements limit exploitation to authorized users with elevated access.

CVSS Score Breakdown Chart

Above is the CVSS Sub-score Breakdown for CVE-2021-37647, illustrating how Base, Impact, and Exploitability factors combine to form the overall severity rating. A higher sub-score typically indicates a more severe or easier-to-exploit vulnerability.

CIA Impact Analysis

Below is the Impact Analysis for CVE-2021-37647, showing how Confidentiality, Integrity, and Availability might be affected if the vulnerability is exploited. Higher values usually signal greater potential damage.

  • Confidentiality: None
    CVE-2021-37647 has no significant impact on data confidentiality.
  • Integrity: High
    CVE-2021-37647 could allow unauthorized modifications to data, potentially affecting system reliability and trust.
  • Availability: High
    CVE-2021-37647 can disrupt system operations, potentially causing complete denial of service (DoS).

Exploit Prediction Scoring System (EPSS)

The EPSS score estimates the probability that this vulnerability will be exploited in the near future.

EPSS Score: 0.044% (probability of exploit)

EPSS Percentile: 15.45% (lower percentile = lower relative risk)
This vulnerability is less risky than approximately 84.55% of others.

CVE-2021-37647 References

External References

CWE Common Weakness Enumeration

CWE-476

Vulnerable Configurations

  • cpe:2.3:a:google:tensorflow:2.3.0:-:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.0:-:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.0:-:*:*:-:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.0:-:*:*:-:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.0:-:*:*:lite:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.0:-:*:*:lite:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.0:rc0:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.0:rc0:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.0:rc0:*:*:-:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.0:rc0:*:*:-:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.0:rc0:*:*:lite:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.0:rc0:*:*:lite:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.0:rc1:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.0:rc1:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.0:rc1:*:*:-:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.0:rc1:*:*:-:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.0:rc1:*:*:lite:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.0:rc1:*:*:lite:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.0:rc2:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.0:rc2:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.0:rc2:*:*:-:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.0:rc2:*:*:-:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.0:rc2:*:*:lite:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.0:rc2:*:*:lite:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.1:*:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.1:*:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.1:*:*:*:-:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.1:*:*:*:-:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.1:*:*:*:lite:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.1:*:*:*:lite:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.2:*:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.2:*:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.3.3:*:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.3.3:*:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:-:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:-:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:-:*:*:-:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:-:*:*:-:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc0:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc0:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc0:*:*:-:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc0:*:*:-:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc0:*:*:lite:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc0:*:*:lite:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc1:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc1:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc1:*:*:-:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc1:*:*:-:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc1:*:*:lite:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc1:*:*:lite:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc2:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc2:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc2:*:*:-:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc2:*:*:-:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc2:*:*:lite:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc2:*:*:lite:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc3:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc3:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc3:*:*:-:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc3:*:*:-:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc3:*:*:lite:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc3:*:*:lite:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc4:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc4:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.0:rc4:*:*:-:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.0:rc4:*:*:-:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.1:*:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.1:*:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.4.2:*:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.4.2:*:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.5.0:*:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.5.0:*:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.6.0:rc0:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.6.0:rc0:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.6.0:rc1:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.6.0:rc1:*:*:*:*:*:*
  • cpe:2.3:a:google:tensorflow:2.6.0:rc2:*:*:*:*:*:*
    cpe:2.3:a:google:tensorflow:2.6.0:rc2:*:*:*:*:*:*

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