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CVE Record

CVE-2021-29535: Heap buffer overflow in `QuantizedMul`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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
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Glexia's TakeAutomated analysislow

Security readout for executives and security teams

Plain-English summary

This TensorFlow flaw can let a low-privileged local attacker crash or disrupt a TensorFlow workload by supplying invalid quantization threshold tensors. The public sources rate it low severity because impact is limited to availability and exploitation requires specific local conditions.

Executive priority

Treat as a low-priority patching item unless affected TensorFlow workloads process untrusted ML inputs. Prioritize remediation in shared platforms, hosted inference services, or research environments where users can run supplied models.

Technical view

`QuantizedMul` assumed four quantization threshold arguments were valid scalars. If any tensor is empty, `.flat<T>()` is empty and reading element 0 can trigger a heap buffer overflow. The issue is tracked as CWE-131 and fixed in TensorFlow 2.5.0 with backports planned for supported 2.1-2.4 branches.

Likely exposure

Exposure is most likely in applications or pipelines running affected TensorFlow versions that execute `QuantizedMul` with attacker-influenced tensors, models, or inputs. The CVSS vector requires local access, low privileges, and high attack complexity.

Exploitation context

The provided sources do not show active exploitation, and the CVE is not listed as KEV. Public evidence supports a denial-of-service style risk, not data theft or privilege escalation.

Researcher notes

Focus review on TensorFlow `QuantizedMul` usage and dependency versions. The available evidence supports local, high-complexity availability impact only. Do not assume remote exploitation or broader product impact without additional vendor evidence.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or later where practical.
  • Apply fixed backports: 2.4.2, 2.3.3, 2.2.3, or 2.1.4 as applicable.
  • Check TensorFlow advisory guidance for unsupported or older branches.
  • Limit untrusted TensorFlow model or tensor input processing until patched.

Validation and detection

  • Inventory deployed TensorFlow package versions in applications and ML pipelines.
  • Identify workloads that accept untrusted models, graphs, or tensor inputs.
  • Confirm affected version ranges are remediated to fixed releases.
  • Review dependency lockfiles and container images for old TensorFlow builds.
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

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cve · low confidence lookup

CVE-2021-29535 mapping review

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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-2021-29535Attack 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.

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