Security readout for executives and security teams
Plain-English summary
CVE-2021-29605 is a TensorFlow Lite memory allocation flaw. A specially crafted model can make an internal size calculation overflow, causing invalid memory allocation and a crash or integrity impact. Business risk is highest where affected TensorFlow/TFLite versions process models from users, partners, or other untrusted sources.
Executive priority
Treat as high priority for ML-enabled products that accept external models or run TensorFlow Lite in production. It is less urgent for isolated research environments with controlled models, but still merits planned upgrade because fixed releases are available and the impact includes availability and integrity.
Technical view
The TFLite allocator for TFLiteIntArray can overflow an int when calculating allocation size. The overflow may become a negative value passed into malloc, followed by dereferencing an invalid pointer through ret->size. The CVSS 3.1 score is 7.1, with local attack vector, low complexity, low privileges, no user interaction, and high integrity and availability impact.
Likely exposure
Exposure is likely in systems using TensorFlow versions before 2.1.4, 2.2.0 before 2.2.3, 2.3.0 before 2.3.3, or 2.4.0 before 2.4.2, especially if they load TensorFlow Lite models from untrusted or semi-trusted sources.
Exploitation context
The source bundle does not indicate known active exploitation, and KEV is false. The advisory describes attacker-controlled crafted models as the triggering condition. Exploitation appears local by CVSS, requiring access sufficient to supply or influence a model consumed by an affected TensorFlow Lite runtime.
Researcher notes
Focus validation on version reachability and model ingestion trust boundaries. The provided evidence supports an integer overflow in TFLiteIntArray allocation and a fixed upstream commit. It does not support claims of remote network exploitation, public weaponization, or confirmed active exploitation.
Mitigation direction
- Upgrade TensorFlow to 2.5.0 or a fixed supported branch release.
- Use 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where branch constraints apply.
- Inventory applications and embedded runtimes that include TensorFlow Lite.
- Avoid loading untrusted TensorFlow Lite models until fixed versions are deployed.
- Check TensorFlow vendor guidance for any environment-specific remediation notes.
Validation and detection
- Confirm deployed TensorFlow versions against package manifests, lockfiles, containers, and SBOMs.
- Identify services or apps that parse or execute TensorFlow Lite models.
- Verify production builds include the fixed TensorFlow release or backported commit.
- Review model ingestion paths for user, partner, or external supply sources.
- Track crash or integrity anomalies around TensorFlow Lite model loading.
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-2021-29605 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-jf7h-7m85-w2v2CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5CVE 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.