In Tensorflow before version 2.4.0, an attacker can pass an invalid axis value to tf.quantization.quantize_and_dequantize. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, DCHECK-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
CVE-2020-15265
This medium-severity CVE scores 5.9 under NVD CVSS v3. EPSS exploit probability: 0.9%, top 44% 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
- 5.9
- EG Score
- 5.9(medium)
- EG Risk
- —
- EPSS
- 55.8%
- KEV
- Not listed
Published
October 21, 2020
Last Modified
November 21, 2024
References (6)
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/issues/42105
- security-advisories@githubhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrfp-j2mp-hq9c
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/issues/42105
- af854a3a-2127-422b-91ae-364da2661108https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrfp-j2mp-hq9c
Affected Packages
(3 across 1 ecosystem)
PyPI(3)
| Package | Vulnerable range | Fixed in | Dependents |
|---|---|---|---|
| tensorflow | 0.12.0rc0 ... 2.3.4 (113 versions) | 2.4.0 | — |
| tensorflow-cpu | 1.15.0 ... 2.3.4 (15 versions) | 2.4.0 | — |
| tensorflow-gpu | 0.12.0 ... 2.3.4 (50 versions) | 2.4.0 | — |
Weakness Classification(1)
MITRE Common Weakness Enumeration — the root-cause categories this CVE belongs to.
Data Freshness Timeline
(refreshed 11× in last 7d / 46× 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-125
- CVE-2014-2898EG 9.8CRITICAL
- CVE-2014-2897EG 9.8CRITICAL
- CVE-2014-2896EG 9.8CRITICAL
- CVE-2015-9290EG 9.8CRITICAL
- CVE-2016-10749EG 9.8CRITICAL
- CVE-1999-0006EG 9.8EPSS 96%CRITICAL
- CVE-2014-3180EG 9.1CRITICAL
- CVE-2014-1508EG 9.1EPSS 90%CRITICAL
- CVE-2016-5198NVD 8.8EG 9.0 KEVEPSS 98%HIGH
- CVE-2016-4523NVD 7.5EG 9.0 KEVEPSS 98%HIGH
Frequently asked(5)
What is CVE-2020-15265?
When was CVE-2020-15265 disclosed?
Is CVE-2020-15265 actively exploited?
What is the CVSS score of CVE-2020-15265?
How do I remediate CVE-2020-15265?
Dependency Blast Radius
See which npm, PyPI, Go, and Maven packages are affected by CVE-2020-15265
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