Language Model Security Database

A vulnerability in large language models (LLMs) allows attackers to elicit unsafe or unethical responses through a chain of semantically relevant multi-turn prompts. The attack, termed "Chain of Attack" (CoA), exploits the model's contextual understanding and adaptive response capabilities to gradually steer the conversation towards the desired harmful output, even if single-turn prompts are rejected due to safety mechanisms. The attack leverages semantic similarity scoring (e.g., using…

Large Language Models (LLMs) are vulnerable to the "Crescendo" multi-turn jailbreak attack. This attack uses a series of benign, escalating prompts to gradually lead the LLM into generating harmful or disallowed content, bypassing built-in safety mechanisms. The attack leverages the LLM's tendency to follow conversational patterns and build upon previous responses, making it difficult to detect based solely on individual prompts.

Multimodal Large Language Models (MLLMs) in multi-agent environments are vulnerable to "infectious jailbreak," where a single adversarial image injected into the memory of one agent can cause nearly all agents to exhibit harmful behaviors exponentially fast through agent-to-agent interaction. The adversarial image acts as a "virus," spreading via pairwise chats without further attacker intervention.

Large Language Model (LLM)-based agents, due to their multi-agent architecture and role-based interactions, are vulnerable to adversarial attacks that exploit the system's design and agent roles. Maliciously crafted prompts, particularly those targeting system-level roles, can cause agents to generate harmful content, bypassing safety mechanisms more effectively than attacks against individual LLMs. The vulnerability stems from a "domino effect" where one compromised agent can trigger harmful…