Security readout for executives and security teams
Plain-English summary
TensorFlow could mishandle malformed sparse tensor inputs because validation missed some invalid sparse-tensor shapes. A low-privileged local attacker able to submit crafted tensor triples could trigger undefined behavior, including crashes and limited memory corruption impact. The main business risk is disruption of ML workloads using affected TensorFlow versions.
Executive priority
Treat as a scheduled security update for ML environments, with higher priority where users can submit tensor data or jobs. The risk is mainly service disruption, with limited integrity impact and no cited active exploitation.
Technical view
The issue is incomplete validation in TensorFlow sparse-sparse binary operation handling, described for `SparseAdd`. The code did not ensure sparse tensors were non-empty or that each `*_indices` second dimension matched the related `*_shape` size. This could cause null pointer dereference or out-of-bounds heap writes.
Likely exposure
Exposure is limited to TensorFlow deployments using affected versions before the fixed releases and processing attacker-controlled sparse tensor triples. CVSS indicates local access, high complexity, low privileges, no user interaction, and high availability impact.
Exploitation context
The source bundle does not show CISA KEV listing or cited active exploitation. Exploitation requires the ability to feed invalid sparse tensors into vulnerable TensorFlow operations; no remote exploitation path is established by the provided sources.
Researcher notes
The bundle title references `SparseSparseMinimum`, while the description discusses `SparseAdd`; avoid over-scoping without checking vendor records. The key validation gaps are empty sparse tensors and mismatched `*_indices` rank versus `*_shape` size.
Mitigation direction
- Upgrade to TensorFlow 2.5.0 or later where the fix is included.
- Use patched supported releases: 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
- Inventory containers, notebooks, services, and training pipelines for affected TensorFlow versions.
- Limit untrusted sparse tensor inputs until affected TensorFlow installations are patched.
- Review TensorFlow advisory and commit notes for vendor-specific guidance.
Validation and detection
- Check installed TensorFlow versions against the affected version ranges in the advisory.
- Identify workloads that accept user-controlled sparse tensor triples or model inputs.
- Confirm patched versions are deployed across runtime images and dependency lockfiles.
- Review sparse TensorFlow operation usage, especially sparse-sparse binary operation paths.
- Verify remediation through dependency scans and controlled regression testing.
Public sources used
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
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Open ATT&CK lookupCVE-2021-29607 mapping review
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Open ATT&CK lookup- Severity
- Medium
- CVSS
- 5.3 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:H
CNA and ADP enrichment extracted from CVE v5
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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:L/A:H14.2Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
5.3MediumVector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:H
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2CVE reference · x_refsource_MISC
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gv26-jpj9-c8gqCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/f6fde895ef9c77d848061c0517f19d0ec2682f3aCVE reference · x_refsource_MISC
Products and packages named in the record
CWE details
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Improper Check for Unusual or Exceptional Conditions
Improper Check for Unusual or Exceptional Conditions represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.