rainbutterfly/cve_corpus huggingface dataset:
CVE-2022-23552 Grafana stored XSS in FileUploader component 
Grafana is an open-source platform for monitoring and observability. Starting with the 8.1 branch and prior to versions 8.5.16, 9.2.10, and 9.3.4, Grafana had a stored XSS vulnerability affecting the core plugin GeoMap. The stored XSS vulnerability was possible because SVG files weren't properly sanitized and allowed arbitrary JavaScript to be executed in the context of the currently authorized user of the Grafana instance. 

An attacker needs to have the Editor role in order to change a panel to include either an external URL to a SVG-file containing JavaScript, or use the `data:` scheme to load an inline SVG-file containing JavaScript. This means that vertical privilege escalation is possible, where a user with Editor role can change to a known password for a user having Admin role if the user with Admin role executes malicious JavaScript viewing a dashboard.   

Users may upgrade to version 8.5.16, 9.2.10, or 9.3.4 to receive a fix.
Problem types: cwe_id: CWE-79
description: CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Affected products: vendor: grafana
product: grafana
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: NONE
baseScore: 7.3
baseSeverity: HIGH
confidentialityImpact: HIGH
integrityImpact: HIGH
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: REQUIRED
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:N
version: 3.1
Published: 2023-01-27T22:59:16.675Z
CVE-2022-23553 URL access filters bypass in Alpine
Alpine is a scaffolding library in Java. Alpine prior to version 1.10.4 allows URL access filter bypass. This issue has been fixed in version 1.10.4. There are no known workarounds.
Problem types: cwe_id: CWE-863
description: CWE-863: Incorrect Authorization
Affected products: vendor: stevespringett
product: alpine
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: NONE
baseScore: 7.5
baseSeverity: HIGH
confidentialityImpact: HIGH
integrityImpact: NONE
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N
version: 3.1
Published: 2022-12-28T18:01:14.741Z
CVE-2022-23554 Authentication bypass in Alpine
Alpine is a scaffolding library in Java. Alpine prior to version 1.10.4 allows Authentication Filter bypass. The AuthenticationFilter relies on the request URI to evaluate if the user is accessing the swagger endpoint. By accessing a URL with a path such as /api/foo;%2fapi%2fswagger the contains condition will hold and will return from the authentication filter without aborting the request. Note that the principal object will not be assigned and therefore the issue wont allow user impersonation. This issue has been fixed in version 1.10.4. There are no known workarounds.
Problem types: cwe_id: CWE-287
description: CWE-287: Improper Authentication
Affected products: vendor: stevespringett
product: alpine
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: NONE
baseScore: 6.5
baseSeverity: MEDIUM
confidentialityImpact: LOW
integrityImpact: LOW
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N
version: 3.1
Published: 2022-12-28T18:12:41.586Z
CVE-2022-23555 authentik vulnerable to Improper Authentication via invitation URL token reuse
authentik is an open-source Identity Provider focused on flexibility and versatility. Versions prior to 2022.11.4 and 2022.10.4 are vulnerable to Improper Authentication. Token reuse in invitation URLs leads to access control bypass via the use of a different enrollment flow than in the one provided. The vulnerability allows an attacker that knows different invitation flows names (e.g. `enrollment-invitation-test` and `enrollment-invitation-admin`) via either different invite links or via brute forcing to signup via a single invitation url for any valid invite link received (it can even be a url for a third flow as long as it's a valid invite) as the token used in the `Invitations` section of the Admin interface does NOT change when a different `enrollment flow` is selected via the interface and it is NOT bound to the selected flow, so it will be valid for any flow when used. This issue is patched in authentik 2022.11.4,2022.10.4 and 2022.12.0. Only configurations that use invitations and have multiple enrollment flows with invitation stages that grant different permissions are affected. The default configuration is not vulnerable, and neither are configurations with a single enrollment flow.  As a workaround, fixed data can be added to invitations which can be checked in the flow to deny requests. Alternatively, an identifier with high entropy (like a UUID) can be used as flow slug, mitigating the attack vector by exponentially decreasing the possibility of discovering other flows.
Problem types: cwe_id: CWE-287
description: CWE-287: Improper Authentication
Affected products: vendor: goauthentik
product: authentik
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: LOW
baseScore: 9.4
baseSeverity: CRITICAL
confidentialityImpact: HIGH
integrityImpact: HIGH
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:L
version: 3.1
Published: 2022-12-28T00:12:35.912Z
CVE-2022-23556 CodeIgniter is vulnerable to IP address spoofing when using proxy
CodeIgniter is a PHP full-stack web framework. This vulnerability may allow attackers to spoof their IP address when the server is behind a reverse proxy. This issue has been patched, please upgrade to version 4.2.11 or later, and configure `Config\App::$proxyIPs`. As a workaround, do not use `$request->getIPAddress()`.

Problem types: cwe_id: CWE-345
description: CWE-345: Insufficient Verification of Data Authenticity
Affected products: vendor: codeigniter4
product: CodeIgniter4
CVSS v3.1: attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: LOW
baseScore: 7
baseSeverity: HIGH
confidentialityImpact: HIGH
integrityImpact: LOW
privilegesRequired: NONE
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:L/A:L
version: 3.1
Published: 2022-12-22T18:50:55.443Z
CVE-2022-23557 Division by zero in TFLite
Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would trigger a division by zero in `BiasAndClamp` implementation. There is no check that the `bias_size` is non zero. 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.
Problem types: cwe_id: CWE-369
description: CWE-369: Divide By Zero
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 6.5
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2022-02-04T22:32:44.000Z
CVE-2022-23558 Integer overflow in TFLite array creation
Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an integer overflow in `TfLiteIntArrayCreate`. The `TfLiteIntArrayGetSizeInBytes` returns an `int` instead of a `size_t. An attacker can control model inputs such that `computed_size` overflows the size of `int` datatype. 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.
Problem types: cwe_id: CWE-190
description: CWE-190: Integer Overflow or Wraparound
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 7.6
baseSeverity: HIGH
confidentialityImpact: LOW
integrityImpact: LOW
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H
version: 3.1
Published: 2022-02-04T22:32:43.000Z
CVE-2022-23559 Integer overflow in TFLite
Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both `embedding_size` and `lookup_size` are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write. Users are advised to upgrade to a patched version.
Problem types: cwe_id: CWE-190
description: CWE-190: Integer Overflow or Wraparound
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 8.8
baseSeverity: HIGH
confidentialityImpact: HIGH
integrityImpact: HIGH
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
version: 3.1
Published: 2022-02-04T22:32:37.000Z
CVE-2022-23560 Read and Write outside of bounds in TFLite
Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors. The fix is 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. Users are advised to upgrade as soon as possible.
Problem types: cwe_id: CWE-125
description: CWE-125: Out-of-bounds Read
cwe_id: CWE-787
description: CWE-787: Out-of-bounds Write
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 8.8
baseSeverity: HIGH
confidentialityImpact: HIGH
integrityImpact: HIGH
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
version: 3.1
Published: 2022-02-04T22:32:36.000Z
CVE-2022-23561 Out of bounds write in TFLite
Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions. 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.
Problem types: cwe_id: CWE-787
description: CWE-787: Out-of-bounds Write
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 8.8
baseSeverity: HIGH
confidentialityImpact: HIGH
integrityImpact: HIGH
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
version: 3.1
Published: 2022-02-04T22:32:46.000Z
CVE-2022-23562 Integer overflow in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. The implementation of `Range` suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations. 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.
Problem types: cwe_id: CWE-190
description: CWE-190: Integer Overflow or Wraparound
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 7.6
baseSeverity: HIGH
confidentialityImpact: LOW
integrityImpact: LOW
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H
version: 3.1
Published: 2022-02-04T22:32:39.000Z
CVE-2022-23563 Insecure temporary file in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. In multiple places, TensorFlow uses `tempfile.mktemp` to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in `mktemp` and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the `mktemp` function usage. We have patched the issue in several commits, replacing `mktemp` with the safer `mkstemp`/`mkdtemp` functions, according to the usage pattern. Users are advised to upgrade as soon as possible.
Problem types: cwe_id: CWE-367
description: CWE-367: Time-of-check Time-of-use (TOCTOU) Race Condition
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: LOCAL
availabilityImpact: NONE
baseScore: 7.1
baseSeverity: HIGH
confidentialityImpact: HIGH
integrityImpact: HIGH
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N
version: 3.1
Published: 2022-02-04T22:32:38.000Z
CVE-2022-23564 Reachable Assertion in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes. 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.
Problem types: cwe_id: CWE-617
description: CWE-617: Reachable Assertion
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 6.5
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2022-02-04T22:32:41.000Z
CVE-2022-23565 `CHECK`-failures in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. An attacker can trigger denial of service via assertion failure by altering a `SavedModel` on disk such that `AttrDef`s of some operation are duplicated. 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.
Problem types: cwe_id: CWE-617
description: CWE-617: Reachable Assertion
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 6.5
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2022-02-04T22:32:40.000Z
CVE-2022-23566 Out of bounds write in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. TensorFlow is vulnerable to a heap OOB write in `Grappler`. The `set_output` function writes to an array at the specified index. Hence, this gives a malicious user a write primitive. 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.
Problem types: cwe_id: CWE-787
description: CWE-787: Out-of-bounds Write
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 8.8
baseSeverity: HIGH
confidentialityImpact: HIGH
integrityImpact: HIGH
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
version: 3.1
Published: 2022-02-04T22:32:33.000Z
CVE-2022-23567 Integer overflows in Tensorflow
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.
Problem types: description: n/a
Affected products: vendor: n/a
product: n/a
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 6.5
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2022-02-03T11:52:48.000Z
CVE-2022-23568 Integer overflows in Tensorflow
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.
Problem types: description: n/a
Affected products: vendor: n/a
product: n/a
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 6.5
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2022-02-03T11:42:54.000Z
CVE-2022-23569 `CHECK`-fails when building invalid tensor shapes in Tensorflow
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.
Problem types: description: n/a
Affected products: vendor: n/a
product: n/a
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 6.5
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2022-02-03T12:47:29.000Z
CVE-2022-23570 Null-dereference in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a `DCHECK`. However, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.
Problem types: cwe_id: CWE-476
description: CWE-476: NULL Pointer Dereference
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 6.5
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2022-02-04T22:32:42.000Z
CVE-2022-23571 Reachable Assertion in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments, if the tensors have an invalid `dtype` and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes. 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.
Problem types: cwe_id: CWE-617
description: CWE-617: Reachable Assertion
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 6.5
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2022-02-04T22:32:34.000Z
CVE-2022-23572 Crash when type cannot be specialized in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a type during shape inference. This case is covered by the `DCHECK` function however, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the `ValueOrDie` line. This results in an assertion failure as `ret` contains an error `Status`, not a value. In the second case we also get a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.
Problem types: cwe_id: CWE-754
description: CWE-754: Improper Check for Unusual or Exceptional Conditions
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 6.5
baseSeverity: MEDIUM
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
version: 3.1
Published: 2022-02-04T22:32:29.000Z
CVE-2022-23573 Uninitialized variable access in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized. 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.
Problem types: cwe_id: CWE-908
description: CWE-908: Use of Uninitialized Resource
Affected products: vendor: tensorflow
product: tensorflow
CVSS v3.1: attackComplexity: LOW
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 7.6
baseSeverity: HIGH
confidentialityImpact: LOW
integrityImpact: LOW
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H
version: 3.1
Published: 2022-02-04T22:32:28.000Z