AI Sec Watch

CVE-2024-10569 is a vulnerability in Gradio's dataframe component that allows a zip bomb attack (a compressed file designed to crash systems when decompressed). An attacker can upload a malicious compressed file, which the component processes using pd.read_csv (a function that reads spreadsheet data), causing the server to crash and become unavailable.

CVE-2024-10188 is a vulnerability in BerriAI/litellm that allows unauthenticated users to crash the litellm Python server by exploiting unsafe input parsing. The vulnerability exists because the code uses ast.literal_eval (a Python function that evaluates code, which is not safe for untrusted input) to process user-supplied data, making it vulnerable to DoS (denial of service, where attackers make a service unavailable) attacks.

CVE-2024-8502 is a vulnerability in modelscope/agentscope v0.0.6a3 where the RpcAgentServerLauncher class unsafely deserializes (converts serialized data back into code) untrusted data using the dill library, allowing attackers to execute arbitrary commands on the server. The vulnerability exists in the AgentServerServicer.create_agent method, which directly deserializes user input without validation.

CVE-2024-12911 is a vulnerability in the `default_jsonalyzer` function of `JSONalyzeQueryEngine` in the llama_index library that allows attackers to perform SQL injection (inserting malicious SQL commands) through prompt injection (hiding hidden instructions in the AI's input). This can lead to arbitrary file creation and denial-of-service attacks (making a system unavailable by overwhelming it).

InvokeAI versions 5.3.1 through 5.4.2 contain a remote code execution vulnerability (the ability for attackers to run commands on a system they don't own) in the model installation API. The flaw comes from unsafe deserialization (converting data back into usable code without checking if it's trustworthy) of model files using torch.load, which allows attackers to hide malicious code in model files that gets executed when loaded.

In gpt_academic version 3.83 and earlier, the CodeInterpreter plugin has a vulnerability where prompt injection (tricking an AI by hiding instructions in its input) allows attackers to inject malicious code. Because the application executes LLM-generated code without a sandbox (a restricted environment that isolates code from the main system), attackers can achieve RCE (remote code execution, where an attacker can run commands on a system they don't own) and potentially take over the backend server.

Applio, a voice conversion tool, has a vulnerability in versions 3.2.8-bugfix and earlier where it unsafely deserializes (converts untrusted data back into code objects) user-supplied model file paths using torch.load, which can allow attackers to run arbitrary code on the system. The vulnerability exists in the inference.py and tts.py files, where user input is passed directly to functions that load models without proper validation.

Applio, a voice conversion tool, has a vulnerability in versions 3.2.8-bugfix and earlier where it unsafely deserializes (reconstructs objects from stored data without validation) user-supplied model files using `torch.load`, which could allow attackers to run arbitrary code on the affected system.

Applio, a voice conversion tool, has a vulnerability in versions 3.2.8-bugfix and earlier where it unsafely deserializes (converts untrusted data back into objects) user-supplied model files using `torch.load`, potentially allowing attackers to run arbitrary code on affected systems.