CVE-2020-15210
published 2020-09-25CVE-2020-15210: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator…
medium6.5CVSS 3.1
AVNACHPRNUINSUCNILAH
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Affected
18 ranges
| Vendor | Product | Version range | Fixed in |
|---|---|---|---|
| debian | tensorflow | — | — |
| tensorflow | < 1.15.4 | 1.15.4 | |
| tensorflow | >= 2.0.0 < 2.0.3 | 2.0.3 | |
| tensorflow | >= 2.1.0 < 2.1.2 | 2.1.2 | |
| tensorflow | >= 2.2.0 < 2.2.1 | 2.2.1 | |
| tensorflow | >= 2.3.0 < 2.3.1 | 2.3.1 | |
| intel | optimization_for_tensorflow | >= 0 < 1.15.4 | 1.15.4 |
| intel | optimization_for_tensorflow | >= 0 < d58c96946b2880991d63d1dacacb32f0a4dfa453 | d58c96946b2880991d63d1dacacb32f0a4dfa453 |
| intel | optimization_for_tensorflow | >= 2.0.0 < 2.0.3 | 2.0.3 |
| intel | optimization_for_tensorflow | >= 2.1.0 < 2.1.2 | 2.1.2 |
| intel | optimization_for_tensorflow | >= 2.2.0 < 2.2.1 | 2.2.1 |
| intel | optimization_for_tensorflow | >= 2.3.0 < 2.3.1 | 2.3.1 |
| opensuse | leap | — | — |
| tensorflow | tensorflow | < 1.15.4 | 1.15.4 |
| tensorflow | tensorflow | — | — |
| tensorflow | tensorflow | — | — |
| tensorflow | tensorflow | — | — |
| tensorflow | tensorflow | — | — |
OSV
Segmentation fault in tensorflow-lite
osv·2020-09-25
CVE-2020-15210 [HIGH] Segmentation fault in tensorflow-lite
Segmentation fault in tensorflow-lite
### Impact
If a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption.
### Patches
We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
### Workarounds
A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that no operator reuses tensors as both inputs and outputs. Care should be taken to check all types of inputs (i.e., constant or variable tensors as well as optional tensors).
### For more information
Please consult [our security guide](https://gi
GHSA
Segmentation fault in tensorflow-lite
ghsa·2020-09-25
CVE-2020-15210 [HIGH] CWE-20 Segmentation fault in tensorflow-lite
Segmentation fault in tensorflow-lite
### Impact
If a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption.
### Patches
We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
### Workarounds
A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that no operator reuses tensors as both inputs and outputs. Care should be taken to check all types of inputs (i.e., constant or variable tensors as well as optional tensors).
### For more information
Please consult [our security guide](https://gi
OSV
CVE-2020-15210: In tensorflow-lite before versions 1
osv·2020-09-25
CVE-2020-15210 CVE-2020-15210: In tensorflow-lite before versions 1
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Debian
CVE-2020-15210: tensorflow - In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a T...
vendor_debian·2020·CVSS 6.5
CVE-2020-15210 [MEDIUM] CVE-2020-15210: tensorflow - In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a T...
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Scope: local
forky: resolved
sid: resolved
No detection rules found.
No public exploits indexed.
No writeups or analysis indexed.
http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.htmlhttps://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.htmlhttps://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2
2020-09-25
Published