Segfault in Tensorflow
Default status is the baseline for the product, each version can override it (e.g. patched versions marked unaffected).
| Vendor | Product | Default status | Versions | ||||||
|---|---|---|---|---|---|---|---|---|---|
| tensorflow | tensorflow | affected |
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No data available yet.
| Source | ID | Title |
|---|---|---|
EUVD |
EUVD-2020-0196 | In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will never be 1, hence there will be no hashmap at index 0 in `per_batch_counts`. Trying to access that in the user code results in a segmentation fault. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. |
Github GHSA |
GHSA-x7rp-74x2-mjf3 | Segfault in Tensorflow |
No CVSS v4.0
No CVSS v3.0
This CVE is not in the KEV list.
Exploit prediction score: < 1% (0.851% probability within 30 days)
Key SSVC decision points have not yet been added.
No history.