Security readout for executives and security teams
Plain-English summary
TensorFlow could crash when its SparseReshape operation receives invalid sparse tensor inputs. The issue is low severity because exploitation is local, high-complexity, and requires low privileges. Business impact is mainly disruption of affected ML workloads rather than data theft or broad system compromise.
Executive priority
Treat as routine but real hygiene work. It is unlikely to drive emergency response without evidence of exploitation, but affected ML workloads should be upgraded during the next maintenance window, especially where shared users or untrusted inputs exist.
Technical view
CVE-2021-29611 is incomplete input validation in TensorFlow SparseReshape. Invalid sparse tensor arguments can trigger a CHECK failure, causing denial of service. The source bundle lists affected TensorFlow versions as below 2.3.3 and 2.4.0 through before 2.4.2. Fixes are stated for 2.5.0, 2.4.2, and 2.3.3.
Likely exposure
Exposure is most likely in TensorFlow environments using affected versions and accepting sparse tensor data from low-privileged or less-trusted local users, jobs, notebooks, pipelines, or plugins. Remote internet exposure is not supported by the provided CVSS vector.
Exploitation context
The bundle does not show KEV listing or active exploitation. CVSS indicates local access, high attack complexity, low privileges, no user interaction, no confidentiality impact, and limited integrity and availability impact. The known outcome is a denial of service via CHECK failure.
Researcher notes
The evidence supports a validation flaw causing CHECK-failure denial of service, not code execution. Keep analysis bounded to TensorFlow SparseReshape and the listed version ranges. The bundle does not provide exploit details, field exploitation, or non-TensorFlow affected products.
Mitigation direction
- Upgrade TensorFlow to 2.5.0, 2.4.2, or 2.3.3 as appropriate.
- Prioritize ML systems that process sparse tensors from less-trusted users or jobs.
- Check the TensorFlow advisory for branch-specific remediation guidance.
- Limit access to affected local ML workflows until upgrades are complete.
Validation and detection
- Inventory TensorFlow versions in training, inference, notebook, and pipeline environments.
- Confirm no deployed environment runs versions below 2.3.3 or 2.4.0 through 2.4.1.
- Review workloads for SparseReshape usage with externally influenced sparse tensor inputs.
- Verify remediation against the TensorFlow advisory and referenced fixing commit.
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-2021-29611 mapping review
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Open ATT&CK lookup- Severity
- Low
- CVSS
- 3.6 (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: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:L/A:L12.5Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
3.6LowVector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:L
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9rpc-5v9q-5r7fCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6CVE 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.