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CVE Record

CVE-2021-29545: Heap buffer overflow in `SparseTensorToCSRSparseMatrix`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in converting sparse tensors to CSR Sparse matrices. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap. If the value at `indices(i, 0)` is such that `indices(i, 0) + 1` is outside the bounds of `csr_row_ptr`, this results in writing outside of bounds of heap allocated data. 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

CVE-2021-29545 is a TensorFlow flaw that can crash affected machine-learning workloads when malformed sparse tensor data is converted to CSR format. The documented impact is denial of service, not data theft or code execution. Business urgency is low unless untrusted users can feed tensors into shared training, notebook, or inference environments.

Executive priority

Handle in normal patch cycles unless affected TensorFlow workloads are multi-tenant or accept untrusted user data. Prioritize shared ML infrastructure first because a crash could disrupt jobs or services for other users.

Technical view

The issue is a heap out-of-bounds write in TensorFlow's SparseTensorToCSRSparseMatrix path. The vulnerable implementation can index csr_row_ptr using indices(i, 0) + 1 without sufficient bounds safety. Sources describe a CHECK failure and availability impact. Affected versions include pre-patched 2.1, 2.2, 2.3, and 2.4 release lines.

Likely exposure

Exposure is most likely where affected TensorFlow versions process untrusted sparse tensor inputs. This includes shared ML development platforms, local notebooks, batch training jobs, or inference systems that let low-privileged users submit model data. Systems not running affected TensorFlow, or not accepting untrusted tensor inputs, have limited practical exposure.

Exploitation context

The source bundle does not show active exploitation, and CISA KEV is false. CVSS 3.1 rates this 2.5 with local access, high complexity, low privileges, and availability-only impact. Treat this as a reliability and tenant-isolation concern, especially in shared compute environments.

Researcher notes

Evidence supports denial of service through a TensorFlow sparse tensor conversion bug. The bundle names affected versions and fixed release targets, but does not provide evidence of exploitation in the wild. Avoid assuming remote reachability; exposure depends on whether attackers can supply tensor inputs to affected code.

Mitigation direction

  • Upgrade TensorFlow to 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 those branches are required.
  • Restrict who can submit sparse tensor inputs to affected TensorFlow services.
  • Check vendor guidance before using unsupported TensorFlow versions.

Validation and detection

  • Inventory deployed TensorFlow versions in applications, notebooks, containers, and training images.
  • Flag versions matching the affected ranges listed in the CVE source bundle.
  • Confirm patched versions are running after rebuilds or dependency updates.
  • Review exposed ML interfaces for untrusted sparse tensor processing.
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

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ATT&CK lookup starting points

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cwe · low confidence lookup

CWE-131: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29545 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-2021-29545Attack 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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