Security readout for executives and security teams
Plain-English summary
TensorFlow versions in the listed ranges can read memory out of bounds when certain element-wise binary operations receive mismatched input sizes. A user able to run or influence TensorFlow computation on an affected system may cause crashes, undefined behavior, and possible information exposure. The vendor fixed it in TensorFlow 2.6.0 and supported patch releases.
Executive priority
Prioritize remediation for shared ML platforms, multi-user notebooks, and services processing untrusted or customer-controlled ML inputs. The issue has high confidentiality and availability impact, but the available evidence does not show confirmed active exploitation. Standard dependency upgrade and image rebuild processes should address most exposure.
Technical view
Binary cwise operations that do not require broadcasting failed to verify equal element counts. The implementation assumed both tensors matched; mismatches could lead the Eigen functor to bind a null pointer, causing heap out-of-bounds reads and undefined behavior. The issue maps to CWE-125 and CWE-476, with CVSS 3.1 score 7.3.
Likely exposure
Exposure is most likely in applications, notebooks, services, or batch jobs using vulnerable TensorFlow versions: below 2.3.4, 2.4.0 to before 2.4.3, or 2.5.0 to before 2.5.1. Risk increases where low-privileged users or tenants can supply TensorFlow graphs, tensors, models, or workloads.
Exploitation context
The CVSS vector is local, low complexity, low privileges, and no user interaction. The provided sources do not report active exploitation, and the CVE is not marked KEV. Treat this as a high-impact local or workload-influenced issue, not as a confirmed internet-exploited vulnerability.
Researcher notes
The vulnerable path is in TensorFlow binary cwise operations without broadcasting. The advisory attributes impact to unchecked element-count assumptions, heap out-of-bounds reads, null pointer reference binding, and undefined behavior. The fixed commit is 93f428fd1768df147171ed674fee1fc5ab8309ec; avoid deriving exploit procedures from the patch in operational reports.
Mitigation direction
- Upgrade TensorFlow to 2.6.0 or later where feasible.
- For older supported branches, use 2.5.1, 2.4.3, or 2.3.4.
- Rebuild containers, notebooks, and runtime images containing vulnerable TensorFlow packages.
- Pin dependency versions to prevent reintroducing affected TensorFlow builds.
- Check TensorFlow advisory guidance for environment-specific remediation details.
Validation and detection
- Inventory TensorFlow versions in source manifests, lockfiles, containers, and deployed ML runtimes.
- Flag versions below 2.3.4, 2.4.0-2.4.2, and 2.5.0.
- Confirm runtime images and notebooks load a fixed TensorFlow version.
- Review user-controlled ML workload paths for affected TensorFlow execution.
- Run existing regression tests after upgrading TensorFlow.
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
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
CWE-125: Exact CWE lookup
Use the exact CWE identifier as the starting point before reviewing related ATT&CK behavior. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupCWE-476: Exact CWE lookup
Use the exact CWE identifier as the starting point before reviewing related ATT&CK behavior. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupCVE-2021-37659 mapping review
Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.
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-q3g3-h9r4-prrcCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/93f428fd1768df147171ed674fee1fc5ab8309ecCVE 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.
NULL Pointer Dereference
NULL Pointer Dereference represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.