AI & LLM Vulnerabilities

The dedupe Python library (which uses machine learning for fuzzy matching, deduplication, and entity resolution on structured data) had a critical vulnerability in its GitHub Actions workflow that allowed attackers to trigger code execution by commenting @benchmark on pull requests, potentially exposing the GITHUB_TOKEN (a credential that grants access to modify repository contents) and leading to repository takeover.

BentoML versions 1.4.0 to 1.4.19 have an SSRF vulnerability (server-side request forgery, where an attacker tricks a server into making requests to internal or restricted addresses) in their file upload feature. An unauthenticated attacker can exploit this to force the server to download files from any URL, including internal network addresses and cloud metadata endpoints (services that store sensitive information), without any validation.

LangChain AI version 0.3.51 contains an indirect prompt injection vulnerability (a technique where attackers hide malicious instructions in data like emails to trick AI systems) in its GmailToolkit component that could allow attackers to run arbitrary code through crafted emails. However, the supplier disputes this, arguing the actual vulnerability comes from user code that doesn't follow LangChain's security guidelines rather than from LangChain itself.

A sandbox escape vulnerability (a security flaw allowing code to break out of a restricted execution environment) was found in huggingface/smolagents version 1.14.0 that lets attackers bypass safety restrictions and achieve remote code execution (RCE, running commands on a system they don't own). The flaw is in the local_python_executor.py module, which failed to properly block Python code execution even though it had safety checks in place.

CVE-2025-49747 is a missing authorization vulnerability (a flaw where a system fails to properly check if a user has permission to perform an action) in Azure Machine Learning that allows someone who already has some access to the system to gain elevated privileges, or higher levels of access, over a network.

CVE-2025-49746 is a vulnerability in Azure Machine Learning where improper authorization (CWE-285, a flaw in how the system checks who is allowed to do what) allows someone who already has legitimate access to gain higher-level privileges over a network. This is categorized as a privilege escalation attack, where an authorized user exploits a weakness to gain permissions they shouldn't normally have.

GPT-SoVITS-WebUI, a tool for voice conversion and text-to-speech, has an unsafe deserialization vulnerability (CWE-502, a weakness where untrusted data is converted back into executable code) in versions 20250228v3 and earlier. The vulnerability exists in process_ckpt.py, where user input for a model file path is passed directly to torch.load without validation, allowing attackers to potentially execute arbitrary code. The vulnerability has a CVSS score (severity rating) of 8.9, indicating it is highly severe.

CVT-2025-49840 is an unsafe deserialization vulnerability (CWE-502, a security flaw where a program processes untrusted data without checking it first) in GPT-SoVITS-WebUI, a tool for voice conversion and text-to-speech. In versions 20250228v3 and earlier, the software unsafely loads user-provided model files using torch.load, allowing attackers to potentially execute malicious code. The vulnerability has a CVSS score (severity rating) of 8.9, indicating high risk.

GPT-SoVITS-WebUI, a tool for converting voices and generating speech from text, has a vulnerability in versions 20250228v3 and earlier where user input (like a file path) is passed directly to torch.load, a function that can execute malicious code when loading files. An attacker could exploit this by providing a specially crafted model file that runs unauthorized code on the system.