Security readout for executives and security teams
Plain-English summary
TensorFlow Lite has a memory read bug in affected TensorFlow versions. A local, low-privileged actor who can make affected code process malicious tensor dimensions could read heap memory outside the intended buffer. Sources do not indicate remote code execution or service disruption, but confidentiality impact is high.
Executive priority
Treat as a moderate confidentiality issue. Prioritize patching where TensorFlow Lite is embedded in distributed products or handles untrusted model content, but do not treat it as confirmed active exploitation based on current sources.
Technical view
In TFLite expand_dims.cc, a large negative axis can remain negative after normalization, pass validation, and make a loop read one element before input_dims.data. This is CWE-125 out-of-bounds read. TensorFlow fixed it in commit d94ffe08a65400f898241c0374e9edc6fa8ed257 and fixed releases.
Likely exposure
Exposure is most likely in applications, services, mobile apps, or embedded products using TensorFlow Lite from affected TensorFlow versions: >=2.5.0 <2.5.1, >=2.4.0 <2.4.3, or <2.3.4.
Exploitation context
The CVSS vector is local, low complexity, low privileges, and no user interaction, with high confidentiality impact. The provided sources do not report active exploitation, and CISA KEV status is false.
Researcher notes
Evidence supports a one-element heap out-of-bounds read in TFLite ExpandDims axis handling. Patch availability is clear. The bundle does not provide proof-of-concept details, exploitation in the wild, or product-specific downstream advisories.
Mitigation direction
- Upgrade to TensorFlow 2.6.0 or later where feasible.
- For supported older branches, upgrade to 2.5.1, 2.4.3, or 2.3.4.
- Apply TensorFlow vendor guidance and the referenced fix commit where release upgrades are constrained.
- Prioritize systems processing untrusted TFLite models or tensor metadata.
Validation and detection
- Inventory TensorFlow and TensorFlow Lite versions across applications, containers, mobile apps, and embedded builds.
- Confirm affected version ranges are not present in production or shipped artifacts.
- Check SBOMs and dependency lockfiles for vulnerable TensorFlow versions.
- Review whether any service loads externally supplied TFLite models or inputs.
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-125: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-37685 mapping review
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Open ATT&CK lookup- Severity
- Medium
- CVSS
- 5.5 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N
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:N/A:N1.83.6Primary CVE scoreVulnerability scoring details
Base CVSS 3.1 score
5.5MediumVector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c545-c4f9-rf6vCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257CVE 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.