Security readout for executives and security teams
Plain-English summary
CVE-2021-37644 is a TensorFlow denial-of-service issue. A user who can run affected TensorFlow operations can pass a negative element count to TensorListReserve and crash the process. The reported impact is availability loss, not data theft or code execution.
Executive priority
Treat this as a moderate availability risk. Prioritize patching shared or production ML systems where one user or workload crash could interrupt service, training, or inference pipelines. It is lower urgency than remote code execution or data exposure issues.
Technical view
Affected TensorFlow versions fail to validate the num_elements input to tf.raw_ops.TensorListReserve. That value reaches std::vector.resize(), and a negative size can raise std::abort. The issue is classified as CWE-617 and has CVSS 3.1 score 5.5 with local, low-privilege exploitation assumptions.
Likely exposure
Exposure is most likely where affected TensorFlow versions are installed and local users, jobs, notebooks, or ML workloads can execute TensorFlow raw operations. Listed affected ranges are TensorFlow before 2.3.4, 2.4.0 before 2.4.3, and 2.5.0 before 2.5.1.
Exploitation context
The provided sources do not show active exploitation, and the CVE is not marked KEV. CVSS describes local access with low privileges and no user interaction. Practical impact is crashing a TensorFlow process, which can disrupt ML services or shared compute environments.
Researcher notes
The root cause is missing validation before resizing a C++ vector in TensorListReserve. The vendor identified commit 8a6e874437670045e6c7dc6154c7412b4a2135e2 as the patch. Evidence provided does not support confidentiality, integrity, or remote exploitation claims.
Mitigation direction
- Upgrade to TensorFlow 2.6.0 or a patched supported release.
- Use TensorFlow 2.5.1, 2.4.3, or 2.3.4 for affected maintained branches.
- Check the TensorFlow advisory for current vendor guidance.
- Avoid running untrusted TensorFlow workloads on affected versions until patched.
Validation and detection
- Inventory TensorFlow package versions across services, notebooks, images, and training workers.
- Confirm no deployed dependency falls within the affected version ranges.
- Review shared ML environments for users able to run arbitrary TensorFlow workloads.
- Verify patch adoption through dependency lockfiles, SBOMs, or image metadata.
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
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ATT&CK lookup starting points
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CWE-617: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-37644 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: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-27j5-4p9v-pp67CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2CVE 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.
Reachable Assertion
Reachable Assertion represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.