AI & LLM Vulnerabilities

iOS Simulator MCP Server (ios-simulator-mcp) versions before 1.3.3 have a command injection vulnerability (a security flaw where attackers insert shell commands into input that gets executed). The vulnerability exists because the `ui_tap` tool uses Node.js's `exec` function unsafely, allowing an attacker to trick an LLM through prompt injection (feeding hidden instructions to an AI to make it behave differently) to pass shell metacharacters like `;` or `&&` in parameters, which can execute unintended commands on the server's computer.

MLflow versions before 3.1.0 have a vulnerability in the gateway_proxy_handler component where it fails to properly validate the gateway_path parameter, potentially allowing SSRF (server-side request forgery, where an attacker tricks the server into making unwanted requests to internal systems). This validation gap could be exploited to access resources the attacker shouldn't be able to reach.

FastGPT, an AI Agent building platform, has a vulnerability in versions before 4.9.12 where the LastRoute parameter on the login page is not properly validated or cleaned of malicious code. This allows attackers to perform open redirect (sending users to attacker-controlled websites) or DOM-based XSS (injecting malicious JavaScript that runs in the user's browser).

Cursor, a code editor designed for AI-assisted programming, had a security flaw in versions before 0.51.0 where JSON files could automatically trigger web requests without user approval. An attacker could exploit this, especially after a prompt injection attack (tricking the AI with hidden instructions in its input), to make the AI agent send data to a malicious website.

FastGPT is an open-source platform for building AI workflows and chatbots that uses a sandbox (an isolated container designed to safely run untrusted code). Versions before 4.9.11 had weak isolation that allowed attackers to escape the sandbox by using overly permissive syscalls (system calls, which are requests programs make to the operating system), letting them read files, modify files, and bypass security restrictions. The vulnerability is fixed in version 4.9.11 by limiting which system calls are allowed to a safer set.

vLLM (a system for running and serving large language models) versions 0.8.0 through 0.9.0 have a vulnerability where the /v1/chat/completions API endpoint doesn't properly check user input in the 'pattern' and 'type' fields when the tools feature is used, allowing a single malformed request to crash the inference worker (the part that actually runs the model) until someone restarts it.

CVE-2025-48943 is a Denial of Service vulnerability (a type of attack that crashes a system) in vLLM versions 0.8.0 through 0.8.x that causes the server to crash when given an invalid regex (a pattern used to match text). This happens specifically when using the structured output feature, which lets the AI format responses in a specific way.

vLLM (an inference and serving engine for large language models) versions 0.8.0 through 0.8.x have a vulnerability where sending an invalid JSON schema as a parameter to the /v1/completions API endpoint causes the server to crash. This happens because the application doesn't properly handle (catch) exceptions that occur when processing malformed input.

vLLM, a software system that runs and serves large language models, has a vulnerability in how it parses tool commands that can be exploited to crash or slow down the service. The problem comes from using an overly complex pattern-matching rule (regular expression with nested quantifiers, optional groups, and inner repetitions) that can cause the system to get stuck processing certain inputs, leading to severe performance problems.

Gradio is an open-source Python package for building machine learning demos and web applications. Before version 5.31.0, a vulnerability in its flagging feature let unauthenticated attackers copy any readable file from the server's filesystem, which could cause DoS (denial of service, where a system becomes unavailable) by copying massive files to fill up disk space, though attackers couldn't actually read the copied files.

vLLM (a system for running large language models) versions 0.7.0 through 0.8.x have a bug in how they create hash values (fingerprints) for images. The hashing method only looks at the raw pixel data and ignores important image properties like width and height, so two different-sized images with the same pixels would create identical hash values. This can cause the system to incorrectly reuse cached results or expose data it shouldn't.