Security readout for executives and security teams
Plain-English summary
This is a low-severity TensorFlow denial-of-service flaw. A user who can run affected TensorFlow code can trigger a divide-by-zero crash path in a convolution backpropagation operation. The main business risk is disruption of ML jobs or services, not data theft or privilege escalation.
Executive priority
Handle through normal patch management unless TensorFlow workloads are exposed to untrusted internal users or shared ML platforms. The issue is availability-focused and low severity, but stale ML dependencies should still be cleared from production and shared research environments.
Technical view
CVE-2021-29524 affects TensorFlow Conv2DBackpropFilter through a caller-controlled divisor used in a modulus operation. CVSS is 2.5, local, high complexity, low privileges, no confidentiality or integrity impact, and low availability impact. Fixed releases were planned for 2.5.0 and supported backports.
Likely exposure
Exposure is most plausible in environments running affected TensorFlow versions where users can execute TensorFlow ops, submit ML jobs, or influence graph parameters. The source does not identify affected hosted services, downstream products, or remote unauthenticated exposure.
Exploitation context
The source states an attacker can trigger the issue, but CISA KEV status is false and no provided source reports active exploitation. The CVSS vector indicates local access, low privileges, high attack complexity, and availability-only impact.
Researcher notes
The evidence identifies CWE-369 and a vulnerable modulus operation in TensorFlow conv_grad_shape_utils.cc. The bundle provides affected version ranges and the fixing commit, but no exploit telemetry, proof-of-concept details, or downstream product mapping.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or the fixed supported backport for your branch.
- Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where applicable.
- Check TensorFlow advisory guidance before choosing a legacy branch fix.
- Reduce untrusted access to TensorFlow job execution environments.
Validation and detection
- Inventory TensorFlow versions in application images, notebooks, training workers, and ML pipelines.
- Flag versions below 2.1.4, 2.2.3, 2.3.3, or 2.4.2 as affected.
- Confirm upgraded environments load the intended patched TensorFlow version.
- Review logs for unexplained TensorFlow job crashes in shared compute environments.
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-2021-29524 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-r4pj-74mg-8868CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404aCVE 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.