AI & LLM Vulnerabilities

CVE-2023-7018 is a deserialization of untrusted data vulnerability (a flaw where an AI library unsafely processes data from untrusted sources) in the Hugging Face Transformers library before version 4.36. This weakness could potentially allow an attacker to execute malicious code through specially crafted input.

CVE-2023-6730 is a deserialization of untrusted data vulnerability (a security flaw where a program unsafely reconstructs objects from untrusted input, potentially allowing attackers to execute malicious code) found in the Hugging Face Transformers library before version 4.36. The vulnerability has a CVSS score of 4.0, which indicates a moderate severity level (a 0-10 rating of how severe a vulnerability is).

CVE-2023-6909 is a path traversal vulnerability (a security flaw where an attacker can access files outside their intended directory using special characters like '..\'). It affects MLflow versions before 2.9.2 in the mlflow/mlflow GitHub repository. The vulnerability was discovered and reported through the huntr.dev bug bounty platform.

CVE-2023-6831 is a path traversal vulnerability (a flaw where an attacker can access files outside the intended directory by using special characters like '..\'). in MLflow versions before 2.9.2 that allows attackers to manipulate file paths and access restricted files they shouldn't be able to reach.

CVE-2023-6572 is a command injection vulnerability (a security flaw where an attacker can run unauthorized commands) in the Gradio application (a tool for building AI demos) versions prior to the main branch. The vulnerability results from improper handling of special characters that could allow attackers to execute commands on affected systems.

CVE-2023-6753 is a path traversal vulnerability (a security flaw where an attacker can access files outside the intended directory by using special path characters) found in MLflow versions before 2.9.2. The vulnerability allows unauthorized access to restricted files on a system running the affected software.

CVE-2023-6709 is a vulnerability in MLflow (a machine learning tool) versions before 2.9.2 involving improper neutralization of special elements in a template engine (a system that generates text by filling in placeholders in templates). This weakness could potentially allow attackers to manipulate how the software processes certain input data.

CVE-2023-6020 is a local file inclusion (LFI, a vulnerability that lets attackers read files they shouldn't access) in Ray's /static/ directory that allows attackers to read any file on the server without needing to log in. The vulnerability stems from missing authorization checks (the system doesn't verify whether a user should have access before serving files).

CVE-2023-6015 is a vulnerability in MLflow that allows attackers to upload arbitrary files to the server using PUT requests. This is a path traversal vulnerability (CWE-22, where an attacker can write files outside the intended directory by manipulating file paths), with a CVSS severity score of 4.0 (a moderate-level security issue on a 0-10 scale).

CVE-2023-5245 is a vulnerability in FileUtil.extract() where zip file extraction does not check if file paths are outside the intended directory, allowing attackers to create files anywhere and potentially execute code when TensorflowModel processes a saved model. This is called path traversal (a technique where an attacker uses file paths like '../../../' to escape a restricted folder).

The Infinite Image Browsing extension for Stable Diffusion web UI (a tool for generating images with AI) has a security flaw that allows attackers to read any file on a computer if Gradio authentication is enabled without a secret key configuration. Attackers can exploit this by manipulating URLs with /file?path= to access sensitive files, such as environment variables that might contain login credentials.

CVE-2023-32786 is a prompt injection vulnerability (tricking an AI by hiding instructions in its input) in Langchain version 0.0.155 and earlier that allows attackers to force the service to retrieve data from any URL they choose. This could lead to SSRF (server-side request forgery, where an attacker makes a server request data from unintended locations) and potentially inject harmful content into tasks that use the retrieved data.