Security readout for executives and security teams
Plain-English summary
Certain TensorFlow versions mishandle malformed sparse tensor inputs in SparseAdd. An attacker with some ability to supply tensor data could cause crashes and potentially corrupt heap memory. This is mainly a risk for shared ML platforms, notebooks, or services that process untrusted TensorFlow inputs.
Executive priority
Treat this as a moderate-priority dependency remediation for ML environments, especially shared platforms accepting user-controlled inputs. It is not reported as actively exploited, but the availability impact and memory corruption behavior justify scheduled patching and exposure review.
Technical view
SparseAdd validates many sparse tensor inputs but missed checks for empty tensors and whether indices dimensions match shape dimensions. Invalid sparse tensor triples can violate internal assumptions, causing null pointer dereference or out-of-bounds heap writes. The CVSS 3.1 score is 5.3 with local, high-complexity, low-privilege attack conditions.
Likely exposure
Exposure is most likely where affected TensorFlow versions process untrusted sparse tensors, user-submitted jobs, models, or data pipelines. Purely internal systems with trusted inputs have lower practical risk. The listed affected ranges include TensorFlow versions before the fixed 2.1.4, 2.2.3, 2.3.3, and 2.4.2 releases.
Exploitation context
The source bundle does not show active exploitation, and KEV is false. Exploitation requires local access or an equivalent ability to influence TensorFlow execution with crafted sparse tensor inputs. Availability impact is the clearest concern; integrity impact is listed as low.
Researcher notes
The core issue is incomplete validation in SparseAdd. Missing checks allow invalid sparse tensor triples where inputs are empty or indices shape metadata disagrees with dense shape metadata. The advisory ties this to undefined behavior, null pointer dereference, and heap out-of-bounds write.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or the fixed supported branch release.
- Use 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where pinned to older branches.
- Restrict untrusted users from submitting arbitrary TensorFlow jobs or tensor inputs.
- Isolate shared ML workloads with least privilege and resource boundaries.
- Review vendor advisory and commits for exact fixed build applicability.
Validation and detection
- Inventory TensorFlow versions in applications, notebooks, containers, and training images.
- Identify workflows that call SparseAdd or process sparse tensor inputs.
- Confirm no deployed package falls within the affected version ranges.
- Verify remediation by checking the deployed TensorFlow version after rebuild.
- Review crash reports for TensorFlow sparse operation failures if exposure existed.
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-2021-29609 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
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: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/security/advisories/GHSA-cjc7-49v2-jp64CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/41727ff06111117bdf86b37db198217fd7a143ccCVE reference · x_refsource_MISC
- https://github.com/tensorflow/tensorflow/commit/6fd02f44810754ae7481838b6a67c5df7f909ca3CVE 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.
Improper Initialization
Improper Initialization represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.