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Vulnerability details: VCID-1ugt-z92x-nfbr
Vulnerability ID VCID-1ugt-z92x-nfbr
Aliases CVE-2020-15212
GHSA-hx2x-85gr-wrpq
PYSEC-2020-135
PYSEC-2020-292
PYSEC-2020-327
Summary 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.
Status Published
Exploitability None
Weighted Severity None
Risk None
Affected and Fixed Packages Package Details
Weaknesses (1)
No exploits are available.
Exploit Prediction Scoring System (EPSS)
Percentile 0.47076
EPSS Score 0.00238
Published At May 30, 2026, 12:55 p.m.
Date Actor Action Source VulnerableCode Version
2026-05-30T20:19:47.107483+00:00 Pypa Importer Import https://github.com/pypa/advisory-database/blob/main/vulns/tensorflow-cpu/PYSEC-2020-292.yaml 38.6.0