BlackTreeCVE Intelligence
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Full vulnerability report · 2021
CVE-2021-37677High confidence

Missing validation in shape inference for `Dequantize` in TensorFlow

tensorflow · tensorflow

Official source article: GitHub GHSA-QFPC-5PJR-MH26 ↗. Check the applicable product and release in the original source.

5.5MediumCVSS 3.1
Recommended action
Scheduled

Medium technical severity with no CISA KEV confirmation; remediate through the normal risk-based patch cycle unless local exposure raises the priority.

Patch available
R
Operational reassessment

Published severity in operational context

Open reassessment dashboard →
Published severityMediumOperational priority:Low, lowered one band.downgradedsince 4 Aug 2024

Evidence used

  • No CISA KEV confirmation is currently recorded.
  • Exploitation requires an existing local or physical foothold with privileges.
  • EPSS is 0.15% for the current model date.

Compensating controls

  • Validate the affected product branch and deploy the verified fixed release.
  • Restrict local access and enforce least privilege on affected hosts.
  • Monitor vendor guidance and exploitation sources for a material change.

Verification

  1. Confirm that the asset runs tensorflow tensorflow and falls inside the recorded affected range.
  2. Verify the installed build against the product-specific fixed version after deployment.
  3. Validate exposure, authentication requirements and compensating controls in the actual environment.
  4. Reopen this reassessment when CVSS, KEV, EPSS, exploit evidence or remediation changes.
Mitigation target: No default targetRemediation target: Normal maintenance

This automated reassessment organises public evidence. It does not know asset exposure, business impact or control effectiveness and does not replace CVSS or a human risk decision.

Distribution package intelligence

Release-specific package status

Debian findings are scoped to the named distribution, release and source package. An absent finding does not mean a package is unaffected.

2 package states
Repository candidate not checked

A published vendor fix does not prove that a matching update is enabled and installable on a particular asset. Confirm the local package candidate before scheduling remediation.

Distribution releaseSource packageVendor stateFixed versionEvidence
Debian forkyforky · sourcetensorflowNot affectedDebian marks this release not affected (fixed-version marker 0).Not published in this feedDebian Security Tracker ↗Source updated 5 Oct 2026
Debian sidsid · sourcetensorflowNot affectedDebian marks this release not affected (fixed-version marker 0).Not published in this feedDebian Security Tracker ↗Source updated 5 Oct 2026
Optional official sources

National CERT insights
?CERT means Computer Emergency Response Team; CSIRT is the closely related term Computer Security Incident Response Team.

Choose official national sources for this report. Each advisory shows its original language. Your selection is remembered on this device and included in shared links.

Official European source

ENISA European Vulnerability Database

Official EUVD identifiers, advisory evidence and known-exploited context. Missing fields are not treated as evidence of low risk.

1 current
ENISA EUVD identifier

EUVD-2021-0403

No EUVD known-exploited evidence

ENISA has published the identifier mapping but no EUVD description has been stored yet.

EUVD state
Present in the current official mapping
Known exploitation
Not present in the current ENISA EUVD known-exploited dataset. This is not proof of no exploitation.
ENISA score
Not supplied in the stored EUVD record
Advisory evidence
No linked advisory details stored yet
Recommended actionScheduled

Medium technical severity with no CISA KEV confirmation; remediate through the normal risk-based patch cycle unless local exposure raises the priority.

Patch available
01

What, why and how

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

What

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Why

The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.

How

An attacker operating through local access may attempt exploitation with low privileges. If successful, the issue may disrupt the affected service.

What

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Why

The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.

How

An attacker operating through local access may attempt exploitation with low privileges. If successful, the issue may disrupt the affected service.

02

Exploit reality and attack path

CVSS severity, EPSS forecast probability, public exploit material and CISA-confirmed exploitation are separate signals.

Observed exploitation
?Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.
No confirmed evidence

No CISA KEV match was present at the last successful refresh. This means no confirmation from that source, not proof of no exploitation.

Public PoC / exploit material
?Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.
None recorded

No exploit-tagged reference or CISA SSVC proof-of-concept state is currently recorded. Research may still exist outside the structured feeds.

Likely attack path
local access → Improper Input Validation → disrupt the affected service
Attack surface
Local
Privileges required
Low: a basic authenticated account is required
User interaction
None
Attack complexity
Low: no specialised conditions are recorded
Security boundary
Unchanged: impact remains within the vulnerable component's security authority
Weakness
?CWE means Common Weakness Enumeration: a standard category for the underlying weakness.
CWE-20 ↗

CWE-20: Improper Input Validation. The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.

CVSS vector
?CVSS means Common Vulnerability Scoring System. The vector records the metric values used to calculate technical severity.
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Common Vulnerability Scoring System 3.1: the compact vector below is decoded into plain language.

AVLocalAttack vector: The attacker needs local access to the vulnerable system.ACLowAttack complexity: No specialised conditions are required beyond attacker-controlled input.PRLowPrivileges required: The attacker needs basic user-level privileges.UINoneUser interaction: No action by another user is required.SUnchangedScope: The security impact remains within the vulnerable component's authority.CNoneConfidentiality impact: No direct loss is represented by this metric.INoneIntegrity impact: No direct loss is represented by this metric.AHighAvailability impact: A successful attack can cause a major loss.
Post-exploitation / living off the land
The issue can support a local privilege or sandbox boundary transition; normal system utilities may then be available in the gained context.
Denial of serviceCWE-20
A

Official authority intelligence

Only matched European and national findings are included. Language selectors and unavailable sources are omitted.

Cyber Security Agency of Singapore · English · CSA-SB-20210818Security Bulletin 18 Aug 2021

The Cyber Security Agency of Singapore included this CVE in its official Security Bulletin 18 Aug 2021, published on 18 August 2021. Open the linked bulletin for the product, severity and reference information published in that issue.

Official advisory ↗
JVN iPedia · Japanese · JVNDB-2021-010023TensorFlow における入力で指定された数量の不適切な検証に関する脆弱性

TensorFlow には、入力で指定された数量の不適切な検証に関する脆弱性が存在します。

Official advisory ↗
03

Patch and workaround

Operational remediation based on structured source evidence.

Status
?Patch availability is based on structured fixed-version fields and authoritative update references. If no fix is verified, check the vendor advisory before making a change.
Patch available
Affected
tensorflow: >= 2.5.0, < 2.5.1, >= 2.4.0, < 2.4.3, < 2.3.4
Fixed
An authoritative update reference is available, but the fixed version is not recorded in the structured CVE fields. Check the linked vendor advisory for the applicable release.
Action
Review the linked authoritative reference and apply the recorded fixed release appropriate to the affected product branch.
Workaround
No verified workaround is recorded. Limit untrusted access and use least privilege until authoritative guidance is available.
04

Evidence and provenance

Published 12 Aug 2021 · Last source change 4 Aug 2024, 01:23 UTC · CWE-20 · Improper Input Validation

CVE recordCVE.org · 5.1
CVSS sourceCNA
EPSS source
?The date BlackTree first stored a score for this CVE from the daily FIRST EPSS feed.
FIRST · tracked since 2026-08-14
European sourceENISA EUVD · EUVD-2021-0403
Product sourceCNA
Remediation sourceCVE/CNA references
CWE sourceCNA
NVD statusNVD modified after enrichment

Core structured fields are present and their contributing authorities are shown above.

Material change intelligence

What changed after publication

View recent updates ↗

No material field changes have been recorded since change tracking began. Routine source refreshes and cosmetic edits are intentionally excluded.

Material fields only · duplicate refreshes suppressed · history retained for the configured operational retention period
CVE published
Fixed release or patch reference recorded
ENISA EUVD · EUVD-2021-0403↗CVE authority or vendor reference · GitHub GHSA-QFPC-5PJR-MH26↗Patch or update reference↗Official CVE record (CNA and ADP data)↗NVD record↗
Technical terms and abbreviations used in this report
CVE
Common Vulnerabilities and Exposures: the public identifier for one disclosed vulnerability.
CVSS
Common Vulnerability Scoring System: a technical severity framework; it is not patching priority by itself.
EPSS
Exploit Prediction Scoring System: FIRST's estimate of the probability that exploitation activity will be observed in the next 30 days; it is a forecast, not confirmation.
CWE
Common Weakness Enumeration: the standard category describing the underlying software or hardware weakness.
CNA
CVE Numbering Authority: an organisation authorised to assign and publish CVE records.
CISA ADP
Cybersecurity and Infrastructure Security Agency Authorized Data Publisher: structured enrichment added to a CVE record.
NVD
National Vulnerability Database: NIST's enrichment service for CVE records.
CERT / CSIRT
A computer security incident response team that publishes warnings or coordinates incident response.
PoC
Proof of concept: public material that demonstrates or helps reproduce exploitation.
CSAF
Common Security Advisory Framework: a machine-readable format for security advisories.
LoTL
Living off the land: abuse of legitimate tools or system functions during an attack.
Free version - for non-commercial use only.CVE-2021-37677 · cve.blacktree.nl