Security readout for executives and security teams
Plain-English summary
CVE-2021-29613 affects TensorFlow’s CTCLoss operation. A user able to run or influence TensorFlow workloads could trigger an out-of-bounds heap read due to incomplete validation. The practical concern is integrity and availability impact in ML training or serving environments using affected TensorFlow versions.
Executive priority
Schedule remediation in the normal security patch cycle, faster for shared or user-facing ML infrastructure. There is no source-provided evidence of active exploitation, but affected versions can create integrity and availability risk when untrusted workloads are allowed.
Technical view
The issue is incomplete validation in `tf.raw_ops.CTCLoss`, allowing an out-of-bounds heap read. CVSS 3.1 is 6.3 with local attack vector, high complexity, low privileges, no user interaction, and high integrity and availability impact. TensorFlow fixed it in 2.5.0 and planned supported-branch backports.
Likely exposure
Exposure is most likely where affected TensorFlow versions run untrusted or user-influenced ML inputs, models, notebooks, training jobs, or service workloads. Confirm versions matching `<2.1.4`, `2.2.0-2.2.2`, `2.3.0-2.3.2`, or `2.4.0-2.4.1`.
Exploitation context
The source bundle does not show CISA KEV listing or cited evidence of active exploitation. Attack requirements are non-trivial: local access, low privileges, and high complexity. Treat this as important for shared ML platforms and lower urgency for isolated trusted-only workloads.
Researcher notes
Evidence is limited to the CVE record, TensorFlow advisory, and fix commits. The advisory names the vulnerable operation and affected version branches but the bundle does not provide exploit details or runtime detection indicators. Validate by version and workload reachability.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or later where feasible.
- For supported older branches, use 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
- Check TensorFlow’s advisory before relying on branch-specific remediation.
- Reduce ability for untrusted users to run arbitrary TensorFlow workloads.
- Prioritize shared notebooks, model-serving hosts, CI training runners, and multi-tenant ML environments.
Validation and detection
- Inventory TensorFlow package versions across applications, containers, notebooks, and training images.
- Flag versions matching the affected ranges from the CVE source bundle.
- Search code and model pipelines for use of `tf.raw_ops.CTCLoss`.
- Confirm upgraded environments use a fixed TensorFlow release.
- Review ML platforms for untrusted users or inputs reaching TensorFlow execution paths.
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
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CWE-665: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29613 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
- Medium
- CVSS
- 6.3 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:H
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:H/A:H15.2Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
6.3MediumVector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:H/A:H
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vvg4-vgrv-xfr7CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864cCVE reference · x_refsource_MISC
- https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005bCVE 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.