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CVE-2020-15212

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Date: September 25, 2020

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. 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 the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. 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.

Language: Python

Severity Score

Severity Score

Weakness Type (CWE)

Out-of-bounds Write

CWE-787

Top Fix

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Upgrade to version v2.2.1,v2.3.1

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CVSS v3.1

Base Score:
Attack Vector (AV): NETWORK
Attack Complexity (AC): LOW
Privileges Required (PR): NONE
User Interaction (UI): NONE
Scope (S): UNCHANGED
Confidentiality (C): LOW
Integrity (I): LOW
Availability (A): HIGH

CVSS v2

Base Score:
Access Vector (AV): NETWORK
Access Complexity (AC): LOW
Authentication (AU): NONE
Confidentiality (C): PARTIAL
Integrity (I): PARTIAL
Availability (A): PARTIAL
Additional information:

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