AI & LLM Vulnerabilities

Cursor, a code editor designed for AI-assisted programming, has a vulnerability in versions 1.7 and below where it automatically loads configuration files from project directories, which can be exploited by attackers. If a user runs Cursor's command-line tool (CLI) in a malicious repository, an attacker could use prompt injection (tricking the AI by hiding instructions in its input) combined with permissive settings to achieve remote code execution (the ability to run commands on the user's system without permission).

Cursor is a code editor that lets programmers work with AI assistance. In versions 1.7 and below, when using MCP (a system for connecting external tools to AI) with OAuth authentication (a login method), an attacker can trick Cursor into running malicious commands by pretending to be a trusted service, potentially giving them full control of the user's computer.

Cursor, a code editor designed for AI-assisted programming, has a critical vulnerability in versions 1.6 and below that allows remote code execution (RCE, where an attacker runs commands on your computer without permission). An attacker who gains control of the AI chat context (such as through a compromised MCP server, a tool that extends the AI's capabilities) can use prompt injection (tricking the AI by hiding malicious instructions in its input) to make Cursor modify workspace configuration files, bypassing an existing security protection and ultimately executing arbitrary code.

Claude Code (an AI tool that writes and runs code automatically) had a security flaw in versions before 1.0.111 where it could execute code from a project before the user confirmed they trusted the project. An attacker could exploit this by tricking a user into opening a malicious project directory.

PyTorch version 2.7.0 has a vulnerability (CVE-2025-55560) that causes a Denial of Service (DoS, where a system becomes unavailable or unresponsive) when a model uses specific sparse tensor functions (torch.Tensor.to_sparse() and torch.Tensor.to_dense()) and is compiled by Inductor (PyTorch's code compilation tool). This issue stems from uncontrolled resource consumption, meaning the system uses up too many computing resources.

CVE-2025-55559 is a vulnerability in TensorFlow v2.18.0 where setting the padding parameter to 'valid' in tf.keras.layers.Conv2D (a layer used in neural networks for image processing) causes a Denial of Service (DoS, where a system becomes unavailable to users). The vulnerability is classified as uncontrolled resource consumption, meaning the system uses up resources like memory or CPU in an uncontrolled way.

CVE-2025-55558 is a buffer overflow (a memory safety error where data is written beyond the intended boundaries) in PyTorch version 2.7.0 that occurs when certain neural network operations are combined and compiled using Inductor, a code compiler. This vulnerability causes a Denial of Service attack (making a service unavailable to users), though no CVSS severity score has been assigned yet.

PyTorch version 2.7.0 has a bug where a name error occurs when a model uses torch.cummin (a function that finds cumulative minimum values) and is compiled by Inductor (PyTorch's compiler for optimizing code). This causes a Denial of Service (DoS, where a system becomes unavailable to users).

CVE-2025-55553 is a syntax error in the proxy_tensor.py file of PyTorch version 2.7.0 that allows attackers to cause a Denial of Service (DoS, a type of attack where a system becomes unavailable to legitimate users). The vulnerability has a CVSS score (a 0-10 rating of how severe a vulnerability is) of 4.0, indicating moderate severity.

PyTorch v2.8.0 has a vulnerability (CVE-2025-55552) where two functions, torch.rot90 (which rotates arrays) and torch.randn_like (which generates random numbers matching a given shape), behave unexpectedly when used together, possibly due to integer overflow or wraparound (where numbers wrap around to negative values instead of staying large).

A vulnerability (CVE-2025-55551) exists in PyTorch version 2.8.0 in a math component called torch.linalg.lu that allows attackers to cause a Denial of Service (DoS, where a system becomes unavailable to users) by performing a slice operation (extracting a portion of data). The issue involves uncontrolled resource consumption (CWE-400, where a program uses too much memory or processing power without limits).