CVE-2020-15208
published 2020-09-25CVE-2020-15208: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK`…
critical9.8CVSS 3.1
AVNACLPRNUINSUCHIHAH
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 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 < 8ee24e7949a203d234489f9da2c5bf45a7d5157d | 8ee24e7949a203d234489f9da2c5bf45a7d5157d |
| 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 | — | — |
Debian
CVE-2020-15208: tensorflow - In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when d...
vendor_debian·2020·CVSS 7.4
CVE-2020-15208 [HIGH] CVE-2020-15208: tensorflow - In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when d...
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Scope: local
forky: resolved
sid: resolved
OSV
Data corruption in tensorflow-lite
osv·2020-09-25
CVE-2020-15208 [HIGH] Data corruption in tensorflow-lite
Data corruption in tensorflow-lite
### Impact
When determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/types.h#L437-L442
Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors.
### Patches
We have patched the issue in 8ee24e7949a20 and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.
OSV
CVE-2020-15208: In tensorflow-lite before versions 1
osv·2020-09-25
CVE-2020-15208 CVE-2020-15208: 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, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
GHSA
Data corruption in tensorflow-lite
ghsa·2020-09-25
CVE-2020-15208 [HIGH] CWE-125 Data corruption in tensorflow-lite
Data corruption in tensorflow-lite
### Impact
When determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/types.h#L437-L442
Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors.
### Patches
We have patched the issue in 8ee24e7949a20 and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.
No detection rules found.
No public exploits indexed.
http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.htmlhttps://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157dhttps://github.com/tensorflow/tensorflow/releases/tag/v2.3.1https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mxjj-953w-2c2vhttp://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.htmlhttps://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157dhttps://github.com/tensorflow/tensorflow/releases/tag/v2.3.1https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mxjj-953w-2c2v
2020-09-25
Published