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CVE Record

CVE-2021-37669: Crash in NMS ops caused by integer conversion to unsigned in TensorFlow

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a `std::vector`. However, as `std::vector::resize` takes the size argument as a `size_t` and `output_size` is an `int`, there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in `CombinedNonMaxSuppression`. We have patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

MediumCVSS 5.5Not KEV-listedUpdated
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Glexia's TakeAutomated analysismoderate

Security readout for executives and security teams

Plain-English summary

CVE-2021-37669 is a TensorFlow denial-of-service flaw. Certain model-serving workloads using TensorFlow non-maximum suppression operations can be crashed when a negative, user-controlled size value is mishandled during vector resizing. The impact is service availability, not data theft or code execution.

Executive priority

Prioritize remediation for production ML services where untrusted users can influence inference inputs. This is not described as remote code execution, but a reliable crash against exposed model-serving workloads can create outage risk and operational disruption.

Technical view

Affected TensorFlow versions mishandle integer conversion in tf.raw_ops.NonMaxSuppressionV5 and CombinedNonMaxSuppression. A signed int output_size can be implicitly converted to unsigned size_t for std::vector::resize. Negative input can produce an invalid resize path and crash the process through division by zero or related failure.

Likely exposure

Exposure is most likely where TensorFlow 2.5.0, 2.4.x before 2.4.3, or versions before 2.3.4 serve models that invoke NonMaxSuppressionV5 or CombinedNonMaxSuppression with attacker-influenced inputs. General TensorFlow installations without those reachable operations have lower practical exposure.

Exploitation context

The source CVSS vector indicates local access, low privileges, no user interaction, and high availability impact. The provided sources do not show CISA KEV listing or active exploitation. Treat exploit status as unconfirmed unless new vendor or threat-intelligence evidence appears.

Researcher notes

The key weakness is CWE-681: incorrect numeric conversion between signed and unsigned types. The affected argument reaches std::vector::resize after implicit size_t conversion. The source bundle names two TensorFlow fixing commits and supported release lines receiving backports.

Mitigation direction

  • Upgrade to TensorFlow 2.6.0 or a fixed supported branch release.
  • For 2.5.x, update to TensorFlow 2.5.1 or later.
  • For 2.4.x, update to TensorFlow 2.4.3 or later.
  • For 2.3.x, update to TensorFlow 2.3.4 or later.
  • Review vendor advisory and commits before applying compensating controls.

Validation and detection

  • Inventory TensorFlow versions in model-serving and batch ML environments.
  • Identify models or code paths using NonMaxSuppressionV5 or CombinedNonMaxSuppression.
  • Confirm deployed packages match fixed TensorFlow versions listed by the advisory.
  • Check service logs for unexplained crashes in affected inference workflows.
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-681: 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-2021-37669 mapping review

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Vulnerability profileCVE Program record
Severity
Medium
CVSS
5.5 (3.1)
Known Exploited
No
Published

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

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
5.5CVSS 3.1MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H1.83.6Primary CVE score

Vulnerability scoring details

Base CVSS 3.1 score

5.5Medium
CVSS 3.1 vector shape for CVE-2021-37669Attack VectorAttack ComplexityPrivileges RequiredUser InteractionScopeConfidentiality ImpactIntegrity ImpactAvailability Impact

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

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>= 2.5.0, < 2.5.1, >= 2.4.0, < 2.4.3, < 2.3.4Listed
Weakness

CWE details

CWE-681

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

CWE-681 · source CWE mapping

Incorrect Conversion between Numeric Types

Incorrect Conversion between Numeric Types represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

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