Security readout for executives and security teams
Plain-English summary
This is a low-severity TensorFlow denial-of-service issue. A local, low-privileged attacker who can influence affected TensorFlow inputs may trigger a division-by-zero runtime error, disrupting availability. The sources do not indicate data theft, privilege escalation, remote exploitation, or active exploitation.
Executive priority
Treat as routine patching unless affected TensorFlow services are multi-tenant or accept untrusted ML workloads. The business impact is availability disruption, not confirmed data compromise.
Technical view
Affected TensorFlow code performs a modulo operation without first ensuring the divisor is nonzero. Because vector_num_elements is derived from input shapes, certain shapes can make it zero and trigger a division-by-zero failure. The advisory states fixes are in TensorFlow 2.5.0 and backported supported releases.
Likely exposure
Exposure is mainly TensorFlow deployments using affected versions: before 2.1.4, 2.2.0 before 2.2.3, 2.3.0 before 2.3.3, or 2.4.0 before 2.4.2, where low-privileged users can influence relevant inputs.
Exploitation context
The CVSS vector indicates local access, high attack complexity, low privileges, no user interaction, and low availability impact. CISA KEV status is false in the bundle, and no cited source supports active exploitation.
Researcher notes
The bundle title references QuantizedAdd, while the description names tf.raw_ops.QuantizedBatchNormWithGlobalNormalization and links quantized_add_op.cc. Validate the exact affected path against the GitHub advisory and commit before writing detections.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a fixed backport release.
- Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where applicable.
- Limit untrusted users from submitting TensorFlow graphs or inputs until fixed.
- Check TensorFlow advisory guidance for supported upgrade paths.
Validation and detection
- Inventory TensorFlow versions across applications, notebooks, containers, and ML workers.
- Confirm deployed versions are outside the affected ranges listed in the advisory.
- Identify services where untrusted users influence TensorFlow input shapes or graphs.
- Retest availability-sensitive ML workflows 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-369: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29549 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-x83m-p7pv-ch8vCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16CVE 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.