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CVE-2021-29607· NVD / CVE Program· CNA GitHub_M

Incomplete validation in `SparseSparseMinimum`

TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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.

HighCVSS 7.8 · v3.1—No exploit Fix available
Published
May 14, 2021
Updated
Jun 16, 2026
EPSS
0.2% · 13th percentile
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Report tools

JSON

Action score

31

Monitor

Low priority for now.

CVSS
31 / 40 · 7.8 / 10
CISA KEV
0 / 30 · Not listed
EPSS
0 / 30 · 0.2%

CNA vs NVD score

NVD
7.8
CNA · GitHub_M
5.3
2.5 point gap

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

The score given by the assigning authority versus NVD’s independent score. A gap means the severity is contested.

Noroxi analysis

No Noroxi analysis for this record yet

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Affected systems

VendorProduct
googletensorflow

Affected versions

NVD version ranges (for catalog products). Add the product to your stack with a version and matching uses these.

  • google tensorflowbefore 2.1.4
  • google tensorflow2.2.0 and later · before 2.2.3
  • google tensorflow2.3.0 and later · before 2.3.3
  • google tensorflow2.4.0 and later · before 2.4.2

Versions reported by the vendor

Affected version ranges reported by the assigning authority (GitHub_M). Independent of NVD's CPE analysis and usually ahead of it.

  • tensorflow tensorflow

    • < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2affected

Package-level exposure

OSV and GitHub Advisory data: ecosystem, package and range. SBOM matching uses this table.

EcosystemPackageAffected rangeFix
PyPItensorflowfrom 2.2.0 · before 2.2.32.2.3
PyPItensorflowfrom 2.3.0 · before 2.3.32.3.3
PyPItensorflowfrom 2.4.0 · before 2.4.22.4.2
PyPItensorflowbefore 2.1.42.1.4
PyPItensorflow-cpufrom 2.2.0 · before 2.2.32.2.3
PyPItensorflow-cpufrom 2.3.0 · before 2.3.32.3.3
PyPItensorflow-cpufrom 2.4.0 · before 2.4.22.4.2
PyPItensorflow-cpubefore 2.1.42.1.4
PyPItensorflow-gpufrom 2.2.0 · before 2.2.32.2.3
PyPItensorflow-gpufrom 2.3.0 · before 2.3.32.3.3
PyPItensorflow-gpufrom 2.4.0 · before 2.4.22.4.2
PyPItensorflow-gpubefore 2.1.42.1.4

Same product

google: all records

Other highest-scoring records for the same primary product.

  • CVE-2023-25668TensorFlow vulnerable to heap out-of-buffer read in the QuantizeAndDequantize operation
    39Monitor
  • CVE-2023-25664TensorFlow vulnerable to Heap Buffer Overflow in AvgPoolGrad
    39Monitor
  • CVE-2022-41900FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess in Tensorflow
    39Monitor
  • CVE-2022-35939Out of bounds write in `scatter_nd` op in TensorFlow Lite
    39Monitor
  • CVE-2022-23587Integer overflow in Tensorflow
    39Monitor
  • CVE-2020-15208Data corruption in tensorflow-lite
    39Monitor

Remediation

Which version to upgrade to

Fix versions compiled from the vendor, package registries and Microsoft. Verify the vendor's note before upgrading.

Product / packageFixed versionSource
PyPI:tensorflow2.1.4Package registry (OSV)
PyPI:tensorflow2.2.3Package registry (OSV)
PyPI:tensorflow2.3.3Package registry (OSV)
PyPI:tensorflow2.4.2Package registry (OSV)
PyPI:tensorflow-cpu2.1.4Package registry (OSV)
PyPI:tensorflow-cpu2.2.3Package registry (OSV)
PyPI:tensorflow-cpu2.3.3Package registry (OSV)
PyPI:tensorflow-cpu2.4.2Package registry (OSV)
PyPI:tensorflow-gpu2.1.4Package registry (OSV)
PyPI:tensorflow-gpu2.2.3Package registry (OSV)
PyPI:tensorflow-gpu2.3.3Package registry (OSV)
PyPI:tensorflow-gpu2.4.2Package registry (OSV)

Exploit status

No known public exploit

No public exploit has been observed yet. That doesn't mean you're safe, only that the bar is a little higher.

Research context

For pentesters and researchers: attack profile, score disagreement, timeline, patch commits, credits, variant and chain candidates, bug bounty scope. All derived from existing data; no exploit code.

Timeline

From publication to today: proof of concept, Metasploit module, CISA KEV and fix record. Dates are as reported by the sources.

No dated events beyond publication.

EPSS, last 120 days

FIRST EPSS daily score; only changes of 0.01 or more are recorded (step chart).

Patch and commit links

Commit, PR and diff links among the references. A starting point for patch-diffing and variant hunting; fixes, not exploits.

Credits

All researchers

Finders, reporters and analysts named in the CNA record. Click a name for that researcher’s other records.

No credits in the CNA record.

Variant candidates

Same product, same weakness class, within 18 months. If the patch missed the root cause, the sibling bug is here.

No nightly-computed relations.

Chain candidates

An authentication bypass and a privilege-requiring bug in the same product, published close together: combined they may become an unauthenticated path.

—

Bug bounty scope

No known public program.

Source: bounty-targets-data (public HackerOne, Bugcrowd, Intigriti, YesWeHack listings).

Technical details

Attack conditions

  • Someone with local access to the system can trigger it.
  • A low-privileged account is enough.
  • No user action is required.
  • No special conditions are required; it is repeatable.

If successful

Confidentiality
high · data can be read
Integrity
high · data or configuration can be modified
Availability
high · the service can be disrupted
Attack vector
Local
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Unchanged
Confidentiality impact
High
Integrity impact
High
Availability impact
High

Weakness class (CWE)

CWE-754 · Improper Check for Unusual or Exceptional Conditions

CVSS vector

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

nvd-primary

Attack context

MITRE CAPEC attack patterns and ATT&CK techniques for this weakness class (CWE). A starting point for detection rules and threat hunting.

MITRE has no CAPEC/ATT&CK mapping for this CWE.

Change log

  1. Fix✗ → ✓

For records you follow, these changes also arrive as notifications. →

References

Vendor advisories and official records. Exploit/PoC links are deliberately left out.

All records