Security readout for executives and security teams
Plain-English summary
CVE-2021-29546 is a low-severity TensorFlow flaw where a crafted use of QuantizedBiasAdd can cause a division-by-zero condition. The expected impact is limited availability disruption, not data theft or privilege escalation, but shared ML platforms should still patch affected TensorFlow versions.
Executive priority
Treat this as routine patching unless TensorFlow is exposed through multi-tenant or user-submitted ML workloads. It is low severity, but availability issues in shared compute or model-serving environments can still disrupt business operations.
Technical view
TensorFlow's Eigen kernel for tf.raw_ops.QuantizedBiasAdd divided by the element count of the smaller shaped input without checking for zero. This can trigger integer division by zero undefined behavior. CVSS 3.1 is 2.5: local attack vector, high complexity, low privileges, and low availability impact only.
Likely exposure
Exposure is most likely where affected TensorFlow versions run workloads from low-privileged or untrusted users, especially shared notebooks, ML pipelines, or services that execute submitted TensorFlow graphs. The bundle lists TensorFlow versions before 2.1.4 and specific 2.2.x, 2.3.x, and 2.4.x ranges as affected.
Exploitation context
The provided sources do not show active exploitation, and the CVE is not marked in KEV. Exploitation requires local access, low privileges, and high attack complexity. The supported impact is availability degradation through a crash or undefined behavior, not confidentiality or integrity compromise.
Researcher notes
The core weakness is CWE-369 in QuantizedBiasAdd shape handling. Evidence supports a division by the smaller input's element count without a zero check. No exploit code, real-world exploitation, or broader product impact is provided in the source bundle.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a fixed supported branch release.
- Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where those branches are required.
- Move older unsupported TensorFlow releases to a supported patched release.
- Review TensorFlow's advisory and fix commit for vendor-specific remediation details.
Validation and detection
- Inventory TensorFlow versions in production, notebooks, CI, and model-serving environments.
- Flag versions below 2.1.4 and affected 2.2.x, 2.3.x, and 2.4.x ranges.
- Confirm patched deployments report TensorFlow 2.5.0 or the applicable fixed branch version.
- Check whether untrusted users can execute TensorFlow workloads on shared systems.
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
These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.
ATT&CK lookup starting points
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
CWE-369: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29546 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-m34j-p8rj-wjxqCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/67784700869470d65d5f2ef20aeb5e97c31673cbCVE 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.
Divide By Zero
Divide By Zero represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.