Security readout for executives and security teams
Plain-English summary
CVE-2021-29579 is a TensorFlow memory safety bug in MaxPoolGrad. A local, low-privileged user or job that can run affected TensorFlow code may cause a crash or limited availability impact. The sources do not support data theft, integrity impact, or active exploitation.
Executive priority
Treat this as routine patch management unless affected TensorFlow is exposed in shared or untrusted ML execution environments. The business risk is mainly service disruption, not confirmed compromise or data exposure.
Technical view
TensorFlow’s tf.raw_ops.MaxPoolGrad did not validate all indices before accessing arrays. input_backprop_flat access was bounds-checked, but out_backprop_flat indexing could go out of bounds, causing heap buffer overflow behavior. Affected versions are below fixed 2.1.4, 2.2.3, 2.3.3, and 2.4.2 releases.
Likely exposure
Exposure is limited to environments running the listed vulnerable TensorFlow versions and allowing users or workloads to invoke MaxPoolGrad with controlled inputs. This is most relevant to shared ML platforms, notebooks, CI jobs, or services executing untrusted TensorFlow workloads.
Exploitation context
The CVSS vector requires local access, low privileges, high attack complexity, and no user interaction. CISA KEV status is false in the provided bundle, and no cited source reports active exploitation. Impact is described as availability-only and low.
Researcher notes
The root issue is missing validation for out_backprop_flat indexing in MaxPoolGrad. The public advisory names the fixed release plan and the commit reference. Evidence does not establish practical exploitation beyond the stated local, high-complexity, low-availability CVSS profile.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a fixed supported branch release.
- Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where branch pinning is required.
- Update dependency lockfiles, containers, and ML runtime images using affected TensorFlow versions.
- Restrict execution of untrusted TensorFlow workloads until affected runtimes are patched.
- Check TensorFlow vendor guidance if unable to upgrade immediately.
Validation and detection
- Inventory deployed TensorFlow package versions across code, images, notebooks, and training environments.
- Confirm no runtime uses TensorFlow versions below the fixed branch releases.
- Check dependency manifests and container layers for transitive TensorFlow pins.
- Run normal ML regression tests after upgrading TensorFlow.
- Document any exception where an affected runtime remains in use.
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
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ATT&CK lookup starting points
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CWE-119: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29579 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-79fv-9865-4qcvCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7CVE 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.
Improper Restriction of Operations within the Bounds of a Memory Buffer
Improper Restriction of Operations within the Bounds of a Memory Buffer represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.