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CVE Record

CVE-2021-29536: Heap buffer overflow in `QuantizedReshape`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 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 is a low-severity TensorFlow memory bug. A user or process with local ability to supply invalid quantization threshold tensors could crash or disrupt workloads using QuantizedReshape. The cited scoring indicates limited availability impact, no confidentiality or integrity impact, high attack complexity, and no known active exploitation in KEV.

Executive priority

Treat this as routine patching unless TensorFlow workloads process untrusted ML inputs in shared or exposed environments. Business risk is primarily service disruption, not data theft or tampering.

Technical view

Affected TensorFlow versions assume QuantizedReshape threshold arguments are valid scalars. If either tensor is empty, flat<T>() returns an empty buffer and accessing element 0 causes a heap buffer overflow. The advisory maps this to CWE-131 and CVSS 3.1 score 2.5 with local, low-availability impact.

Likely exposure

Exposure is most likely in ML services, notebooks, pipelines, or applications running affected TensorFlow versions and accepting untrusted models, graphs, or tensor inputs that can reach QuantizedReshape.

Exploitation context

The source bundle reports no KEV listing and provides no evidence of active exploitation. The CVSS vector requires local access, privileges, and high attack complexity, with impact limited to availability.

Researcher notes

The key issue is invalid scalar assumptions in QuantizedReshape threshold handling. Validate exposure by version and reachable input paths. The public sources identify the fix commit and patched release targets but do not provide active exploitation evidence.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or later where the fix is included.
  • Use patched backports: 2.4.2, 2.3.3, 2.2.3, or 2.1.4 as applicable.
  • Restrict untrusted model, graph, and tensor input processing until patched.
  • Prioritize internet-facing or multi-tenant ML environments first.

Validation and detection

  • Inventory deployed TensorFlow versions in code, lockfiles, containers, and notebooks.
  • Confirm affected ranges are not present in runtime environments.
  • Review ML entry points for untrusted model or tensor ingestion paths.
  • Run existing unit and integration tests after upgrading TensorFlow.
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.

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cve · low confidence lookup

CVE-2021-29536 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-2021-29536Attack 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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