Security readout for executives and security teams
Plain-English summary
CVE-2021-37653 is a TensorFlow denial-of-service issue. A user who can run affected TensorFlow operations can crash the process by triggering a divide-by-zero condition in ResourceGather. The documented impact is availability loss, not data theft or code execution.
Executive priority
Treat this as a medium-priority availability risk. Escalate where TensorFlow runs in shared platforms or production ML services, because a crash can interrupt workloads. It is less urgent than code execution or data exposure issues.
Technical view
Affected TensorFlow versions divide by a computed batch_size in tf.raw_ops.ResourceGather without first checking for zero. This can cause a floating point exception and process crash. The issue is CWE-369 and has CVSS 3.1 score 5.5 with local, low-complexity, low-privilege attack conditions.
Likely exposure
Exposure is most likely in systems running vulnerable TensorFlow versions where local users, notebooks, jobs, or application inputs can invoke TensorFlow graph operations. The source bundle identifies TensorFlow only; no other affected products are evidenced.
Exploitation context
The CVE is not listed as KEV, and the provided sources do not show active exploitation. The attack model is local with low privileges and no user interaction, so shared ML environments deserve more attention than isolated developer machines.
Researcher notes
The fix is commit ac117ee8a8ea57b73d34665cdf00ef3303bc0b11, which addresses unchecked zero division in ResourceGather. The provided evidence supports denial of service only. No public exploitation evidence is included in the bundle.
Mitigation direction
- Upgrade TensorFlow to a fixed release for the deployed branch.
- Use TensorFlow 2.6.0 or later where feasible.
- For maintained older branches, use 2.5.1, 2.4.3, or 2.3.4 as applicable.
- Review vendor guidance before relying on compensating controls.
- Limit untrusted users' ability to run arbitrary TensorFlow operations until patched.
Validation and detection
- Inventory TensorFlow versions in applications, notebooks, containers, and ML jobs.
- Compare installed versions against the affected ranges in the advisory.
- Prioritize shared or user-driven ML execution environments.
- Confirm patched versions are deployed after remediation.
- Check dependency lockfiles and container images for stale TensorFlow packages.
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
Use the exact CWE identifier as the starting point before reviewing related ATT&CK behavior. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupCVE-2021-37653 mapping review
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Open ATT&CK lookup- Severity
- Medium
- CVSS
- 5.5 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
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:L/PR:L/UI:N/S:U/C:N/I:N/A:H1.83.6Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
5.5MediumVector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qjj8-32p7-h289CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/ac117ee8a8ea57b73d34665cdf00ef3303bc0b11CVE 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.