CVE-2020-15213

描述

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.

如何修補 CVE-2020-15213

要修補 CVE-2020-15213,請將受影響套件升級到下列已修補版本。

• —升級至 2.2.1 或更新版本
• —升級至 2.2.1 或更新版本
• —升級至 2.2.1 或更新版本

CVE-2020-15213 正在被利用嗎?

低 — EPSS 為 0.6%,目前沒有觀察到大規模利用活動。

受影響套件(3)

• >= 2.2.0, < 2.2.1
• >= 2.2.0, < 2.2.1
• >= 2.2.0, < 2.2.1

CVSS 分數

參考連結(11)

• ADVISORYnvd.nist.gov/vuln/detail/CVE-2020-15213
• PATCHgithub.com/tensorflow/tensorflow
• WEBgithub.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-293.yaml
• WEBgithub.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-328.yaml