LiveActive security incident?Get immediate response
CVE Record

CVE-2021-29537: Heap buffer overflow in `QuantizedResizeBilinear`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

LowCVSS 2.5Not KEV-listedUpdated
Discuss exposureBack to CVEs
Glexia's TakeAutomated analysislow

Security readout for executives and security teams

Plain-English summary

CVE-2021-29537 is a low-severity TensorFlow flaw that can crash or disrupt affected machine-learning workloads under specific local conditions. The issue is in QuantizedResizeBilinear handling of invalid quantization thresholds. Public sources do not show active exploitation, and the documented impact is limited availability loss.

Executive priority

Treat as routine patching unless TensorFlow workloads accept untrusted local jobs or model submissions. The business risk is mainly service disruption, not data theft or system compromise based on the provided sources.

Technical view

TensorFlow’s QuantizedResizeBilinear implementation assumed threshold arguments were valid scalars and directly accessed their numeric values. Invalid thresholds could trigger a heap buffer overflow. The CVSS 3.1 score is 2.5, with local attack vector, high complexity, low privileges required, and low availability impact only.

Likely exposure

Exposure is most likely in systems running affected TensorFlow versions that process quantized resize operations, especially where local users or submitted ML workloads can influence model inputs. Affected ranges include versions before 2.1.4 and specific 2.2.x, 2.3.x, and 2.4.x releases before patched versions.

Exploitation context

The source bundle marks KEV as false and provides no cited evidence of active exploitation. CVSS indicates exploitation requires local access, low privileges, and high attack complexity, with no confidentiality or integrity impact and only limited availability impact.

Researcher notes

The key evidence is the GitHub Security Advisory and linked fix commit. The weakness is mapped to CWE-131. Evidence does not support claims of remote exploitation, active exploitation, or broader product impact beyond TensorFlow versions named in the source bundle.

Mitigation direction

  • Upgrade to TensorFlow 2.5.0 or a patched supported branch release.
  • Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where branch pinning is required.
  • Inventory application, notebook, container, and training dependencies for affected TensorFlow versions.
  • Limit untrusted local ML workload execution until affected runtimes are patched.
  • Check the TensorFlow advisory for branch-specific upgrade guidance.

Validation and detection

  • Confirm deployed TensorFlow versions against the affected ranges listed in the advisory.
  • Review ML pipelines for QuantizedResizeBilinear or quantized resize model usage.
  • Verify patched builds are present in runtime containers and batch workers.
  • Check dependency lockfiles and transitive package sources for older TensorFlow pins.
  • Confirm vulnerability scanners recognize the upgraded TensorFlow package version.
Prepared
Confidence
high
Sources
4

Public sources used

Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.

Potential ATT&CK relevance

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 · low confidence lookup

CWE-131: 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 lookup
cve · low confidence lookup

CVE-2021-29537 mapping review

Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.

Open ATT&CK lookup
Vulnerability profileCVE Program record
Severity
Low
CVSS
2.5 (3.1)
Known Exploited
No
Published

Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Official CVE source material

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.

1CVSS vectors
0Timeline events
0ADP providers
3Source links

CVSS vector scores

1 official score

We 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.

ScoreVersionSeverityVectorExploitImpactSource
2.5CVSS 3.1LowCVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L11.4Primary CVE score

Vulnerability scoring details

Base CVSS 3.1 score

2.5Low
CVSS 3.1 vector shape for CVE-2021-29537Attack VectorAttack ComplexityPrivileges RequiredUser InteractionScopeConfidentiality ImpactIntegrity ImpactAvailability Impact

Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Attack Vector
NetworkAdjacentLocalPhysical
Attack Complexity
LowHigh
Privileges Required
NoneLowHigh
User Interaction
NoneRequired
Scope
ChangedUnchanged
Confidentiality Impact
HighLowNone
Integrity Impact
HighLowNone
Availability Impact
HighLowNone

Source materials

Affected products

Products and packages named in the record

VendorProductVersion / packageStatus
tensorflowtensorflow< 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2Listed
Weakness

CWE details

CWE-131

CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.

CWE-131 · source CWE mapping

Incorrect Calculation of Buffer Size

Incorrect Calculation of Buffer Size represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

Open CWE intelligenceSame-CWE CVEs