Security readout for executives and security teams
Plain-English summary
A TensorFlow raw operation accepted invalid axis values, causing memory access before an expected heap buffer. An attacker with local ability to run TensorFlow code could affect integrity or availability. The vendor rated this low severity and provided fixed releases for supported branches.
Executive priority
Treat as a low-priority patching item unless TensorFlow runs in shared or semi-trusted execution environments. It should be handled through normal dependency maintenance, with faster action for platforms that let multiple users submit ML workloads.
Technical view
CVE-2021-29610 affects tf.raw_ops.QuantizeAndDequantizeV2. Its axis validation mixed conditions incorrectly, allowing axis values below -1 to pass and cause heap underflow. Affected TensorFlow branches are before 2.1.4, 2.2.3, 2.3.3, and 2.4.2.
Likely exposure
Exposure is most likely in applications, notebooks, ML pipelines, or services running affected TensorFlow versions where local users or workloads can execute TensorFlow operations. Remote exposure is not established by the provided sources.
Exploitation context
The CVSS vector is local, high complexity, low privileges, and no user interaction. The source bundle says CISA KEV is false, and the cited sources do not report active exploitation.
Researcher notes
The root issue is incorrect OP_REQUIRES validation in quantize_and_dequantize_op.cc. The vendor commit corrects validation for invalid axis handling. Avoid assuming broader TensorFlow impact beyond the listed package and versions.
Mitigation direction
- Upgrade to TensorFlow 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 applicable.
- Prioritize shared ML environments where less-trusted users can run TensorFlow workloads.
- Check vendor advisory and release guidance before applying branch-specific fixes.
Validation and detection
- Inventory deployed TensorFlow versions across applications, notebooks, images, and CI environments.
- Flag TensorFlow versions matching the affected ranges in the source bundle.
- Review code paths that allow users or jobs to invoke raw TensorFlow operations.
- Confirm remediation by verifying the deployed TensorFlow version is a fixed release.
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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Open ATT&CK lookupCVE-2021-29610 mapping review
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Open ATT&CK lookup- Severity
- Low
- CVSS
- 3.6 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/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:L/A:L12.5Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
3.6LowVector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:L
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mq5c-prh3-3f3hCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9CVE 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 Initialization
Improper Initialization represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.