Security vulnerabilities, privacy incidents, safety concerns, and policy updates affecting LLMs and AI agents.
CVE-2024-27565 is a server-side request forgery (SSRF, a flaw that allows attackers to trick a server into making unwanted requests to other systems) vulnerability found in the weixin.php file of ChatGPT-wechat-personal at commit a0857f6. This vulnerability lets attackers force the application to make arbitrary requests on their behalf. The vulnerability has a CVSS 4.0 severity rating (a moderate score on a 0-10 scale measuring how serious a security flaw is).
A critical vulnerability was found in LangChain's langchain_community library version 0.0.26 in the TFIDFRetriever component (a tool that retrieves relevant documents for AI systems). The flaw allows server-side request forgery (SSRF, where an attacker tricks a server into making unwanted network requests on their behalf), and it can be exploited remotely.
CVE-2024-27444 is a vulnerability in LangChain Experimental (a Python library for building AI applications) before version 0.1.8 that allows attackers to bypass a previous security fix and run arbitrary code (malicious commands they choose) by using Python's special attributes like __import__ and __globals__, which were not blocked by the pal_chain/base.py security checks.
MLflow, a machine learning platform, has a vulnerability where it doesn't properly clean user input from dataset tables, allowing XSS (cross-site scripting, where attackers inject malicious code into web pages). When someone runs a recipe using an untrusted dataset in Jupyter Notebook, this can lead to RCE (remote code execution, where an attacker can run commands on the user's computer).
MLflow has a vulnerability (CVE-2024-27132) where template variables are not properly sanitized, allowing XSS (cross-site scripting, where malicious code runs in a user's browser) when running an untrusted recipe in Jupyter Notebook. This can lead to client-side RCE (remote code execution, where an attacker can run commands on the user's computer) through insufficient input cleaning.
ONNX (a machine learning model format library) versions 1.15.0 and earlier have an out-of-bounds read vulnerability (accessing memory outside intended boundaries) caused by an off-by-one error in the ONNX_ASSERT and ONNX_ASSERTM functions, which handle string copying. This flaw could allow attackers to read sensitive data from memory.
ONNX (a machine learning model format) versions 1.15.0 and earlier contain a directory traversal vulnerability (a security flaw where an attacker can access files outside the intended directory) in the external_data field of tensor proto (a data structure component). This vulnerability bypasses a previous security patch, allowing attackers to potentially access files they shouldn't be able to reach.
CVE-2023-30767 is a vulnerability in Intel's Optimization for TensorFlow before version 2.13.0 caused by improper buffer restrictions (inadequate checks on how much data can be written to a memory area). An authenticated user with local access to a system could exploit this flaw to gain higher privilege levels than they should have.
CVE-2024-0964 is a vulnerability in Gradio (an AI tool library) where an attacker can remotely read files from a server by sending a specially crafted JSON request. The flaw exists because Gradio doesn't properly limit which files users can access through its API, allowing attackers to bypass directory restrictions and read sensitive files they shouldn't be able to reach.
LlamaIndex (a tool for building AI applications with custom data) versions up to 0.9.34 has a SQL injection vulnerability (a flaw where attackers can insert malicious database commands into normal text input) in its Text-to-SQL feature. This allows attackers to run harmful SQL commands by hiding them in English language requests, such as deleting database tables.
LlamaHub (a library for loading plugins) versions before 0.0.67 have a vulnerability in how they handle OpenAPI and ChatGPT plugin loaders that allows attackers to execute arbitrary code (run any code they choose on a system). The problem is that the code uses unsafe YAML parsing instead of safe_load (a secure function that prevents malicious code in configuration files).
NVIDIA Triton Inference Server for Linux and Windows has a vulnerability (CVE-2023-31036) that occurs when launched with the non-default --model-control explicit option, allowing attackers to use path traversal (exploiting how file paths are handled to access unintended directories) through the model load API. A successful attack could lead to code execution (running unauthorized commands), denial of service (making the system unavailable), privilege escalation (gaining higher access levels), information disclosure (exposing sensitive data), and data tampering (modifying files).
CVE-2023-7215 is a cross-site scripting (XSS) vulnerability, a type of attack where malicious code gets injected into a webpage that a user views in their browser, found in Chanzhaoyu chatgpt-web version 2.11.1. An attacker can exploit this by manipulating the Description argument with malicious image code, and the attack can be performed remotely over the internet. The vulnerability has been publicly disclosed and may already be in use by attackers.
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.
LangChain versions up to 0.1.10 have a path traversal vulnerability (a flaw where an attacker can use ../ sequences to access files outside the intended directory) that allows someone controlling part of a file path to load configurations from anywhere instead of just the intended GitHub repository, potentially exposing API keys or enabling remote code execution (running malicious commands on a system). This bug affects how the load_chain function handles file paths.
Fix: A patch is available in langchain-core version 0.1.29 and later. Update to this version or newer to fix the vulnerability.
NVD/CVE DatabaseFix: Upgrading to version 0.0.27 addresses this issue.
NVD/CVE DatabaseZenML Server in the ZenML machine learning package before version 0.46.7 has a remote privilege escalation vulnerability (CVE-2024-25723), meaning an attacker can gain higher-level access to the system from a distance. The flaw exists in a REST API endpoint (a web-based interface for requests) that activates user accounts, because it only requires a valid username and new password to change account settings, without proper access controls checking who should be allowed to do this.
Fix: Update ZenML to version 0.46.7 or use one of the patched versions: 0.44.4, 0.43.1, or 0.42.2.
NVD/CVE DatabaseFix: Update to LangChain version 0.1.8 or later. A patch is available at https://github.com/langchain-ai/langchain/commit/de9a6cdf163ed00adaf2e559203ed0a9ca2f1de7.
NVD/CVE DatabaseFix: A patch is available at https://github.com/mlflow/mlflow/pull/10893
NVD/CVE DatabaseFix: Update Intel Optimization for TensorFlow to version 2.13.0 or later.
NVD/CVE DatabaseFix: A patch is available at https://github.com/gradio-app/gradio/commit/d76bcaaaf0734aaf49a680f94ea9d4d22a602e70, which addresses the path traversal vulnerability (CWE-22, improper limitation of pathname access).
NVD/CVE DatabaseFix: Upgrade LlamaHub to version 0.0.67 or later, as indicated by the release notes and patch references in the source.
NVD/CVE DatabaseGradio is a Python package for building web demos of machine learning models. Versions before 4.11.0 had a file traversal vulnerability (a weakness that lets attackers read files they shouldn't access) in the `/file` route, allowing attackers to view arbitrary files on machines running publicly accessible Gradio apps if they knew the file paths.
Fix: Update Gradio to version 4.11.0 or later, where this issue has been patched.
NVD/CVE DatabaseFix: Update to Transformers version 4.36 or later. A patch is available at the GitHub commit: https://github.com/huggingface/transformers/commit/1d63b0ec361e7a38f1339385e8a5a855085532ce
NVD/CVE DatabaseFix: Update MLflow to version 2.9.2 or later. A patch is available at the GitHub commit referenced: https://github.com/mlflow/mlflow/commit/1da75dfcecd4d169e34809ade55748384e8af6c1
NVD/CVE DatabaseFix: Update MLflow to version 2.9.2 or later. A patch is available at https://github.com/mlflow/mlflow/commit/1da75dfcecd4d169e34809ade55748384e8af6c1.
NVD/CVE Database