AI & LLM Vulnerabilities

Daytona, a tool for running AI-generated code safely, had a security flaw before version 0.185.0 where it didn't verify TLS certificates (the security credentials that prove a website is authentic) when cloning Git repositories (copying code from remote servers). This meant an attacker intercepting the connection could steal Git credentials (login information) and replace the real code with fake, harmful code.

Open WebUI, a self-hosted AI platform that runs offline, had a vulnerability before version 0.9.6 where authenticated users could bypass access controls by manipulating a url_idx parameter (a number used to select which backend server to use). This allowed them to reach Ollama backends (the AI model servers) they shouldn't have access to, including internal or admin-disabled ones, because the system only checked if they could use a model but not which backend server they were routed to.

Open WebUI, a self-hosted AI platform that runs offline, had a security flaw in versions before 0.9.6 where access controls (ACL, rules that restrict who can access what) could be bypassed when a database feature called Milvus multitenancy mode was enabled. An attacker could exploit this by using a specially crafted collection name that wasn't properly cleaned before being used in a database query, allowing them to access data they shouldn't be able to reach.

LangChain, a framework for building AI agents and applications powered by large language models, had a vulnerability before version 1.3.9 where several components that work with file paths did not properly restrict access to files. This meant attackers could use glob patterns (wildcards for matching multiple files), symlinks (shortcuts to files), or specially crafted paths to read files outside the intended directory, especially when an AI system processes untrusted input. The vulnerability allowed unauthorized file disclosure.

vLLM versions 0.6.3 through 0.9.0 contain ReDoS (regular expression denial of service, where specially crafted text causes regex patterns to consume excessive CPU time) vulnerabilities in several components including the LoRA utility parser, phi4mini tool parser, and OpenAI chat endpoint. An attacker can send malicious input with nested or repeated structures to trigger severe CPU consumption and make the service unavailable.

A vulnerability in pydantic-settings' `NestedSecretsSettingsSource` (a feature that reads secret values from files in a directory) allows attackers to read files outside the configured secrets directory by creating symbolic links (shortcuts that point to other locations on the system). The same flaw also bypasses `secrets_dir_max_size`, a size limit meant to prevent loading excessively large files. This can happen when `secrets_nested_subdir=True` is enabled and an attacker can add symbolic links to the secrets directory.

The `EnvironmentManager.restore()` function in Network-AI 5.12.1 is vulnerable to path traversal (a technique where an attacker uses sequences like `../` to access files outside the intended directory). An attacker can pass a malicious backup ID to copy arbitrary files from anywhere on the system into the environment's data folder, potentially exposing sensitive information or breaking environment isolation.

Network-AI versions up to 5.12.1 have a vulnerability in the `EnvironmentManager.backup()` function where it follows symlinks (shortcuts to other directories) when collecting files to back up. An attacker who can create a symlink in the environment data directory can trick the backup process into copying files from outside the intended environment root directory, potentially exposing sensitive information in backup artifacts.

Network-AI's AgentRuntime sandbox uses a flawed string-prefix check to keep file access within a configured base directory, but the check is too broad. A sandbox at `/tmp/network-ai-sandbox` also matches the sibling directory `/tmp/network-ai-sandbox_evil`, allowing agents to read or list files outside the intended sandbox boundary. This vulnerability affects Network-AI version 5.12.1 and has a medium severity CVSS score (a 0-10 rating of how severe a vulnerability is).

Langflow's logout button fails to properly clear user sessions, leaving authentication tokens (access_token_lf and refresh_token_lf) in the browser's storage, so the previous user remains logged in until someone else logs in explicitly. This happens because the logout endpoint doesn't delete cookies with the same security settings they were created with, and the frontend doesn't clear stored tokens either. On shared computers, users may incorrectly think they've logged out when they haven't.