Language Model Security Database

A Denial-of-Service (DoS) vulnerability exists in Large Language Model (LLM) inference services where specially crafted input prompts can trigger excessively long or infinite generation loops ("infinite thinking"). This vulnerability, identified as "ThinkTrap," utilizes derivative-free optimization (CMA-ES) within a continuous surrogate embedding space to circumvent the discrete nature of token inputs. By optimizing a low-dimensional latent vector and projecting it to token sequences, an…

Multi-tenant Large Language Model (LLM) inference systems utilizing global Key-Value (KV) cache sharing are vulnerable to a timing side-channel attack. By measuring the Time-To-First-Token (TTFT) latency of crafted API requests, an unprivileged remote attacker can determine if specific token sequences have been previously processed and cached by the system for other users. This observable timing difference between cache hits (low TTFT) and cache misses (high TTFT) allows for the token-by-token…

Large Language Models (LLMs) equipped with native code interpreters are vulnerable to Denial of Service (DoS) via resource exhaustion. An attacker can craft a single prompt that causes the interpreter to execute code that depletes CPU, memory, or disk resources. The vulnerability is particularly pronounced when a resource-intensive task is framed within a plausibly benign or socially-engineered context ("indirect prompts"), which significantly lowers the model's likelihood of refusal compared…

Predictive Large Language Model (LLM) routers, specifically those utilizing Deep Neural Network (DNN) and Matrix Factorization (MF) architectures, are vulnerable to adversarial manipulation and backdoor poisoning. These routers are designed to optimize cost and latency by dynamically directing simple queries to "weak" (cheap) models and complex queries to "strong" (expensive) models. Attackers can exploit this mechanism in two ways: 1. Inference-time Attacks: By appending specific adversarial…

An untrusted reinforcement-learning-from-human-feedback (RLHF) platform can selectively manipulate preference samples associated with an attacker's target domain. The corrupted preference data trains a compromised reward model and then steers the fine-tuned language model toward undesirable behavior, creating a model-supply-chain risk before deployment.

A vulnerability exists in Large Language Model (LLM) routing systems (control planes) that allows for the manipulation of inference flow via adversarial input sequences. LLM routers, which dynamically direct user queries to either "weak" (cheaper) or "strong" (expensive) models based on predicted query complexity, can be bypassed by appending specific, pre-optimized token sequences known as "confounder gadgets." These gadgets artificially inflate the router's complexity score for an input…