TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in tf.raw_ops.FractionalAvgPool. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of input_size[i] and pooling_ratio_[i] (via the value.shape() and pooling_ratio arguments). If the value in input_size[i] is smaller than the pooling_ratio_[i], then the floor operation results in output_size[i] being 0. The DCHECK_GT line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to GeneratePoolingSequence(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since output_length can be 0, this results in runtime crashing. 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-2021-29550
This low-severity CVE scores 2.5 under NVD CVSS v3. EPSS exploit probability: 0.2% (7th percentile of EPSS-scored CVEs). 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
A fix is available — apply it.
- CVSS v3
- 2.5
- EG Score
- 2.5LOWmedium confidence
- EG Risk
- 16EG Risk 16/100CISA SSVC
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 computedSeverity25% × 45%Exploitation0% × 40%Automatability30% × 15%CISA SSVC: Track at low or medium mission impact; Track or Attend at high (mission-essential systems).Action: A fix is available. Apply it within your standard update timelines at low or medium mission impact and sooner than that at high. - EPSS PROB
- 0.2%
- EPSS %ILE
- 7th
- KEV
- Not listed
CISA SSVCTrack at low or medium mission impact; Track or Attend at high (mission-essential systems).
A fix is available. Apply it within your standard update timelines at low or medium mission impact and sooner than that at high.
Exploitation none (no KEV listing, exploit record or EPSS ≥ 50%) · Automatable unknown (not published for this CVE) · Technical impact partial (CVSS below 9.0). Mission impact is CISA's Mission & Well-being decision point, and only you can judge it: high means the affected system is essential to your organisation's mission, or its compromise could cause irreversible harm to people. CISA's decision table
Published
May 14, 2021
Last Modified
November 21, 2024
References (4)
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-f78g-q7r4-9wcv
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f78g-q7r4-9wcv
Affected Packages
(3 across 1 ecosystem)
PyPI(3)
| Package | Vulnerable range | Fix by version range | Dependents |
|---|---|---|---|
| tensorflow | 0.12.0 ... 2.4.2 (102 versions) |
| — |
| tensorflow-cpu | 1.15.0 ... 2.4.2 (17 versions) |
| — |
| tensorflow-gpu | 0.12.0 ... 2.4.2 (52 versions) |
| — |
Weakness Classification(1)
MITRE Common Weakness Enumeration — the root-cause categories this CVE belongs to.
Data Freshness Timeline
(refreshed 8× in last 7d / 32× 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.
Showing the most recent 100 of 177 total refreshes for this CVE.
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Related CVEs(same product + same CWE)
Same product
10 shownPyPI:tensorflow-gpu
Frequently asked(5)
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Dependency Blast Radius
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