AI & LLM Vulnerabilities

Langfuse, an open source platform for managing large language models, has a vulnerability in versions 2.95.0–2.95.11 and 3.17.0–3.130.x where attackers could take over user accounts if certain security settings are not configured. The attack works by tricking an authenticated user into clicking a malicious link (via CSRF, which is cross-site request forgery where an attacker tricks your browser into making unwanted requests, or phishing).

vLLM is a tool that runs large language models and serves them to users. In versions 0.5.5 through 0.11.0, two API endpoints accept a parameter called chat_template_kwargs that isn't properly checked before being used, allowing attackers to send specially crafted requests that freeze the server and prevent other users' requests from being processed.

vLLM (an inference and serving engine for large language models) versions 0.5.5 through 0.11.0 have a vulnerability where users can crash the engine by sending multimodal embedding inputs (data that combines multiple types of information, like images and text) with incorrect shape parameters, even if the model doesn't support such inputs. This bug has a CVSS score of 8.3 (a 0-10 scale measuring vulnerability severity), indicating it's a high-severity issue.

The WP Import – Ultimate CSV XML Importer plugin for WordPress has a security flaw in versions up to 7.33 where the showsetting() function is missing an authorization check (a verification that the person accessing it has permission). This allows authenticated attackers with Author-level access or higher to extract sensitive information, including OpenAI API keys (secret credentials used to access the OpenAI service) that are configured through the plugin's admin interface.

CVE-2025-33202 is a stack overflow vulnerability (a memory safety bug where a program writes too much data into a reserved area of memory) in NVIDIA's Triton Inference Server for Linux and Windows. An attacker could exploit this by sending extremely large data payloads, potentially crashing the service and making it unavailable to users (denial of service).

CVE-2025-62453 is a vulnerability in GitHub Copilot and Visual Studio Code where improper validation of generative AI output (not properly checking what the AI generates) allows an authorized attacker to bypass a security feature on their local computer. The vulnerability is classified as a protection mechanism failure (CWE-693, a flaw in how security controls are designed).

A path traversal vulnerability (CWE-22, where an attacker manipulates file paths to access files outside their intended directory) was discovered in Visual Studio Code's CoPilot Chat Extension that allows an authorized attacker to bypass a security feature on their local computer. The vulnerability is tracked as CVE-2025-62449 and was reported by Microsoft Corporation.

Langfuse, an open source platform for managing large language models, had a vulnerability in versions 2.70.0 through 2.95.10 and 3.x through 3.124.0 where the server didn't properly check which organization a user belonged to, allowing any authenticated user to see names and email addresses of members in other organizations if they knew the target organization's ID. The vulnerability required the attacker to have a valid account on the same Langfuse instance and knowledge of the target organization's ID, and no customer data like traces, prompts, or evaluations were exposed.

A WordPress plugin called Tag, Category, and Taxonomy Manager – AI Autotagger with OpenAI has a SQL injection vulnerability (a security flaw where attackers can insert harmful database commands into the plugin's code) in versions up to 3.40.0. Attackers with Editor-level access or higher can exploit the 'post_types' parameter to extract sensitive information from the website's database because the plugin doesn't properly clean up user input before using it in database queries.