Security readout for executives and security teams
Plain-English summary
CVE-2021-37640 can make TensorFlow crash when sparse reshape input is malformed. The business impact is availability: affected ML jobs or services using TensorFlow may terminate unexpectedly if reachable by a low-privileged local user or workload submitter.
Executive priority
Treat as a moderate operational reliability issue. It is not described as data theft or remote takeover, but it can disrupt ML workloads where untrusted or low-privileged users can influence TensorFlow operations.
Technical view
TensorFlow's tf.raw_ops.SparseReshape could call reshape logic without confirming input and target shapes both had non-zero element counts. The reshape functor then divided by target dimensions, causing an integer divide-by-zero exception. Sources identify CWE-369 and CVSS 5.5 with high availability impact only.
Likely exposure
Exposure is most likely in TensorFlow deployments before the fixed 2.5.1 or 2.6.0 releases, especially shared ML platforms, notebooks, batch systems, or services where users can influence tensors or TensorFlow ops. The source bundle does not identify non-TensorFlow products.
Exploitation context
No KEV listing is provided, and the supplied sources do not claim active exploitation. The CVSS vector indicates local access, low attack complexity, low privileges, no user interaction, and availability impact without confidentiality or integrity impact.
Researcher notes
The root cause is missing zero-element shape validation before invoking sparse reshape logic. The public fix is commit 4923de56ec94fff7770df259ab7f2288a74feb41. Evidence is sufficient for affected component and remediation direction, but the bundle provides no real-world exploitation evidence.
Mitigation direction
- Upgrade TensorFlow to a fixed release: 2.5.1, 2.6.0, or later.
- Prioritize shared ML environments and services accepting user-controlled tensor inputs.
- If upgrade is delayed, restrict untrusted access to TensorFlow workloads using SparseReshape.
- Review the TensorFlow advisory for exact branch and package guidance.
Validation and detection
- Inventory TensorFlow package versions across services, notebooks, images, and training workers.
- Confirm no deployed environment remains on an affected TensorFlow version.
- Identify code paths or jobs using tf.raw_ops.SparseReshape or sparse reshape wrappers.
- Check dependency locks and container images for stale TensorFlow builds.
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-2021-37640 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-95xm-g58g-3p88CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41CVE 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.