AI Sec Watch

pytorch-lightning (a popular machine learning library) contains a vulnerability related to deserialization of untrusted data (CWE-502, where a program unsafely processes data from an untrusted source, potentially allowing an attacker to run malicious code). The vulnerability was identified and reported through the huntr.dev bug bounty program.

Gradio, a framework for building interactive machine learning demos, had a vulnerability in versions before 2.5.0 where users could read any file on the host computer if they knew the file path, since file access wasn't restricted (though files could only be opened in read-only mode). This meant anyone with a link to a Gradio interface could potentially access sensitive files on the server.

Sockeye, an open-source tool for Neural Machine Translation (a type of AI that translates text between languages), had a security flaw in versions before 2.3.24 where it used unsafe YAML loading (a method to read configuration files without proper safety checks). An attacker could hide malicious code in a model's configuration file, and if a user downloaded and ran that model, the hidden code would execute on their computer.

Aim is an open-source tool for tracking machine learning experiments. Versions before 3.1.0 have a path traversal vulnerability (a type of attack where special sequences like '../' are used to access files outside the intended directory), which could allow attackers to read sensitive files like source code, configuration files, or system files on the server.

TensorFlow's `saved_model_cli` tool (a command-line utility for working with machine learning models) has a code injection vulnerability because it runs `eval` on user-supplied strings, which could allow attackers to execute arbitrary code on the system. The risk is limited since the tool is only run manually by users, not automatically.

TensorFlow (an open source machine learning platform) has a vulnerability in the `ImmutableConst` operation that allows attackers to read arbitrary memory contents. The issue occurs because the operation doesn't properly handle a special type of string called `tstring` that can reference memory-mapped data.

TensorFlow's Grappler optimizer (the part of TensorFlow that improves how machine learning models run) has a bug where a variable called `dequeue_node` is never initialized if a saved model doesn't contain a specific type of operation called a `Dequeue` node. This uninitialized variable could cause the optimizer to behave unpredictably or crash.

TensorFlow, an open source platform for machine learning, has a vulnerability in the `SplitV` function where supplying negative arguments can cause a segfault (a crash from accessing invalid memory). The crash happens when the `size_splits` parameter contains multiple values with at least one being negative.

TensorFlow (an open source machine learning platform) has a vulnerability where shape inference code for certain operations can be tricked into accessing invalid memory through a heap buffer overflow (where a program writes data beyond the allocated memory space). This happens because the code doesn't verify that certain input parameters have the correct structure before using them.