Security readout for executives and security teams
Plain-English summary
This issue affects TensorFlow Lite's concatenation handling. A specially crafted ML model can cause an integer overflow when valid TensorFlow dimensions are represented as smaller TFLite integers. The main business risk is integrity and availability impact in systems that process untrusted models with affected TensorFlow versions.
Executive priority
Treat as a moderate-priority supply-chain and ML-platform maintenance issue. Prioritize environments that process third-party models or automate model conversion, because impact is integrity and availability rather than data confidentiality.
Technical view
TFLite uses int for tensor dimensions while TensorFlow uses int64. During concatenation, crafted model dimensions can overflow int, affecting TensorFlow versions before listed patched releases. CVSS 3.1 is 6.3 with local attack vector, high complexity, low privileges, no user interaction, and high integrity and availability impact.
Likely exposure
Exposure is most likely in ML pipelines, mobile or edge apps, or services that convert or execute TFLite models using affected TensorFlow releases and accept models from users, partners, plugins, or automated supply-chain inputs.
Exploitation context
The source bundle does not show active exploitation, and KEV is false. Exploitation requires a crafted model and local attack conditions with high complexity and low privileges, so risk rises where model ingestion is not tightly controlled.
Researcher notes
The key design mismatch is TensorFlow int64 dimensions versus TFLite int dimensions. The advisory states valid TensorFlow models can trigger overflow after TFLite conversion. Evidence in the bundle supports affected versions and planned fixed releases, but not in-the-wild exploitation.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or the fixed supported branch release.
- Use 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where those branches are required.
- Restrict ingestion and conversion of untrusted ML models.
- Review vendor advisory and commit details for exact fixed behavior.
- Inventory TensorFlow and TFLite use across build, training, and runtime environments.
Validation and detection
- Check deployed TensorFlow versions against the affected ranges.
- Identify workflows that convert TensorFlow models into TFLite format.
- Confirm whether externally supplied models can reach TFLite concatenation processing.
- Verify patched versions are present in application and build artifacts.
- Document compensating controls for any systems awaiting upgrade.
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-190: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-29601 mapping review
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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-9c84-4hx6-xmm4CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73CVE 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.
Integer Overflow or Wraparound
Integer Overflow or Wraparound represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.