Security readout for executives and security teams
Plain-English summary
This TensorFlow flaw lets a low-privileged local attacker trigger unsafe memory behavior in affected TensorFlow processes using malformed QuantizeV2 inputs. Business impact depends on where TensorFlow runs and whether users can submit models or tensors. There is no provided evidence of active exploitation.
Executive priority
Prioritize remediation for shared ML platforms, hosted notebooks, model-serving systems, and environments where users can submit TensorFlow workloads. Isolated internal systems with trusted inputs can follow normal patch cycles, but affected versions should still be retired.
Technical view
CVE-2021-37663 is incomplete input validation in tf.raw_ops.QuantizeV2. min_range and max_range lengths were not adequately checked, especially with axis handling, allowing null pointer binding or out-of-bounds heap array access. TensorFlow patched this in commit 6da6620efad397c85493b8f8667b821403516708.
Likely exposure
Exposure is most likely in ML workloads, notebooks, pipelines, or serving systems using affected TensorFlow versions and processing untrusted or semi-trusted tensors or models. Purely isolated training environments with trusted inputs are lower risk.
Exploitation context
The CVSS vector is local, low complexity, low privileges, and no user interaction. The sources do not show KEV listing or active exploitation. Practical risk increases where untrusted users can run TensorFlow operations or supply model inputs.
Researcher notes
The vulnerability is a validation gap around min_range, max_range, and axis-derived dimensional checks in QuantizeV2. Sources describe memory safety consequences but do not provide exploit evidence, exploit maturity, or product impacts beyond TensorFlow.
Mitigation direction
- Upgrade to TensorFlow 2.6.0 or later where feasible.
- For supported older branches, use TensorFlow 2.5.1, 2.4.3, or 2.3.4.
- Check vendor guidance for unsupported TensorFlow branches.
- Restrict untrusted access to TensorFlow execution environments.
- Review model-serving paths that accept external tensors or models.
Validation and detection
- Inventory TensorFlow versions in applications, notebooks, images, and lockfiles.
- Confirm no runtime uses affected versions listed by the advisory.
- Identify paths invoking tf.raw_ops.QuantizeV2 or quantization workflows.
- Verify deployed containers and environments include patched TensorFlow builds.
- Confirm no public or tenant-controlled path can submit unsafe TensorFlow inputs.
Public sources used
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Conservative CVE-to-ATT&CK context
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Open ATT&CK lookupCVE-2021-37663 mapping review
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Open ATT&CK lookup- Severity
- High
- CVSS
- 7.8 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
CNA and ADP enrichment extracted from CVE v5
These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.
CVSS vector scores
1 official scoreWe collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H1.85.9Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
7.8HighVector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g25h-jr74-qp5jCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708CVE reference · x_refsource_MISC
Products and packages named in the record
CWE details
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.