AI & LLM Vulnerabilities

LiteLLM is a proxy server (an intermediary that forwards requests to AI language model APIs) that had a security flaw before version 1.84.0 where attackers could fake an Authorization header to bypass authentication checks and access AI tools without a valid LiteLLM key. The vulnerability affected the MCP Streamable HTTP endpoint (a network interface for handling streaming data) and allowed unauthenticated requests to reach protected systems.

LiteLLM is a proxy server (a middleman that forwards requests to different AI services) that had a vulnerability before version 1.83.7-stable where it didn't properly check file paths when extracting uploaded skill files (packaged as ZIP archives). This meant an authenticated user could upload a specially crafted file that uses path traversal (a technique to write files outside the intended folder) to place files in dangerous locations on the server.

Gradio before version 6.20.0 has a vulnerability where the /gradio_api/file= endpoint accepts unvalidated URLs in the file_fetch() function, allowing attackers to perform an open redirect (sending users to malicious websites) or SSRF (server-side request forgery, where the server makes unintended requests to internal systems). Attackers can exploit this to target cloud metadata services and steal sensitive credentials like EC2 IAM role credentials (authentication tokens used by cloud services).

A security flaw in @better-auth/oauth-provider allows two token requests sent at the same time to both redeem a single authorization code (a temporary token that should only work once), bypassing OAuth security rules. The vulnerability affects versions 1.6.0 through 1.6.10, and similar issues exist in the legacy plugins from better-auth versions 1.4.8-beta.7 through 1.6.0.

vLLM, a system that runs large language models efficiently, has a vulnerability in versions before 0.24.0 where users can submit malicious regular expressions (patterns used to match text) through the structured_outputs.regex parameter that cause the system to hang indefinitely, making it unavailable to other users. The vulnerability exists because the regex patterns are sent directly to backend compilers without checking if they will take too long to process, and patterns with nested quantifiers (like repeated matching operations inside each other) can cause exponential state-space expansion (the number of possible states the regex checker must evaluate grows exponentially). An attacker could exploit this to perform a denial-of-service attack (making a service unavailable to legitimate users).

vLLM is a library for running and serving large language models (LLMs, AI systems that generate text). Versions 0.12.0 through 0.23.x have a bug where sending certain specially crafted requests to the /v1/completions endpoint (an API route for generating text) with models using M-RoPE (a rotary position embedding technique) causes the server to crash and stop working entirely, and any authorized user can trigger this crash.

vLLM (a system for running LLMs efficiently) versions before 0.24.0 have a bug where certain requests can cause the rejection sampler (a component that filters generated tokens) to produce an invalid token value that crashes the engine's GPU worker. Because these requests can be sent remotely through public endpoints, an attacker can trigger this crash to shut down the service for all users until the worker restarts, creating a denial of service attack (making a service unavailable to legitimate users).

Coder's AI Bridge Proxy had a security flaw where it disabled TLS certificate verification (the process that confirms a server's identity over encrypted connections) in its default configuration, meaning it would accept any certificate from the Coder server. An attacker positioned between the proxy and server could intercept sensitive data like session tokens and API keys. This only affects systems where the proxy and server are on separate machines; co-located systems using loopback connections are unaffected.