Buffer overflow in Google Tensorflow
CVE-2020-15201
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Vulnerability class: Drupalgeddon 2 (CVE-2018-7600)
EPSS: 0.006 (43.7th percentile) — read the EPSS interpretation.
CVSS v3 metric
CVSS v3 base score 4.8 (Medium). Vector: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N.
Affected products
- Google Tensorflow — versions 2.3.0
- Tensorflow — versions = 2.3.0
Weakness classification (CWE)
References
- security-advisories@github.com (Third Party Advisory, x_refsource_MISC)
- security-advisories@github.com (Patch, Third Party Advisory, x_refsource_MISC)
- security-advisories@github.com (x_refsource_CONFIRM, Exploit, Third Party Advisory)
Frequently asked questions
- What is CVE-2020-15201?
- CVE-2020-15201 is a medium-severity vulnerability in Google Tensorflow, classified under Improper Input Validation. CVSS score: 4.8/10. Published 2020-09-25.
- How severe is CVE-2020-15201?
- Medium severity. CVSS v3 base score is 4.8 out of 10.