Security vulnerabilities, privacy incidents, safety concerns, and policy updates affecting LLMs and AI agents.
A vulnerability (CVE-2025-5320) was found in Gradio, a web framework for building AI demos, affecting versions up to 5.29.1. An attacker could manipulate the localhost_aliases parameter in the CORS Handler (the component that controls which websites can access the application) to gain elevated privileges, though executing this attack is difficult and requires remote access.
CVE-2025-5277 is a command injection vulnerability (a flaw where an attacker can trick a program into running unwanted commands) in aws-mcp-server, an MCP server (a software tool that helps AI systems interact with AWS cloud services). An attacker can craft a malicious prompt that, when accessed by an MCP client (a program that connects to the server), executes arbitrary commands on the host system, with a critical severity rating of 9.4.
vLLM versions 0.6.5 through 0.8.4 have a vulnerability when using `PyNcclPipe` (a tool for peer-to-peer communication between multiple computers running the AI model) with the V0 engine. The issue is that a network communication interface called `TCPStore` was listening on all network connections instead of just the private network specified by the `--kv-ip` parameter, potentially exposing the system to unauthorized access.
Langroid, a Python framework for building AI applications, has a vulnerability in versions before 0.53.15 where the `LanceDocChatAgent` component uses pandas eval() (a function that executes Python code stored in strings) in an unsafe way, allowing attackers to run malicious commands on the host system. The vulnerability exists in the `compute_from_docs()` function, which processes user queries without proper protection.
Langroid, a Python framework for building LLM-powered applications, had a code injection vulnerability (CWE-94, a flaw where untrusted input can be executed as code) in its `TableChatAgent` component before version 0.53.15 because it used `pandas eval()` without proper safeguards. This could allow attackers to run arbitrary code if the application accepted untrusted user input.
ChatGPT through March 30, 2025, renders SVG documents (scalable vector graphics, a type of image format) directly in web browsers instead of displaying them as plain text, which allows attackers to inject HTML (the code that structures web pages) and potentially trick users through phishing attacks.
A vulnerability in the `preprocess_string()` function of the huggingface/transformers library (version v4.48.3) allows a ReDoS attack (regular expression denial of service, where a poorly written pattern causes the computer to do exponential amounts of work). An attacker can send specially crafted input with many newline characters that makes the function use excessive CPU, potentially crashing the application.
CVE-2025-1975 is a vulnerability in Ollama server version 0.5.11 that allows an attacker to crash the server through a Denial of Service attack by sending specially crafted requests to the /api/pull endpoint (the function that downloads AI models). The vulnerability stems from improper validation of array index access (CWE-129, which means the program doesn't properly check if it's trying to access memory locations that don't exist), which happens when a malicious user customizes manifest content and spoofs a service.
CVE-2025-4701 is a vulnerability in VITA-MLLM Freeze-Omni (versions up to 20250421) where improper input validation in the torch.load function of models/utils.py allows deserialization (converting data back into executable code) of untrusted data through a manipulated file path argument. This vulnerability has a CVSS score (a 0-10 rating of how severe a vulnerability is) of 4.8 (medium severity) and can be exploited locally by users with basic privileges.
CVE-2025-0649 is a bug in Google's TensorFlow Serving (a tool that runs machine learning models as a service) versions up to 2.18.0 where incorrect handling of JSON input can cause unbounded recursion (a program calling itself repeatedly without stopping), leading to server crashes. This vulnerability has a CVSS score (a 0-10 rating of how severe a vulnerability is) of 8.9, indicating high severity. The issue relates to out-of-bounds writes (writing data to unintended memory locations) and stack-based buffer overflow (overflowing a memory region meant for temporary data).
CVE-2025-30165 is a vulnerability in vLLM (a system for running large language models) that affects multi-node deployments using the V0 engine. The vulnerability exists because vLLM deserializes (converts from storage format back into usable data) incoming network messages using pickle, an unsafe method that allows attackers to execute arbitrary code on secondary hosts. This could let an attacker compromise an entire vLLM deployment if they control the primary host or use network-level attacks like ARP cache poisoning (redirecting network traffic to a malicious server).
CVE-2025-25014 is a prototype pollution vulnerability (a type of bug where an attacker modifies the basic template that objects are built from) in Kibana that allows attackers to execute arbitrary code (run commands they shouldn't be able to run) by sending specially crafted HTTP requests (malicious web requests) to machine learning and reporting endpoints. The vulnerability affects multiple versions of Kibana and was identified by Elastic.
The Terraform WinDNS Provider (a tool for managing Windows DNS servers through Terraform, an infrastructure automation tool) had a security flaw before version 1.0.5 where the `windns_record` resource didn't properly validate user input, allowing authenticated command injection (an attack where malicious commands are sneaked into legitimate input to execute unauthorized code in the underlying PowerShell command prompt). This vulnerability only affects users who already have authentication access to the system.
A vulnerability (CVE-2025-4287) was found in PyTorch 2.6.0+cu124 in a function that handles GPU communication, which can be exploited to cause a denial of service (making a system or service stop working) by someone with local access to the computer. The vulnerability has been publicly disclosed and rated as medium severity.
Retrieval-based-Voice-Conversion-WebUI (a framework for changing voices using AI) in version 2.2.231006 and earlier has a critical vulnerability where user input is passed unsafely to a function that loads model files using torch.load (a Python tool that can execute code from files). An attacker could exploit this by providing a malicious model file path, leading to RCE (remote code execution, where an attacker can run commands on the system).
Retrieval-based-Voice-Conversion-WebUI, a voice changing framework, has a vulnerability in versions 2.2.231006 and earlier where user input (like a file path) is passed directly to torch.load (a function that reads model files). This unsafe deserialization (loading untrusted data that could contain malicious code) allows attackers to execute arbitrary commands on the system running the software.
Retrieval-based-Voice-Conversion-WebUI is a voice changing tool that has a security flaw in versions 2.2.231006 and earlier. The vulnerability allows unsafe deserialization (loading untrusted data that could contain malicious code) when the program takes user input for a model file path and loads it using torch.load, which could let attackers run arbitrary code on the system.
Retrieval-based-Voice-Conversion-WebUI, a voice changing tool, has a vulnerability in versions 2.2.231006 and earlier where unsafe deserialization (loading data in a way that can execute malicious code) allows attackers to run code remotely. The problem occurs because the software takes user input for model file paths and loads them using torch.load without proper safety checks, enabling RCE (remote code execution, where attackers can run commands on the affected system).
Retrieval-based-Voice-Conversion-WebUI, a voice-changing tool, has a vulnerability in versions 2.2.231006 and earlier where user input for model file paths is passed unsafely to torch.load (a function that loads saved AI models). This unsafe deserialization (loading data from untrusted sources without checking it first) can allow attackers to run arbitrary code on the system.
Retrieval-based-Voice-Conversion-WebUI, a voice-changing framework, has a critical vulnerability in versions 2.2.231006 and earlier where unsafe deserialization (loading data from untrusted sources without checking it first) can occur. An attacker can exploit this by providing a malicious file path that gets loaded using torch.load, which can lead to RCE (remote code execution, where an attacker runs commands on a system they don't own).
Fix: Update to vLLM version 0.8.5 or later. According to the source: "As of version 0.8.5, vLLM limits the `TCPStore` socket to the private interface as configured."
NVD/CVE DatabaseFix: Upgrade to Langroid version 0.53.15 or later. The fix involves input sanitization (cleaning and filtering user input) to the affected function by default to block common attack vectors, along with added warnings in the project documentation about the risky behavior.
NVD/CVE DatabaseFix: Upgrade to Langroid version 0.53.15 or later. According to the source, 'Langroid 0.53.15 sanitizes input to `TableChatAgent` by default to tackle the most common attack vectors, and added several warnings about the risky behavior in the project documentation.'
NVD/CVE DatabaseFix: A patch is available at https://github.com/tensorflow/serving/commit/6cb013167d13f2ed3930aabb86dbc2c8c53f5adf (identified by Google Inc. as the official patch for this vulnerability).
NVD/CVE DatabaseFix: The maintainers recommend that users ensure their environment is on a secure network. Additionally, the V0 engine has been off by default since v0.8.0, and the V1 engine is not affected by this issue.
NVD/CVE DatabaseFix: A security update is available from Elastic for Kibana versions 8.17.6, 8.18.1, or 9.0.1, as referenced in the Elastic vendor advisory at https://discuss.elastic.co/t/kibana-8-17-6-8-18-1-or-9-0-1-security-update-esa-2025-07/377868.
NVD/CVE DatabaseFix: Update to version 1.0.5, which contains a fix for the issue.
NVD/CVE DatabaseFix: Apply the patch identified as commit 5827d2061dcb4acd05ac5f8e65d8693a481ba0f5, which is recommended to fix this issue.
NVD/CVE Database