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CVE Record

CVE-2021-29575: Overflow/denial of service in `tf.raw_ops.ReverseSequence`

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.ReverseSequence` allows for stack overflow and/or `CHECK`-fail based denial of service. The implementation(https://github.com/tensorflow/tensorflow/blob/5b3b071975e01f0d250c928b2a8f901cd53b90a7/tensorflow/core/kernels/reverse_sequence_op.cc#L114-L118) fails to validate that `seq_dim` and `batch_dim` arguments are valid. Negative values for `seq_dim` can result in stack overflow or `CHECK`-failure, depending on the version of Eigen code used to implement the operation. Similar behavior can be exhibited by invalid values of `batch_dim`. 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 TensorFlow flaw can let a local, low-privileged user crash affected ML code by causing ReverseSequence to receive invalid dimension arguments. It is an availability issue, not a known data theft or code execution issue. Business urgency is usually low unless affected TensorFlow is used in shared, multi-tenant, or untrusted model execution environments.

Executive priority

Treat as routine patching unless TensorFlow is exposed through shared research platforms, hosted notebooks, or systems running untrusted models. Prioritize upgrades in multi-user ML environments where a crash could disrupt services or other tenants.

Technical view

TensorFlow failed to validate seq_dim and batch_dim in tf.raw_ops.ReverseSequence. Invalid negative or out-of-range values may trigger stack overflow or CHECK-fail denial of service, depending on Eigen behavior. CVSS is 2.5 with local access, high complexity, low privileges, no user interaction, and low availability impact.

Likely exposure

Exposure is most likely where affected TensorFlow versions execute user-influenced graphs, models, notebooks, or operation parameters. The listed affected ranges are 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.

Exploitation context

The source bundle does not show active exploitation, and KEV is false. The CVSS vector indicates local access, low privileges, and high attack complexity. Impact is limited to availability through process crash or denial of service, not confidentiality or integrity.

Researcher notes

The official advisory attributes the issue to missing validation for seq_dim and batch_dim. The commit reference is the relevant fix source. Evidence supports denial of service only; no cited source supports remote exploitation, privilege escalation, data exposure, or exploitation in the wild.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or the fixed supported patch release for your branch.
  • For 2.1, 2.2, 2.3, or 2.4 branches, use 2.1.4, 2.2.3, 2.3.3, or 2.4.2.
  • Restrict execution of untrusted TensorFlow graphs, notebooks, or raw operation parameters.
  • Check TensorFlow vendor guidance if maintaining a backported or distribution-packaged build.

Validation and detection

  • Inventory deployed TensorFlow versions across notebooks, training jobs, model services, and containers.
  • Confirm no affected version ranges remain in production or shared execution environments.
  • Review whether user-controlled workflows can reach tf.raw_ops.ReverseSequence.
  • Run normal ML workload regression 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

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ATT&CK lookup starting points

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cwe · low confidence lookup

CWE-119: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29575 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-2021-29575Attack 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-119

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