Security readout for executives and security teams
Plain-English summary
A user who can run TensorFlow code on an affected system can crash a TensorFlow process by supplying negative values to a boosted trees quantile resource operation. This is a denial-of-service issue, not a data theft or code execution issue in the provided sources.
Executive priority
Treat this as a moderate availability risk for shared ML infrastructure. It does not indicate data compromise, but it can disrupt jobs or services if affected TensorFlow workloads are exposed to low-privileged users.
Technical view
The vulnerable TensorFlow kernel failed to validate that num_streams values were non-negative. Negative signed integers were implicitly converted to large unsigned sizes during memory reservation, causing a standard-library crash. The issue is tracked as CWE-681 with CVSS 5.5, local attack vector, low complexity, and low privileges required.
Likely exposure
Exposure is most likely in ML platforms, notebooks, batch jobs, or shared compute environments running affected TensorFlow versions where users can execute TensorFlow workloads. Systems not using TensorFlow, or already on patched releases, are not indicated as affected by the provided sources.
Exploitation context
The source bundle does not report active exploitation, and the CVE is not listed as KEV. Exploitation requires local low-privileged ability to run or influence TensorFlow operations. The expected impact is process crash and availability loss.
Researcher notes
Evidence supports a signed-to-unsigned conversion flaw in boosted_trees_create_quantile_stream_resource. The provided sources identify patched versions and the fixing commit, but do not provide evidence of exploitation in the wild or broader product impact beyond TensorFlow.
Mitigation direction
- Upgrade to TensorFlow 2.6.0 or patched supported releases 2.5.1, 2.4.3, or 2.3.4.
- Prioritize shared ML services where untrusted users can run TensorFlow workloads.
- Restrict access to affected TensorFlow execution environments until patched.
- Review TensorFlow advisory GHSA-gf88-j2mg-cc82 for vendor guidance.
Validation and detection
- Inventory TensorFlow versions in applications, notebooks, containers, and ML job images.
- Flag versions >=2.5.0 <2.5.1, >=2.4.0 <2.4.3, and <2.3.4.
- Check whether boosted trees TensorFlow operations are reachable in shared workloads.
- Confirm deployed builds include commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992 or later patched releases.
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-681: 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-2021-37661 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
- 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:N/I:N/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:N/A:H1.83.6Primary 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:N/I:N/A:H
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf88-j2mg-cc82CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992CVE 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.
Incorrect Conversion between Numeric Types
Incorrect Conversion between Numeric Types represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.