Deserialization of Untrusted Data

There is a vulnerability in AVEVA PI Web API that could allow malicious code to execute on the PI Web API environment under the privileges of an interactive user that was socially engineered to use API XML import functionality with content supplied by an attacker.Show less

There is a vulnerability in AVEVA PI Asset Framework Client that could allow malicious code to execute on the PI System Explorer environment under the privileges of an interactive user that was socially engineered to import XML supplied by an attacker.Show less

Dell Common Event Enabler, version 8.9.10.0 and prior, contain an insecure deserialization vulnerability in CAVATools. A local unauthenticated attacker could potentially exploit this vulnerability, leading to arbitrary code execution in the context of the logged in user. Exploitation of this issue requires a victim to open a malicious file.Show less

Untrusted data deserialization vulnerability has been found in Mentor - Employee Portal, affecting version 3.83.35. This vulnerability could allow an attacker to execute arbitrary code, by injecting a malicious payload into the “ViewState” field.Show less

Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal'), Deserialization of Untrusted Data vulnerability in BdThemes Element Pack Pro allows Path Traversal, Object Injection.This issue affects Element Pack Pro: from n/a before 7.19.3.Show less

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded PyTorch model to run arbitrary code on an end user’s system when interacted with.Show less

Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.5.0 or newer, enabling a maliciously uploaded Langchain AgentExecutor model to run arbitrary code on an end user’s system when interacted with.Show less

Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end user’s system when interacted with.Show less

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.23.0 or newer, enabling a maliciously uploaded LightGBM scikit-learn model to run arbitrary code on an end user’s system when interacted with.Show less

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.24.0 or newer, enabling a maliciously uploaded pmdarima model to run arbitrary code on an end user’s system when interacted with.Show less

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with.Show less

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.Show less

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.Show less