CVE-2020-5215

low-risk
Published 2020-01-28

In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled. This issue is patched in TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched. TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected. Users are encouraged to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0.

Do I need to act?

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0.23% chance of exploitation
EPSS score — low exploit probability
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Not on CISA KEV list
No confirmed active exploitation reported to CISA
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Patch status unknown
Check vendor advisories for fix availability and mitigation guidance
5
CVSS 5.0/10 Medium
LOCAL / HIGH complexity

Affected Products (1)

Affected Vendors

19
/ 100
low-risk
Severity 13/34 · Low
Exploitability 1/34 · Minimal
Exposure 5/34 · Minimal