Security readout for executives and security teams
Plain-English summary
A maliciously crafted TensorFlow Lite model can make affected TensorFlow versions read beyond valid heap memory while processing Split_V. The published score is high because successful triggering can affect integrity and availability. The main business risk is in systems that accept or run TFLite models from users, partners, marketplaces, or automated pipelines.
Executive priority
Treat this as a high-priority dependency update for ML systems that run TFLite models. Urgency is highest where external model files are accepted. There is no provided evidence of active exploitation, so prioritization should be risk-based rather than emergency-driven.
Technical view
CVE-2021-29606 is a heap out-of-bounds read in TensorFlow Lite Split_V. If axis_value is outside 0 to NumDimensions(input), SizeOfDimension can access outside the tensor shape array. The advisory says the fix is in TensorFlow 2.5.0 and cherry-picked to supported 2.4.2, 2.3.3, 2.2.3, and 2.1.4 releases.
Likely exposure
Exposure is most likely where affected TensorFlow versions process TFLite models, especially if models can come from untrusted users, third parties, CI pipelines, or edge/mobile update channels.
Exploitation context
The source bundle does not show CISA KEV listing or cited evidence of active exploitation. The CVSS vector indicates local attack, low privileges, no user interaction, unchanged scope, no confidentiality impact, and high integrity and availability impact.
Researcher notes
The root condition is missing validation of axis_value before SizeOfDimension uses it against tensor shape metadata. Research should focus on affected TensorFlow Lite Split_V call paths and whether deployed products permit attacker-influenced model files. Do not assume products beyond TensorFlow without additional evidence.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a patched supported branch release.
- Use 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where those branches are required.
- Restrict processing of untrusted TFLite models until patched.
- Check downstream vendor guidance for embedded, packaged, or forked TensorFlow builds.
- Prioritize systems that ingest models from users, partners, or automated supply chains.
Validation and detection
- Inventory TensorFlow versions in applications, containers, SBOMs, and build manifests.
- Identify services, mobile apps, and edge components that load TFLite models.
- Confirm affected ranges are absent or replaced by patched releases.
- Verify downstream builds include TensorFlow commit ae2daeb45abfe2c6dda539cf8d0d6f653d3ef412 or vendor-equivalent fixes.
- Regression test model-loading paths after upgrade for compatibility and stability.
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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Open ATT&CK lookupCVE-2021-29606 mapping review
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Open ATT&CK lookup- Severity
- High
- CVSS
- 7.1 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:L/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:L/PR:L/UI:N/S:U/C:N/I:H/A:H1.85.2Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
7.1HighVector: CVSS:3.1/AV:L/AC:L/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-h4pc-gx2w-f2xvCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/ae2daeb45abfe2c6dda539cf8d0d6f653d3ef412CVE 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 Read
Out-of-bounds Read represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.