Language Model Security Database

Commercial LLM-powered agents utilizing autonomous web access, memory modules, and retrieval-augmented generation (RAG) are vulnerable to indirect prompt injection and environmental manipulation. Attackers can embed malicious instructions into external data sources trusted by the agent (such as Reddit posts, public databases, or ArXiv papers). When the agent autonomously retrieves and processes this content during task execution, it executes the embedded malicious commands. This vulnerability…

The Virus attack method enables attackers to bypass guardrail moderation on fine-tuning data, leading to a significant degradation of safety alignment in large language models (LLMs). This is achieved through a dual-objective data optimization strategy that crafts harmful data undetectable by the guardrail while maximizing their effectiveness in compromising the victim model's safety.

A vulnerability in Retrieval-Augmented Generation (RAG)-based Large Language Model (LLM) agents allows attackers to inject malicious demonstrations into the agent's memory or knowledge base. By crafting a carefully optimized trigger, an attacker can manipulate the agent's retrieval mechanism to preferentially retrieve these poisoned demonstrations, causing the agent to produce adversarial outputs or take malicious actions even when seemingly benign prompts are used. The attack, termed…