Security readout for executives and security teams
Plain-English summary
A TensorFlow flaw can make certain operations crash when given non-numeric tensors where numeric tensors are expected. The business impact is limited availability disruption in local or already-authorized contexts, not data theft or remote compromise based on the provided sources. Organizations using affected TensorFlow versions should prioritize routine dependency updates.
Executive priority
Handle through normal patch governance unless TensorFlow is exposed in multi-user ML platforms or customer-submitted workload processing. The issue is low severity and availability-only, but outdated ML dependencies can accumulate operational risk.
Technical view
TensorFlow's Python-to-C++ array conversion had type confusion. Operations expecting numeric tensors could receive non-numeric tensors and dereference null pointers. CVSS is 2.5 with local access, high complexity, low availability impact, and no confidentiality or integrity impact. Affected ranges include TensorFlow before 2.1.4 and specific 2.2.x, 2.3.x, and 2.4.x releases.
Likely exposure
Exposure is likely limited to applications or workflows running affected TensorFlow versions where local or authenticated users, jobs, or trusted code paths can submit tensors to vulnerable operations. The source bundle does not indicate default network-reachable exposure.
Exploitation context
The bundle reports no CISA KEV listing and provides no cited evidence of active exploitation. CVSS indicates local access, high complexity, and low availability impact only. Treat this as a stability or denial-of-service risk, especially in shared ML environments.
Researcher notes
The public bundle identifies CWE-476 and a type confusion path in ndarray_tensor.cc during Python array to C++ array conversion. Evidence is sufficient for affected-version tracking, but it does not include proof of active exploitation or broader product impact.
Mitigation direction
- Upgrade to TensorFlow 2.5.0 or a fixed supported branch release named by the advisory.
- Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where those branches are required.
- Review vendor advisory and commit details before accepting residual risk.
- Restrict who can run untrusted TensorFlow workloads in shared environments.
Validation and detection
- Inventory TensorFlow versions in applications, notebooks, containers, and training images.
- Confirm no deployed package matches the affected version ranges.
- Check dependency lockfiles and image manifests for transitive TensorFlow installs.
- Run existing unit and workload tests 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-476: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29513 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-452g-f7fp-9jf7CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/030af767d357d1b4088c4a25c72cb3906abac489CVE 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.
NULL Pointer Dereference
NULL Pointer Dereference represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.