Security readout for executives and security teams
Plain-English summary
CVE-2021-29557 is a low-severity TensorFlow denial-of-service issue. A crafted condition involving an empty tensor can trigger a divide-by-zero runtime error in SparseMatMul, crashing the affected operation. It does not indicate data theft or code execution in the provided sources.
Executive priority
Schedule remediation through normal dependency maintenance unless SparseMatMul is exposed to untrusted users in a critical service. Prioritize higher if crashes could interrupt customer-facing ML workloads or shared compute environments.
Technical view
TensorFlow tf.raw_ops.SparseMatMul can reach Eigen code where an empty b tensor causes division by zero, producing a floating point exception. The source describes local, high-complexity, low-privilege exploitation with no confidentiality or integrity impact and low availability impact.
Likely exposure
Exposure is most likely in ML workloads, notebooks, containers, or services running affected TensorFlow versions and invoking SparseMatMul with user-influenced tensor inputs. The sources do not support broad remote exposure by default.
Exploitation context
The CVSS vector is local, high complexity, low privileges, and no user interaction. The source bundle does not identify active exploitation, and CISA KEV status is false. Treat this as a targeted availability risk in specific TensorFlow execution paths.
Researcher notes
The evidence supports CWE-369 division by zero and denial of service only. The provided fix reference is a TensorFlow commit, with patched releases named in the advisory text. Do not infer broader TensorFlow API impact beyond SparseMatMul from these sources.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a patched supported branch release.
- Use 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where those branches are required.
- Restrict untrusted access to jobs or services that can reach SparseMatMul.
- Check the TensorFlow advisory before rollout for branch-specific guidance.
Validation and detection
- Inventory TensorFlow versions in production, notebooks, containers, and training images.
- Identify workloads using tf.raw_ops.SparseMatMul or dependent sparse matrix operations.
- Confirm deployed dependencies resolve to a patched TensorFlow release.
- Retest affected ML workflows for availability after updating.
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-2021-29557 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-xw93-v57j-fcghCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/7f283ff806b2031f407db64c4d3edcda8fb9f9f5CVE 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.