Security readout for executives and security teams
Plain-English summary
This is a low-severity TensorFlow availability bug. A user who can run or influence a specific TensorFlow quantized multiplication operation may cause a division-by-zero crash. The sources do not show data theft, data modification, remote unauthenticated attack, or active exploitation.
Executive priority
Treat as routine patching unless TensorFlow is exposed in shared execution environments. The available evidence supports low business urgency because impact is crash-only and requires local, low-privileged access with high complexity.
Technical view
CVE-2021-29528 is CWE-369 in TensorFlow `tf.raw_ops.QuantizedMul`. The implementation divides by a caller-controlled quantity, allowing division by zero. CVSS 3.1 is 2.5, with local attack vector, high complexity, low privileges, no user interaction, and low availability impact only.
Likely exposure
Exposure is most plausible in TensorFlow environments where low-privileged or untrusted users can execute TensorFlow operations or control inputs reaching `QuantizedMul`. Listed affected ranges include TensorFlow versions before 2.1.4 and selected 2.2.x, 2.3.x, and 2.4.x releases before patched versions.
Exploitation context
The provided sources do not identify public exploitation or KEV listing. The CVSS vector indicates local access with low privileges and high attack complexity. Impact is limited to availability, so business urgency is mainly preventing crashes in shared ML, notebook, or batch-processing environments.
Researcher notes
The key condition is caller control over the divisor in `QuantizedMul`. The source bundle cites the vulnerable implementation and a fixing commit, but does not provide evidence of exploit availability, exploitation in the wild, or broader product impact beyond TensorFlow.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or patched supported releases 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
- Check TensorFlow advisory GHSA-6f84-42vf-ppwp for exact affected-version guidance.
- Prioritize shared or multi-user ML platforms before isolated developer workstations.
- Restrict untrusted users from running arbitrary TensorFlow workloads where upgrades are delayed.
Validation and detection
- Inventory TensorFlow package versions across applications, notebooks, containers, and ML pipelines.
- Flag versions matching the affected ranges listed in the CVE bundle.
- Confirm upgraded systems report TensorFlow 2.5.0 or a patched supported branch.
- Review whether any exposed workload permits untrusted TensorFlow operation execution.
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-2021-29528 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-6f84-42vf-ppwpCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/a1b11d2fdd1e51bfe18bb1ede804f60abfa92da6CVE 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.