CVE-2022-35996

MEDIUMNVD 5.95.9—
EchelonGraph scoreMEDIUM confidence

This medium-severity CVE scores 5.9 under NVD CVSS v3. EPSS exploit probability: 0.4%, top 66% 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).

Triggered by: NVD CVSS baseline
Sources: epss, nvd
Trending — 3 sources updated this week
5.9
EchelonGraph verdictMonitorLow exploitation likelihood right now — keep watching.
  • Lower severity and no public exploit yet
CISA-KEV: Not listedEPSS: 0%CVSS: 5.9Exploit: NoneExposed: 0

No vendor fix yet — apply a workaround or compensating control (WAF / firewall / segmentation) and watch for a patch.

TensorFlow is an open source platform for machine learning. If Conv2D is given empty input and the filter and padding sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

CVSS v3
5.9
EG Score
5.9(medium)
EG Risk
27(Track)
EG Risk 27/100SSVC: 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 computed
Severity59% × 45%
Exploitation0% × 40%
Automatability0% × 15%
Action: Routine — remediate on your standard cadence.
EPSS
33.6%
KEV
Not listed

Published

September 16, 2022

Last Modified

November 21, 2024

References (4)

Affected Packages

(3 across 1 ecosystem)
PyPI(3)
PackageVulnerable rangeFixed inDependents
tensorflow2.9.02.9.1—
tensorflow-cpu2.9.02.9.1—
tensorflow-gpu2.9.02.9.1—

Weakness Classification(1)

MITRE Common Weakness Enumeration — the root-cause categories this CVE belongs to.

Data Freshness Timeline

(refreshed 14× in last 7d / 51× 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 109 total refreshes for this CVE.

  1. 2026-07-23 02:12 UTCEG score recompute
  2. 2026-07-22 22:59 UTCEG score recompute
  3. 2026-07-22 14:06 UTCEPSS rescore
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Frequently asked(5)

What is CVE-2022-35996?
CVE-2022-35996 is a medium vulnerability published on September 16, 2022. TensorFlow is an open source platform for machine learning. If Conv2D is given empty input and the filter and padding sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. We have patched the issue in…
When was CVE-2022-35996 disclosed?
CVE-2022-35996 was first published in the National Vulnerability Database on September 16, 2022, with the most recent update on November 21, 2024. EchelonGraph re-ingests CVE updates from NVD on a 2-hour cycle, so this page reflects the latest published state.
Is CVE-2022-35996 actively exploited?
CVE-2022-35996 is not currently on CISA's Known Exploited Vulnerabilities catalog. FIRST EPSS estimates a 33.6% percentile likelihood of exploitation in the next 30 days — higher percentiles indicate greater predicted risk.
What is the CVSS score of CVE-2022-35996?
CVE-2022-35996 has a CVSS v3 base score of 5.9 (NVD).
How do I remediate CVE-2022-35996?
Patch to the fixed version published by the affected vendor. Where vendor advisories exist for CVE-2022-35996, EchelonGraph cross-links them in the Vendor Advisories panel below — those typically contain the canonical remediation steps, fixed version numbers, and any vendor-specific mitigations.

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