Security readout for executives and security teams
Plain-English summary
CVE-2021-29603 is a low-severity TensorFlow Lite flaw where a maliciously crafted model can cause a heap out-of-bounds write in ArgMin/ArgMax handling. Business urgency is mainly for systems that load untrusted or user-supplied TFLite models.
Executive priority
Treat as routine but real risk management. Prioritize patching where TFLite models cross trust boundaries, especially product features accepting uploaded or third-party models. Lower priority is reasonable for isolated systems using only vetted internal models.
Technical view
TensorFlow Lite ArgMin/ArgMax mishandles an out-of-range axis_value, which can lead to writes past output_dims->data. It is CWE-787 with CVSS 3.1 score 2.5. Affected ranges include TensorFlow <2.1.4, 2.2.0 to <2.2.3, 2.3.0 to <2.3.3, and 2.4.0 to <2.4.2.
Likely exposure
Exposure is most likely in applications, pipelines, or edge products that execute TFLite models from users, partners, downloaded files, or other untrusted sources. Systems using only trusted, internally generated models have lower practical exposure.
Exploitation context
The source bundle does not show known active exploitation, and KEV status is false. The advisory describes a specially crafted TFLite model as the trigger, with local, high-complexity, low-privilege CVSS conditions and availability-only impact.
Researcher notes
The issue is in TensorFlow Lite ArgMin/ArgMax dimension handling. The cited fix commit is available, but the provided sources do not establish exploitability beyond availability impact or active exploitation. Avoid assuming broader TensorFlow API exposure without local code review.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or later where feasible.
- Apply patched supported versions 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
- Restrict loading of untrusted TFLite models until patched.
- Check TensorFlow vendor guidance for environment-specific remediation details.
Validation and detection
- Inventory TensorFlow and TensorFlow Lite versions in applications and build images.
- Identify services that load TFLite models from external or user-controlled sources.
- Confirm deployed versions are outside the listed affected ranges.
- Review dependency lockfiles and container images for vulnerable TensorFlow packages.
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-787: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29603 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-crch-j389-5f84CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/c59c37e7b2d563967da813fa50fe20b21f4da683CVE 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.
Out-of-bounds Write
Out-of-bounds Write represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.