Language Model Security Database

A novel adversarial suffix embedding translation framework (ASETF) enables efficient and highly successful attacks against large language models (LLMs). ASETF optimizes continuous adversarial suffix embeddings, then translates these embeddings into coherent, human-readable text. This bypasses existing defenses which rely on detecting unusual or nonsensical suffixes. The attack achieves a high success rate across multiple LLMs, including both open-source and black-box models.

Large Language Models (LLMs) are vulnerable to efficient adversarial attacks using Projected Gradient Descent (PGD) on a continuously relaxed input prompt. This attack bypasses existing alignment methods by crafting adversarial prompts that induce the model to produce undesired or harmful outputs, significantly faster than previous state-of-the-art discrete optimization methods. The effectiveness stems from carefully controlling the error introduced by the continuous relaxation of the discrete…

Large Language Models (LLMs) are vulnerable to an indirect jailbreak attack, termed "Puzzler," which leverages implicit clues instead of explicit malicious intent in prompts. By providing associated behaviors or hints related to a malicious query, Puzzler elicits malicious responses from the LLM, bypassing its safety mechanisms. The attack works by first obtaining "defensive measures" from the LLM against a target malicious action, then querying for the corresponding "offensive measures" that…

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 Models (LLMs) are vulnerable to a multi-round "Contextual Interaction Attack" where a series of benign preliminary questions, crafted to be semantically aligned with a malicious target query, are used to manipulate the LLM's context vector. The autoregressive nature of LLMs causes them to incorporate previous conversation rounds into their generation process, allowing the attacker to prime the model into providing harmful information in response to the final, seemingly benign…

Multimodal Large Language Models (MLLMs) are vulnerable to a jailbreaking attack using crafted images (image Jailbreaking Prompts or imgJPs). These imgJPs, when presented as input alongside malicious prompts, cause the MLLM to bypass safety mechanisms and generate objectionable content, including instructions for harmful activities like identity theft or creation of violent video games. The attack demonstrates both prompt-universality (a single imgJP works across multiple prompts) and, to a…

A vulnerability exists in several Large Language Models (LLMs) allowing attackers to bypass safety and ethical protocols through a novel code injection technique using personalized encryption and decryption functions. The attack leverages the LLMs' code execution capabilities to process encrypted malicious instructions, circumventing the intent security recognition mechanism.

Large Language Models (LLMs) are vulnerable to DrAttack, a jailbreaking technique that decomposes malicious prompts into semantically neutral sub-prompts. The sub-prompts are then implicitly reconstructed by the LLM through in-context learning using benign examples, evading safety mechanisms and eliciting harmful responses. This attack exploits the LLM's ability to piece together fragmented information, even when presented with seemingly innocuous phrases.

Large Language Models (LLMs) utilizing Retrieval Augmented Generation (RAG) are vulnerable to a novel attack vector, termed "RAG Poisoning," where malicious content is injected into the external knowledge base accessed by the LLM via prompt manipulation. This allows attackers to elicit undesirable or malicious outputs from the LLM, bypassing its safety filters. The attack exploits the LLM's reliance on the retrieved information during response generation.

Large Language Models (LLMs) are vulnerable to adversarial prompts generated by the Rainbow Teaming technique. Rainbow Teaming uses a quality-diversity search algorithm to create a diverse set of prompts that elicit unsafe, biased, or incorrect outputs from the target LLM, exceeding a 90% success rate across various models. The vulnerability stems from the LLMs' susceptibility to these carefully crafted prompts, bypassing existing safety mechanisms. These prompts are highly transferable across…