Security readout for executives and security teams
Plain-English summary
This TensorFlow flaw can crash a process when specific 3D convolution backpropagation operations receive empty tensors. The business impact is denial of service, not data theft or code execution. Risk is mainly for ML systems where users or tenants can influence tensor shapes or model execution paths on affected TensorFlow versions.
Executive priority
Treat this as a low-priority availability fix unless affected TensorFlow workloads are multi-tenant or exposed to untrusted model inputs. Patch during normal maintenance, but address shared ML platforms sooner because a crash could disrupt other users.
Technical view
`tf.raw_ops.Conv3DBackprop*` did not validate non-empty input tensors before calculating shard size. A zero divisor could cause a division-by-zero crash. Sources rate it low severity with CVSS 2.5, local attack vector, high complexity, low privileges, and low availability impact only.
Likely exposure
Exposure is most plausible in training, research, notebook, or ML-serving environments running affected TensorFlow versions and accepting user-controlled tensor sizes, models, or computation graphs. Systems that do not expose TensorFlow execution to untrusted users are less likely to be affected.
Exploitation context
The source bundle does not show KEV listing or active exploitation. The advisory states an attacker who controls input sizes can trigger denial of service. Evidence supports crash-oriented abuse only, not privilege escalation, data compromise, or remote code execution.
Researcher notes
The bug is narrowly scoped to TensorFlow `Conv3DBackprop*` operations and empty tensor handling. Available sources identify CWE-369 and a division-by-zero denial of service. No source in the bundle supports broader impact or active exploitation claims.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a listed patched maintenance release.
- Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where applicable.
- Restrict untrusted control over tensor shapes, models, and raw operation execution.
- Apply vendor guidance for unsupported or pinned TensorFlow deployments.
- Prioritize shared ML platforms where one user can affect others.
Validation and detection
- Inventory TensorFlow versions in applications, notebooks, containers, and training images.
- Check dependency lockfiles and runtime package versions for affected ranges.
- Identify endpoints or jobs accepting user-controlled tensor dimensions or uploaded models.
- Confirm patched TensorFlow versions are deployed in production and CI images.
- Review monitoring for repeated TensorFlow worker crashes or availability errors.
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-2021-29522 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-c968-pq7h-7fxvCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3faCVE 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.