Security readout for executives and security teams
Plain-English summary
CVE-2021-37677 is a TensorFlow denial-of-service flaw. Invalid arguments to Dequantize shape inference can crash the process with a segmentation fault. It does not indicate data theft or code execution, but it can disrupt ML workloads that process attacker-controlled inputs.
Executive priority
Treat as a moderate reliability risk. Patch on normal security maintenance timelines, sooner for shared ML platforms or services that process untrusted ML inputs where a crash could affect customers or operations.
Technical view
TensorFlow’s shape inference for tf.raw_ops.Dequantize failed to validate axis before using it to determine minmax_rank and access tensor dimensions. Unexpected axis values could trigger a segfault. The issue is CWE-20 with CVSS 3.1 score 5.5 and availability impact only.
Likely exposure
Exposure is mainly affected TensorFlow deployments using vulnerable versions where a local low-privileged actor, or an application path reachable by such input, can supply invalid Dequantize arguments. Listed affected ranges include TensorFlow before 2.3.4, 2.4.x before 2.4.3, and 2.5.x before 2.5.1.
Exploitation context
The CVSS vector is local, low complexity, low privilege, no user interaction, with high availability impact. The source bundle does not show CISA KEV listing or other evidence of active exploitation.
Researcher notes
The root cause is missing validation of axis in TensorFlow array_ops.cc shape inference for Dequantize. The advisory states the fix is commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764 and is included or cherry-picked into named fixed releases.
Mitigation direction
- Upgrade TensorFlow to 2.6.0 or a supported fixed maintenance release.
- Use TensorFlow 2.5.1, 2.4.3, or 2.3.4 where applicable.
- Check TensorFlow’s advisory for any branch-specific guidance.
- Restrict untrusted model or operation inputs until upgraded.
- Prioritize systems where crashes affect production ML services.
Validation and detection
- Inventory TensorFlow versions across production, CI, notebooks, and containers.
- Confirm affected ranges are not present in deployed environments.
- Review ML services that accept externally supplied models or operation parameters.
- Verify runtime dependency lockfiles resolve to fixed TensorFlow releases.
- Document whether Dequantize-related inputs can cross trust boundaries.
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
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
CWE-20: Exact CWE lookup
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Open ATT&CK lookupCVE-2021-37677 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-qfpc-5pjr-mh26CVE reference · x_refsource_CONFIRM
- https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764CVE 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.
Improper Input Validation
Improper Input Validation represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.