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CVE Record

CVE-2021-29542: Heap buffer overflow in `StringNGrams`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements. If input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`. 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 flaw that can crash affected machine-learning workloads when specially crafted input reaches the StringNGrams operation. It is not described as allowing data theft or code execution in the provided sources.

Executive priority

Handle through normal patch management unless TensorFlow processes untrusted input in production. Business risk is mainly service disruption, not compromise, based on the provided CVSS and advisory data.

Technical view

The bug is a heap buffer overflow in TensorFlow's StringNGrams kernel. Certain padding and token-count edge cases can lead the operator to read before the expected data buffer. CVSS 3.1 is 2.5, with local access, high complexity, low privileges, and availability impact only.

Likely exposure

Exposure is limited to environments running affected TensorFlow versions and reachable code paths that invoke tf.raw_ops.StringNGrams with attacker-influenced input. ML serving, data preprocessing, or notebook workloads that accept untrusted strings deserve review.

Exploitation context

Sources describe local, low-severity, high-complexity exploitation requiring low privileges, with availability impact only. The bundle does not cite KEV listing or active exploitation evidence; treat exploitation as unconfirmed.

Researcher notes

Affected ranges are TensorFlow before 2.1.4, 2.2.0 to before 2.2.3, 2.3.0 to before 2.3.3, and 2.4.0 to before 2.4.2. The fix is represented by the referenced upstream commit and release backports.

Mitigation direction

  • Inventory services and notebooks using TensorFlow.
  • Upgrade TensorFlow to 2.5.0 or patched supported branches.
  • Use 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where branch-pinning is required.
  • Prioritize systems that process untrusted tensor or string inputs.
  • Check vendor guidance if upgrade timing is constrained.

Validation and detection

  • Confirm deployed TensorFlow versions against affected ranges.
  • Search ML code for tf.raw_ops.StringNGrams usage.
  • Verify patched versions in lockfiles, containers, and runtime environments.
  • Review monitoring for unusual TensorFlow string preprocessing crashes.
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-2021-29542 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-2021-29542Attack 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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