Security vulnerabilities, privacy incidents, safety concerns, and policy updates affecting LLMs and AI agents.
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.
CVE-2025-47995 is a vulnerability in Azure Machine Learning that involves weak authentication (a system that doesn't properly verify user identity), allowing someone who already has some access to gain elevated privileges (higher-level permissions) over a network. The vulnerability has a CVSS 4.0 severity rating, though a full assessment from NIST has not yet been provided.
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.
GPT-SoVITS-WebUI (a tool for converting voices and creating speech from text) has a vulnerability in versions 20250228v3 and earlier where user input for model file paths is passed unsafely to torch.load, a function that reads model files. This unsafe deserialization (loading files without proper security checks) could allow attackers to execute malicious code by providing a specially crafted model file.
GPT-SoVITS-WebUI, a tool for converting voices and generating speech from text, has an unsafe deserialization vulnerability (a flaw where untrusted data is converted back into code objects, potentially allowing attackers to run malicious code) in versions 20250228v3 and earlier. The vulnerability occurs because user-supplied file paths are directly passed to torch.load, a function that can execute arbitrary code during the deserialization process.
DSpace, an open-source application for storing and accessing digital files, has a vulnerability in versions before 7.6.4, 8.2, and 9.1 where it doesn't properly disable XML External Entity (XXE) injection, a technique where attackers embed malicious code in XML files to read sensitive files or steal data from the server). The vulnerability affects both the command-line import tool and the web interface's batch import feature, but only administrators can trigger it by importing archive files.
A ReDoS (regular expression denial of service, where carefully designed text input causes a regex pattern to consume excessive CPU) vulnerability was found in the Hugging Face Transformers library's DonutProcessor class, affecting versions 4.50.3 and earlier. The vulnerable regex pattern can be exploited through crafted input strings to cause the system to slow down or crash, disrupting document processing tasks that use the Donut model.
A WordPress plugin called 'Photos, Files, YouTube, Twitter, Instagram, TikTok, Ecommerce Contest Gallery' has a vulnerability called Stored Cross-Site Scripting (XSS, where an attacker can hide malicious code in a webpage that runs when others view it) in versions up to 26.0.8. Attackers with Author-level permissions or higher can inject harmful scripts through the upload title field because the plugin doesn't properly clean and secure user input.
CVE-2025-7021 is a vulnerability in OpenAI Operator SaaS on Web where an attacker can trick users into entering sensitive information like login credentials by creating a fake fullscreen interface that mimics browser controls and hides security warnings. The attacker overlays distracting elements (such as a fake cookie consent screen) to obscure notifications and deceive users into interacting with the malicious site. This vulnerability has a CVSS score of 6.9 (MEDIUM severity).
Roo Code is an AI tool that can write code automatically. Before version 3.22.6, if a user had auto-approved write permissions, an attacker could send prompts to the agent that would modify VS Code settings files (configuration files that control how the editor works) and run malicious code on the user's computer. For example, an attacker could change a PHP validation setting to point to a harmful command, then create a PHP file to execute it.
Hugging Face Transformers versions up to 4.49.0 have a vulnerability in the `image_utils.py` file where URL validation (checking if a URL starts with certain text) can be tricked through URL username injection (adding fake credentials to a URL). Attackers can create fake URLs that look like they're from YouTube but actually point to malicious sites, risking phishing attacks, malware, or stolen data.
A ReDoS vulnerability (regular expression denial of service, where specially crafted text causes a regex pattern to consume excessive CPU) was found in Hugging Face Transformers library version 4.49.0, specifically in code that filters Python try/except blocks. Attackers could exploit this to crash or slow down systems using the library, potentially disrupting model serving or supply chain processes.
A ReDoS vulnerability (regular expression denial of service, where specially crafted input causes a program to use excessive CPU by making the regex engine work inefficiently) was found in the Hugging Face Transformers library version 4.49.0, specifically in a function that reads configuration files. An attacker could send malicious input to make the application slow down or crash by exhausting its computing resources.
A ReDoS vulnerability (regular expression denial of service, where inefficient pattern matching causes a system to slow down or crash) was found in the Hugging Face Transformers library version 4.49.0. The problem is in a regex pattern called `SETTING_RE` that uses inefficient repetition, causing it to take exponentially longer when processing specially crafted input strings, which can make the application unresponsive or crash.
BerriAI litellm version 1.65.4 contains a SQL injection vulnerability (a type of attack where malicious SQL code is inserted into user inputs to manipulate database queries) in the /key/block endpoint. This weakness allows attackers to potentially access or modify database contents through this vulnerable endpoint.
A vulnerability exists in Anthropic's deprecated Slack MCP Server (Model Context Protocol Server, a tool that lets AI agents interact with Slack) that allows attackers to steal sensitive data. When an AI agent processes untrusted input, an attacker can trick it into creating messages with malicious links that, when Slack's link preview bots automatically expand them, secretly send private data to the attacker's server without requiring any user action.
The @cyanheads/git-mcp-server (an MCP server, or a tool that lets AI systems interact with Git repositories) has a command injection vulnerability (a flaw where attackers can sneak extra system commands into input) in versions before 2.1.5. Because the server doesn't check user input before running system commands, attackers can execute arbitrary code on the server, or trick an AI client into running unwanted actions through indirect prompt injection (hiding malicious instructions in data the AI reads).
Fix: The source explicitly states: 'The fix is included in DSpace 7.6.4, 8.2, and 9.1. Please upgrade to one of these versions.' For organizations unable to upgrade immediately, the source mentions: 'it is possible to manually patch the DSpace backend' and recommends administrators 'carefully inspect any SAF archives (they did not construct themselves) before importing' and 'affected external services can be disabled to mitigate the ability for payloads to be delivered via external service APIs.'
NVD/CVE DatabaseFix: Update the Hugging Face Transformers library to version 4.52.1 or later, as this version contains the fix for the vulnerability.
NVD/CVE DatabaseFix: Update Roo Code to version 3.22.6 or later, where this vulnerability is fixed.
NVD/CVE DatabaseFix: The issue is fixed in version 4.52.1. Update Hugging Face Transformers to version 4.52.1 or later.
NVD/CVE DatabaseFix: Update to version 4.51.0, where the vulnerability is fixed.
NVD/CVE DatabaseFix: Update to version 4.51.0, where the issue is resolved.
NVD/CVE DatabaseFix: Update to version 4.51.0 or later, where the issue is fixed.
NVD/CVE DatabaseFix: Update to version 2.1.5, where this issue has been patched.
NVD/CVE Database