Vulnerabilities (CVE)

In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.

In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.

In devinfo, there is a possible information disclosure due to a missing SELinux policy. This could lead to local information disclosure of device identifier with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS09555228; Issue ID: MSV-2760.

The AES-GCM specification in RFC 5084, as used in Android 5.x and 6.x, recommends 12 octets for the aes-ICVlen parameter field, which might make it easier for attackers to defeat a cryptographic protection mechanism and discover an authentication key via a crafted application, aka internal bug 26234568. NOTE: The vendor disputes the existence of this potential issue in Android, stating "This CVE was raised in error: it referred to the authentication tag size in GCM, whose default according to A ... The AES-GCM specification in RFC 5084, as used in Android 5.x and 6.x, recommends 12 octets for the aes-ICVlen parameter field, which might make it easier for attackers to defeat a cryptographic protection mechanism and discover an authentication key via a crafted application, aka internal bug 26234568. NOTE: The vendor disputes the existence of this potential issue in Android, stating "This CVE was raised in error: it referred to the authentication tag size in GCM, whose default according to ASN.1 encoding (12 bytes) can lead to vulnerabilities. After careful consideration, it was decided that the insecure default value of 12 bytes was a default only for the encoding and not default anywhere else in Android, and hence no vulnerability existed.

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In keyInstall, there is a possible escalation of privilege due to type confusion. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS08358566; Issue ID: ALPS08358566.

there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

Use after free in Peer Connection in Google Chrome prior to 121.0.6167.139 allowed a remote attacker to potentially exploit stack corruption via a crafted HTML page. (Chromium security severity: High)

In thermal, there is a possible out of bounds write due to a race condition. This could lead to local escalation of privilege if a malicious actor has already obtained the System privilege. User interaction is not needed for exploitation. Patch ID: ALPS09698599; Issue ID: MSV-3228.

In scp, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege if a malicious actor has already obtained the System privilege. User interaction is not needed for exploitation. Patch ID: ALPS09625562; Issue ID: MSV-3027.

Use after free in CSS in Google Chrome prior to 106.0.5249.62 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

Insufficient policy enforcement in developer tools in Google Chrome prior to 106.0.5249.62 allowed a remote attacker to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

Use after free in media in Google Chrome prior to 106.0.5249.62 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

Use after free in survey in Google Chrome on ChromeOS prior to 106.0.5249.62 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

Use after free in survey in Google Chrome on ChromeOS prior to 106.0.5249.62 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. (Chromium security severity: High)

Use after free in logging in Google Chrome prior to 106.0.5249.62 allowed a remote attacker who had compromised a WebUI process to potentially perform a sandbox escape via a crafted HTML page. (Chromium security severity: Medium)

Incorrect security UI in full screen in Google Chrome prior to 106.0.5249.62 allowed a remote attacker to spoof security UI via a crafted HTML page. (Chromium security severity: Medium)

Insufficient validation of untrusted input in VPN in Google Chrome on ChromeOS prior to 106.0.5249.62 allowed a local attacker to bypass managed device restrictions via physical access to the device. (Chromium security severity: Medium)

Insufficient policy enforcement in custom tabs in Google Chrome on Android prior to 106.0.5249.62 allowed an attacker who convinced the user to install an application to bypass same origin policy via a crafted application. (Chromium security severity: Medium)

Use after free in assistant in Google Chrome on ChromeOS prior to 106.0.5249.62 allowed a remote attacker who convinced a user to engage in specific UI gestures to potentially perform a sandbox escape via specific UI gestures. (Chromium security severity: Medium)

In modem-ps-nas-ngmm, there is a possible undefined behavior due to incorrect error handling. This could lead to remote information disclosure no additional execution privileges needed

In SecurityCommand message after as security has been actived., there is a possible improper input validation. This could lead to remote information disclosure no additional execution privileges needed

In modem-ps-nas-ngmm, there is a possible undefined behavior due to incorrect error handling. This could lead to remote information disclosure no additional execution privileges needed

In modem driver, there is a possible system crash due to improper input validation. This could lead to local information disclosure with System execution privileges needed

In ril service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In ril service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In ril service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In modem-ps-nas-ngmm, there is a possible undefined behavior due to incorrect error handling. This could lead to remote information disclosure no additional execution privileges needed

In ngmm, there is a possible undefined behavior due to incorrect error handling. This could lead to remote denial of service with no additional execution privileges needed

In vsp driver, there is a possible missing verification incorrect input. This could lead to local denial of service with no additional execution privileges needed

In camera driver, there is a possible use after free due to a logic error. This could lead to local denial of service with System execution privileges needed

In wifi display, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed.

In Plaintext COUNTER CHECK message accepted before AS security activation, there is a possible missing permission check. This could lead to remote information disclosure no additional execution privileges needed

Tensorflow is an Open Source Machine Learning Framework. Multiple operations in TensorFlow can be used to trigger a denial of service via `CHECK`-fails (i.e., assertion failures). This is similar to TFSA-2021-198 and has similar fixes. We have patched the reported issues in multiple GitHub commits. It is possible that other similar instances exist in TensorFlow, we will issue fixes as these are discovered. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on Tenso ... Tensorflow is an Open Source Machine Learning Framework. Multiple operations in TensorFlow can be used to trigger a denial of service via `CHECK`-fails (i.e., assertion failures). This is similar to TFSA-2021-198 and has similar fixes. We have patched the reported issues in multiple GitHub commits. It is possible that other similar instances exist in TensorFlow, we will issue fixes as these are discovered. 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.

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Tensorflow is an Open Source Machine Learning Framework. The implementation of `AddManySparseToTensorsMap` is vulnerable to an integer overflow which results in a `CHECK`-fail when building new `TensorShape` objects (so, an assert failure based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit o ... Tensorflow is an Open Source Machine Learning Framework. The implementation of `AddManySparseToTensorsMap` is vulnerable to an integer overflow which results in a `CHECK`-fail when building new `TensorShape` objects (so, an assert failure based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. 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.

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Tensorflow is an Open Source Machine Learning Framework. The implementations of `Sparse*Cwise*` ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or `CHECK`-fails when building new `TensorShape` objects (so, assert failures based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. The fix will be included i ... Tensorflow is an Open Source Machine Learning Framework. The implementations of `Sparse*Cwise*` ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or `CHECK`-fails when building new `TensorShape` objects (so, assert failures based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. 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.

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Tensorflow is an Open Source Machine Learning Framework. ### Impact An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive. The fix will be included in Tensor ... Tensorflow is an Open Source Machine Learning Framework. ### Impact An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive. 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.

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Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseCountSparseOutput` is vulnerable to a heap overflow. 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.

Tensorflow is an Open Source Machine Learning Framework. The implementation of `QuantizedMaxPool` has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer. 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.

Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseCountSparseOutput` can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation. 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.

Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated. The fix will be included in TensorFlow 2.8. ... Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated. 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.

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