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.
CVE-2021-29607
This medium-severity CVE scores 5.3 under NVD CVSS v3. EPSS exploit probability: 0.2%, top 86% of all CVEs by exploit prediction. GitHub Security Advisory data not yet ingested — confidence will rise once GHSA publishes (typical lag: hours to days for open-source ecosystem CVEs; never for infrastructure-only CVEs).
- Lower severity and no public exploit yet
No vendor fix yet — apply a workaround or compensating control (WAF / firewall / segmentation) and watch for a patch.
- CVSS v3
- 5.3
- EG Score
- 5.3(medium)
- EG Risk
- 28(Track)EG Risk 28/100SSVC: Track
EG Risk is EchelonGraph's 0–100 priority score: it fuses intrinsic severity with real-world exploitation and automatability so you can rank equal-severity CVEs and fix the most dangerous first. Higher = act sooner. Distinct from the 0–10 EG Score (severity).
How it’s computedSeverity53% × 45%Exploitation0% × 40%Automatability30% × 15%Action: Routine — remediate on your standard cadence. - EPSS
- 14.3%
- KEV
- Not listed
Published
May 14, 2021
Last Modified
November 21, 2024
References (6)
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/commit/f6fde895ef9c77d848061c0517f19d0ec2682f3a
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-gv26-jpj9-c8gq
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/commit/f6fde895ef9c77d848061c0517f19d0ec2682f3a
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gv26-jpj9-c8gq
Affected Packages
(3 across 1 ecosystem)
PyPI(3)
| Package | Vulnerable range | Fixed in | Dependents |
|---|---|---|---|
| tensorflow | 0.12.0 ... 2.4.1 (98 versions) | 2.4.2 | — |
| tensorflow-cpu | 1.15.0 ... 2.4.1 (13 versions) | 2.4.2 | — |
| tensorflow-gpu | 0.12.0 ... 2.4.1 (48 versions) | 2.4.2 | — |
Weakness Classification(1)
MITRE Common Weakness Enumeration — the root-cause categories this CVE belongs to.
Data Freshness Timeline
(refreshed 14× in last 7d / 49× in last 30d)
Each row is a source pipeline that fetched or updated this CVE on that date, with what changed. For example, "NVD update" means NVD published or revised its analysis for this CVE; "MITRE cvelistV5" means we ingested or refreshed it from the CNA feed. Most recent first.
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Related CVEs(same CWE)
Same CWE
10 shownCWE-754
Frequently asked(5)
What is CVE-2021-29607?
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Dependency Blast Radius
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