Security readout for executives and security teams
Plain-English summary
A malformed or empty tensor passed to TensorFlow's FractionalMaxPoolGrad can crash the process. The business impact is limited availability loss in workloads that run affected TensorFlow versions and process attacker-controlled or unreliable model inputs. It is not described as exposing data or changing results.
Executive priority
Treat as routine patching unless TensorFlow processes untrusted model inputs in shared or production services. The expected impact is process crash, not data theft or privilege escalation, based on provided sources.
Technical view
TensorFlow failed to validate that tensors supplied to tf.raw_ops.FractionalMaxPoolGrad were non-empty and same rank. Empty tensors can trigger undefined behavior, and a failed CHECK can abort the process. The vendor fix adds validation and was planned for TensorFlow 2.5.0 and patched supported maintenance releases.
Likely exposure
Exposure is likely limited to systems using affected TensorFlow versions and invoking FractionalMaxPoolGrad with inputs influenced by users, jobs, or model pipelines. Ordinary deployments that do not use this operation are less exposed.
Exploitation context
The source bundle does not show active exploitation, and KEV is false. CVSS indicates local access, high attack complexity, low privileges, no user interaction, and low availability impact only.
Researcher notes
The vulnerability is a missing validation issue in fractional_max_pool_op.cc. Evidence supports undefined behavior and process abort through failed assumptions about empty tensors and rank equality. No public exploit status is provided in the bundle.
Mitigation direction
- Upgrade to TensorFlow 2.5.0 or a patched maintenance release named by the vendor.
- For 2.4.x, use TensorFlow 2.4.2 or later within that branch.
- For 2.3.x, use TensorFlow 2.3.3 or later within that branch.
- For 2.2.x, use TensorFlow 2.2.3 or later within that branch.
- For 2.1.x, use TensorFlow 2.1.4 or later within that branch.
- Check TensorFlow vendor guidance if constrained to older unsupported versions.
Validation and detection
- Inventory TensorFlow versions in applications, notebooks, training images, and inference containers.
- Identify code paths using tf.raw_ops.FractionalMaxPoolGrad or models that may call it indirectly.
- Confirm deployed artifacts no longer match the affected version ranges.
- Review dependency lockfiles and base images for patched TensorFlow versions.
- Run regression tests for ML workloads after upgrading TensorFlow.
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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ATT&CK lookup starting points
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CWE-908: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29580 mapping review
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Open ATT&CK lookup- Severity
- Low
- CVSS
- 2.5 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
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:H/PR:L/UI:N/S:U/C:N/I:N/A:L11.4Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
2.5LowVector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgpCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925CVE 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.
Use of Uninitialized Resource
Use of Uninitialized Resource represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.