AI Sec Watch

LangChain, a framework for building applications powered by LLMs (large language models), had a serialization injection vulnerability (a flaw where specially crafted data can be misinterpreted as legitimate code during the conversion of objects to JSON format) in its toJSON() method. The vulnerability occurred because the method failed to properly escape objects containing 'lc' keys, which LangChain uses internally to mark serialized objects, allowing attackers to trick the system into treating malicious user data as legitimate LangChain objects when deserializing (converting back from JSON format).

LangChain, a framework for building AI agents and applications powered by large language models, had a serialization injection vulnerability (a flaw in how it converts data to stored formats) in its dumps() and dumpd() functions before versions 0.3.81 and 1.2.5. The functions failed to properly escape dictionaries containing 'lc' keys, which LangChain uses internally to mark serialized objects, allowing attackers to trick the system into treating user-supplied data as legitimate LangChain objects during deserialization (converting stored data back into usable form).

A vulnerability in Hugging Face Transformers GLM4 allows attackers to run harmful code on a system by tricking users into opening a malicious file or visiting a malicious webpage. The problem occurs because the software doesn't properly check data when loading model weights (the numerical values that make the AI work), allowing deserialization of untrusted data (converting unsafe external files into code the system will execute).

A vulnerability in Hugging Face Transformers' X-CLIP checkpoint conversion allows attackers to execute arbitrary code (running commands they choose on a system) by tricking users into opening malicious files or visiting malicious pages. The flaw occurs because the code doesn't properly validate checkpoint data before deserializing it (converting stored data back into usable objects), which lets attackers inject malicious code that runs with the same permissions as the application.

A vulnerability in Hugging Face Transformers' HuBERT convert_config function allows attackers to execute arbitrary code (RCE, or remote code execution, where an attacker runs commands on a system) by tricking users into converting a malicious checkpoint (a saved model file). The flaw occurs because the function doesn't properly validate user input before using it to run Python code.

Hugging Face Transformers (a popular library for working with AI language models) has a vulnerability in its SEW-D convert_config function that allows attackers to run arbitrary code (any commands they want) on a victim's computer. The flaw exists because the function doesn't properly check user input before using it to execute Python code, and an attacker can exploit this by tricking a user into converting a malicious checkpoint (a saved model file).

A vulnerability in Hugging Face Transformers (a popular AI library) allows attackers to run arbitrary code on a user's computer through a malicious checkpoint (a saved model file). The flaw exists in the convert_config function, which doesn't properly validate user input before executing it as Python code, meaning an attacker can trick a user into converting a malicious checkpoint to execute code with the user's permissions.

A vulnerability in Hugging Face Transformers (a popular library for working with AI language models) allows attackers to run arbitrary code on a computer by tricking users into opening malicious files or visiting malicious websites. The flaw occurs because the software doesn't properly check data when loading saved model checkpoints (files that store a model's learned parameters), which lets attackers execute code by sending untrusted data through deserialization (the process of converting stored data back into usable objects).

A vulnerability in Hugging Face Transformers' Transformer-XL model allows attackers to run arbitrary code (remote code execution) on a victim's computer by tricking them into opening a malicious file or visiting a malicious webpage. The flaw occurs because the software doesn't properly validate data when reading model files, allowing attackers to exploit the deserialization process (converting saved data back into objects that the program can use) to inject and execute malicious code.