Security readout for executives and security teams
Plain-English summary
TensorFlow sparse reduction operations can read or access heap memory outside expected tensor bounds. In ML systems that accept attacker-controlled tensors or model inputs, this could expose data, corrupt results, or crash the process. The issue is high severity but sources do not show known active exploitation.
Executive priority
Prioritize remediation for ML services or pipelines that process untrusted data. The business risk is process crash or data exposure inside TensorFlow workloads, not a broadly remote internet worm scenario based on the provided sources.
Technical view
CVE-2021-37635 affects TensorFlow sparse reduction code that failed to validate reduction group overflow and corresponding index bounds. The advisory maps this to CWE-125 with CVSS 7.3. Patched code was committed and released through TensorFlow 2.6.0 and supported cherrypicks.
Likely exposure
Exposure is most likely where TensorFlow versions before the fixed releases process untrusted or semi-trusted sparse tensor data. Listed affected ranges include TensorFlow 2.5.0 before 2.5.1, 2.4.x before 2.4.3, and versions before 2.3.4.
Exploitation context
The CVSS vector indicates local attack, low complexity, low privileges, and no user interaction. The provided bundle says KEV is false and includes no evidence of active exploitation or public weaponization. Treat exploitation status as not established from these sources.
Researcher notes
Focus review on sparse_reduce_op.cc validation around reduction groups and input tensor index bounds. The source evidence identifies heap out-of-bounds access and CWE-125, but does not provide exploit maturity, bypass notes, or operational indicators.
Mitigation direction
- Upgrade TensorFlow to 2.6.0 or a later fixed release.
- For supported older branches, use TensorFlow 2.5.1, 2.4.3, or 2.3.4.
- Prioritize systems processing untrusted sparse tensor inputs.
- Check TensorFlow vendor guidance before relying on compensating controls.
Validation and detection
- Inventory TensorFlow versions in applications, containers, notebooks, and training images.
- Flag versions matching the affected ranges listed in the advisory.
- Confirm fixed versions or commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750 are present.
- Review ML entry points that accept externally supplied sparse tensors.
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 lookupCVE-2021-37635 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-cgfm-62j4-v4rfCVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750CVE 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.