Security readout for executives and security teams
Plain-English summary
A flaw in TensorFlow’s RaggedGather operation can make TensorFlow read beyond heap memory when given invalid ragged tensor arguments. This can expose data and disrupt ML workloads, but the source data indicates a local, low-privilege attack vector, not unauthenticated internet exploitation.
Executive priority
Treat as a high-priority dependency upgrade for ML systems that process untrusted inputs or allow shared local execution. It is less urgent than internet-facing remote code execution, but still material because confidentiality and availability impact are rated high.
Technical view
CVE-2021-37641 is a CWE-125 heap out-of-bounds read in tf.raw_ops.RaggedGather. The implementation read a tensor shape dimension before confirming rank >= 1 and failed to reject an empty params_nested_splits tensor list. CVSS is 7.3 high.
Likely exposure
Exposure is most likely in applications, notebooks, services, or pipelines running vulnerable TensorFlow versions that allow local users, plugins, model code, or request-handling logic to invoke RaggedGather with untrusted or malformed inputs.
Exploitation context
The supplied CVSS vector is AV:L/PR:L/UI:N, so sources indicate local access and low privileges are required. CISA KEV is false in the bundle, and no cited source states active exploitation.
Researcher notes
The key validation issue is missing input-shape and empty-list checks before heap-backed shape access. The public advisory names commit a2b743f6017d7b97af1fe49087ae15f0ac634373 as the patch. Evidence does not establish weaponized exploitation.
Mitigation direction
- Upgrade TensorFlow to 2.6.0 or later where practical.
- If pinned, use patched backports 2.5.1, 2.4.3, or 2.3.4.
- Remove or isolate unsupported TensorFlow versions below the patched ranges.
- Restrict execution of untrusted model code or tensor operations.
- Check TensorFlow advisory guidance for version-specific constraints.
Validation and detection
- Inventory TensorFlow versions across source, containers, notebooks, and deployed services.
- Compare installed versions against the affected ranges in the source bundle.
- Identify code paths using tf.raw_ops.RaggedGather or ragged tensor gather behavior.
- Verify runtime images actually load the patched TensorFlow package.
- Confirm no legacy dependency pins force vulnerable TensorFlow releases.
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-125: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-37641 mapping review
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Open ATT&CK lookup- Severity
- High
- CVSS
- 7.3 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:L/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:H/I:L/A:H1.85.5Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
7.3HighVector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:H
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c8h-vvrj-w2p8CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373CVE 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.