In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based 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 sorted, 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.
This vulnerability carries a HIGH severity rating with a CVSS v3.1 score of 8.1, indicating it can be exploited remotely over the network but requires specific conditions to be met without requiring user interaction and does not require pre-existing privileges . The vulnerability impacts limited data confidentiality, limited integrity, and availability (service disruption) for affected systems. Impacting 1 product from google organizations running these solutions should prioritize assessment and patching.
Reported in 2020, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.
2020-09-25T19:15:16.713
2026-06-17T02:56:17.057
Modified
CVSSv3.1: 8.1 (HIGH)
AV:N/AC:M/Au:N/C:P/I:P/A:P
8.6
6.4
| Type | Vendor | Product | Version/Range | Vulnerable? |
|---|---|---|---|---|
| Application | tensorflow | < 2.2.1 | Yes | |
| Application | tensorflow | < 2.3.1 | Yes |
SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For google's affected products, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.