CVE-2020-15213

MEDIUMNVD 4.04.0
EchelonGraph scoreLOW confidence

This medium-severity CVE scores 4.0 under NVD CVSS v3. EPSS exploit-prediction score not yet available (the EPSS model rescores nightly; freshly-published CVEs typically appear within 48 hours). 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: nvd
4.0
EchelonGraph verdictMonitorLow exploitation likelihood right now — keep watching.
  • Lower severity and no public exploit yet
CISA-KEV: Not listedEPSS: 1%CVSS: 4.0Exploit: NoneExposed: 0

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

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

CVSS v3
4.0
EG Score
4.0(low)
EPSS
46.5%
KEV
Not listed

Published

September 25, 2020

Last Modified

November 21, 2024

References (6)

Affected Packages

(3 across 1 ecosystem)
PyPI(3)
PackageVulnerable rangeFixed inDependents
tensorflow2.2.0, 2.3.02.3.1—
tensorflow-cpu2.2.0, 2.3.02.3.1—
tensorflow-gpu2.2.0, 2.3.02.3.1—

Weakness Classification(2)

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

Data Freshness Timeline

(refreshed 10× in last 7d / 45× 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.

  1. 2026-07-22 14:05 UTCEPSS rescore
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  71. 2026-05-23 07:12 UTCEG score recompute

Related CVEs(same CWE)

Frequently asked(5)

What is CVE-2020-15213?
CVE-2020-15213 is a medium vulnerability published on September 25, 2020. In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor,…
When was CVE-2020-15213 disclosed?
CVE-2020-15213 was first published in the National Vulnerability Database on September 25, 2020, 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-2020-15213 actively exploited?
CVE-2020-15213 is not currently on CISA's Known Exploited Vulnerabilities catalog. FIRST EPSS estimates a 46.5% percentile likelihood of exploitation in the next 30 days — higher percentiles indicate greater predicted risk.
What is the CVSS score of CVE-2020-15213?
CVE-2020-15213 has a CVSS v3 base score of 4.0 (NVD). The EG score is currently aggregating — additional source signals are being incorporated as they become available..
How do I remediate CVE-2020-15213?
Patch to the fixed version published by the affected vendor. Where vendor advisories exist for CVE-2020-15213, 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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