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CVE Record

CVE-2021-29612: Heap buffer overflow in `BandedTriangularSolve`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`. The implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty. Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen in this op's implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. 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 3.6Not KEV-listedUpdated
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Glexia's TakeAutomated analysislow

Security readout for executives and security teams

Plain-English summary

CVE-2021-29612 is a low-severity TensorFlow memory safety bug. A user who can run TensorFlow operations locally could trigger a heap buffer overflow in BandedTriangularSolve, potentially causing limited integrity or availability impact. The sources do not show active exploitation.

Executive priority

Treat this as routine patch management unless TensorFlow runs in a shared or user-extensible ML environment. Business urgency increases for hosted notebooks, multi-tenant model execution, or systems accepting untrusted model logic.

Technical view

The vulnerable TensorFlow op tf.raw_ops.BandedTriangularSolve fails to validate empty tensors and continues after helper validation sets a non-OK context status. This can reach Eigen code with invalid assumptions and trigger a heap buffer overflow. CVSS 3.1 is 3.6, with local access, high complexity, and low privileges required.

Likely exposure

Exposure is most relevant in environments running affected TensorFlow versions where users can execute models, notebooks, plugins, or tensor operations. Public web exposure is indirect unless the service maps untrusted input into this operation.

Exploitation context

The bundle marks KEV as false and provides no evidence of active exploitation. The CVSS vector indicates local access, high attack complexity, low privileges, no user interaction, and limited integrity and availability impact.

Researcher notes

The root cause is incomplete input validation around empty tensors and improper continuation after OP_REQUIRES records an error status. The provided sources identify fixes in TensorFlow commits and patched releases, but do not describe exploitation in the wild.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or a patched supported release.
  • Use TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where applicable.
  • Check TensorFlow vendor guidance before relying on unsupported version branches.
  • Restrict untrusted users from executing arbitrary TensorFlow operations in shared environments.
  • Prioritize remediation in multi-tenant ML notebooks, workers, and model execution services.

Validation and detection

  • Inventory TensorFlow versions in applications, notebooks, containers, and dependency lockfiles.
  • Confirm no runtime uses affected version ranges listed in the advisory.
  • Identify code paths or models using tf.raw_ops.BandedTriangularSolve.
  • Review whether untrusted users can submit tensors, models, or execution graphs.
  • Verify deployed images include the patched TensorFlow release.
Prepared
Confidence
high
Sources
5

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-120: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29612 mapping review

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Vulnerability profileCVE Program record
Severity
Low
CVSS
3.6 (3.1)
Known Exploited
No
Published

Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/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
4Source 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
3.6CVSS 3.1LowCVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:L12.5Primary CVE score

Vulnerability scoring details

Base CVSS 3.1 score

3.6Low
CVSS 3.1 vector shape for CVE-2021-29612Attack VectorAttack ComplexityPrivileges RequiredUser InteractionScopeConfidentiality ImpactIntegrity ImpactAvailability Impact

Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/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-120

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

CWE-120 · source CWE mapping

Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')

Buffer Copy without Checking Size of Input ('Classic Buffer Overflow') represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

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