Security readout for executives and security teams
Plain-English summary
CVE-2021-29526 is a low-severity TensorFlow denial-of-service issue. A caller can trigger a divide-by-zero condition in Conv2D, potentially crashing or disrupting a local ML process. It does not indicate data theft or code execution in the provided sources.
Executive priority
Treat this as routine patch hygiene unless affected TensorFlow workloads are shared, multi-tenant, or reliability-critical. Prioritize during normal dependency maintenance, with faster action for exposed ML platforms where users submit models or tensors.
Technical view
TensorFlow tf.raw_ops.Conv2D divided by a caller-controlled value in conv_ops.cc, causing CWE-369 division by zero. CVSS 3.1 is 2.5: local attack vector, high complexity, low privileges, no user interaction, and low availability impact only.
Likely exposure
Exposure is most likely in systems running affected TensorFlow versions where local or low-privileged users can influence Conv2D inputs. Internet exposure is not established by the sources unless an application forwards untrusted inputs into this operation.
Exploitation context
The source bundle marks KEV as false, and no cited source states active exploitation. The CVSS vector indicates local access, low privileges, high complexity, and availability-only impact.
Researcher notes
Focus review on affected TensorFlow versions and Conv2D call paths accepting caller-controlled shape or parameter values. Evidence supports denial of service via divide-by-zero only; do not assume remote exploitability, confidentiality impact, or code execution from these sources.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a fixed supported branch release.
- Use 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where pinned to those branches.
- Check TensorFlow vendor guidance for any later supported fixed versions.
- Limit untrusted users from controlling raw Conv2D parameters in shared ML environments.
Validation and detection
- Inventory TensorFlow versions in dependency locks, runtime images, and notebooks.
- Compare versions against the affected ranges in the CVE source bundle.
- Identify services or jobs exposing Conv2D input control to low-privileged users.
- Confirm production runtimes use fixed TensorFlow builds, not only updated source files.
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-2021-29526 mapping review
Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.
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-4vf2-4xcg-65cxCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42bCVE 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.