Security readout for executives and security teams
Plain-English summary
CVE-2021-29555 is a low-severity TensorFlow denial-of-service issue. If an attacker can influence inputs to a vulnerable FusedBatchNorm operation, TensorFlow can hit a divide-by-zero runtime error and stop the affected process. The sources do not indicate data theft, integrity impact, or active exploitation.
Executive priority
Treat as a routine dependency update unless TensorFlow is exposed to untrusted ML inputs in production. Business risk is service disruption, not compromise of data or control.
Technical view
TensorFlow tf.raw_ops.FusedBatchNorm can trigger a floating-point exception because the implementation divides using the last dimension of the x tensor, which user input can control. The issue is CWE-369 and affects specified TensorFlow 2.1.x through 2.4.x ranges before patched releases.
Likely exposure
Exposure is most likely in applications running affected TensorFlow versions and accepting untrusted models, graphs, or tensor shapes that reach FusedBatchNorm. Internal-only ML workflows with trusted inputs have lower practical risk.
Exploitation context
The CVSS vector requires local access, low privileges, high attack complexity, and no user interaction. KEV status is false, and the provided sources do not report active exploitation. Impact is limited to low availability loss.
Researcher notes
The root cause is a divide-by-zero condition in FusedBatchNorm tied to tensor shape handling. The public bundle names the fixing release branches but does not provide evidence of exploitation in the wild.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a patched supported branch release.
- Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where applicable.
- Prioritize services that process untrusted ML inputs or user-controlled tensor shapes.
- Check TensorFlow advisory guidance before relying on compensating controls.
Validation and detection
- Inventory TensorFlow versions across applications, containers, and notebooks.
- Compare installed versions against the affected ranges in the CVE source bundle.
- Identify code paths using FusedBatchNorm with externally influenced inputs.
- Confirm upgraded environments use a fixed TensorFlow release.
- Review application monitoring for unexplained TensorFlow process crashes.
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
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CWE-369: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29555 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-r35g-4525-29fqCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084dCVE 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.