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CVE Record

CVE-2021-29529: Heap buffer overflow caused by rounding

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value. For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, in the interpolation code(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer 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

CVE-2021-29529 is a low-severity TensorFlow memory safety bug. A specially shaped input to a specific quantized image-resizing operation can trigger a heap buffer overflow, mainly risking a local denial-of-service condition rather than data theft or system takeover.

Executive priority

Treat as routine patch management unless TensorFlow handles untrusted local or server-side ML inputs. The business risk is limited by low severity and lack of reported exploitation, but affected ML runtimes should still be upgraded during normal maintenance.

Technical view

The issue is in `tf.raw_ops.QuantizedResizeBilinear`. Float rounding can make calculated interpolation bounds inconsistent, causing an out-of-bounds image element access and heap buffer overflow. CVSS 3.1 is 2.5: local attack vector, high complexity, low privileges, no confidentiality or integrity impact, and low availability impact.

Likely exposure

Exposure is most plausible in TensorFlow workloads using affected versions and invoking the quantized resize bilinear raw operation with attacker-influenced inputs. This is narrower than general TensorFlow use, because the source CVSS requires local access, low privileges, and high attack complexity.

Exploitation context

The provided sources do not report active exploitation, and KEV status is false. They describe a crash-oriented memory bug triggered by manipulated input values, but provide no evidence of in-the-wild exploitation or broader compromise impact.

Researcher notes

This maps to CWE-131 and affects TensorFlow version ranges before 2.1.4, 2.2.3, 2.3.3, and 2.4.2. The vendor advisory states the fix is in 2.5.0 and cherry-picked to supported branches. Evidence is sufficient for version-based validation, not exploitation claims.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or later where practical.
  • For supported older branches, use 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
  • Rebuild containers, notebooks, and model-serving images that bundle affected TensorFlow versions.
  • Check TensorFlow vendor guidance before applying alternative mitigations.

Validation and detection

  • Inventory TensorFlow versions in applications, lockfiles, images, and ML runtime environments.
  • Identify workloads that call `tf.raw_ops.QuantizedResizeBilinear` or related quantized resize paths.
  • Confirm patched versions are deployed in production and batch-processing environments.
  • Review dependency scans and SBOMs for the affected version ranges.
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-2021-29529 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-2021-29529Attack 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.