#android

In Bluetooth, there is a possible information disclosure due to incorrect error handling. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS06108487; Issue ID: ALPS06108487.

There is an improper memory access permission configuration on ACPU.Successful exploitation of this vulnerability may cause out-of-bounds access.

Improper access control in the Intel(R) Capital Global Summit Android application may allow an authenticated user to potentially enable information disclosure via local access.

Improper access control in the Intel(R) Smart Campus Android application before version 6.1 may allow authenticated user to potentially enable information disclosure via local access.

Microsoft OneDrive for Android Security Feature Bypass Vulnerability

Microsoft OneDrive for Android Security Feature Bypass Vulnerability.

Server Side Request Forgery (SSRF) vulneraility exists in Gitea before 1.7.0 using the OpenID URL.

**What privileges are required to exploit this vulnerability?** The attacker needs access to an unlocked mobile device to exploit the vulnerability.

Tensorflow is an Open Source Machine Learning Framework. TensorFlow's type inference can cause a heap out of bounds read as the bounds checking is done in a `DCHECK` (which is a no-op during production). An attacker can control the `input_idx` variable such that `ix` would be larger than the number of values in `node_t.args`. The fix will be included in TensorFlow 2.8.0. This is the only affected version.

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that any binary op would trigger `CHECK` failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the `dtype` no longer matches the `dtype` expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If `Tin` and `Tout` don't match the type of data in `out` and `input_*` tensors then `flat<*>` would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a `CHECK` crash, hence a denial of service. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.