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CVE Record

CVE-2021-29521: Segfault in SparseCountSparseOutput

TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., `std::vector<absl::flat_hash_map<int64,T>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.

LowCVSS 2.5Not KEV-listedUpdated
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Glexia's TakeAutomated analysislow

Security readout for executives and security teams

Plain-English summary

CVE-2021-29521 is a low-severity TensorFlow crash issue. A specially shaped negative tensor argument can make a TensorFlow operation crash, affecting availability rather than confidentiality or integrity. Business urgency is limited unless untrusted users can influence TensorFlow model inputs or raw operations in production.

Executive priority

Treat as routine patching unless TensorFlow workloads accept untrusted model inputs. Prioritize exposed ML services and shared research environments first; otherwise fold remediation into normal dependency maintenance.

Technical view

The issue is in tf.raw_ops.SparseCountSparseOutput. A negative dense_shape value can be used to initialize a BatchedMap backed by std::vector, breaking vector invariants and causing a segmentation fault. The vendor states validation that dense_shape is a valid non-negative tensor shape resolves it.

Likely exposure

Exposure is most plausible where TensorFlow versions below 2.3.3 or 2.4.0 through before 2.4.2 process untrusted tensors, models, or raw op calls. Typical internally controlled ML jobs have lower practical exposure.

Exploitation context

The CVSS vector indicates local access, high attack complexity, low privileges, no user interaction, and low availability impact. The source bundle does not identify active exploitation, and KEV status is false.

Researcher notes

The weakness is classified as CWE-131 and impacts availability via a segfault. The source bundle names the fix approach but does not provide evidence of remote exploitation or broader product impact beyond TensorFlow.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0, 2.4.2, 2.3.3, or later supported fixed releases.
  • Validate dense_shape values are non-negative before invoking SparseCountSparseOutput.
  • Restrict execution of untrusted TensorFlow models, tensors, or raw operations.
  • Check TensorFlow vendor guidance for branch-specific remediation details.

Validation and detection

  • Inventory TensorFlow versions in applications, notebooks, containers, and build lockfiles.
  • Find code paths using tf.raw_ops.SparseCountSparseOutput.
  • Confirm external inputs cannot supply negative dense_shape values.
  • Review dependency scans for CVE-2021-29521 after remediation.
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

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cwe · low confidence lookup

CWE-131: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29521 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-2021-29521Attack 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< 2.3.3, >= 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.