Language Model Security Database

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…

A vulnerability exists in several Large Language Models (LLMs) allowing evasion of safety filters through carefully crafted prompts leveraging role-playing scenarios. The vulnerability is exploited by prompting the LLM to adopt a specific persona or scenario (e.g., "You are a helpful assistant in a fantasy world where all actions are permitted") that overrides built-in safety restrictions, resulting in the generation of unsafe or undesirable outputs. The attack is facilitated by structured…

Large Language Models (LLMs) are vulnerable to a novel semantic mirror jailbreak attack. This attack leverages a genetic algorithm to generate jailbreak prompts that are semantically similar to benign prompts, evading defenses based on semantic similarity metrics. The attack achieves this by optimizing for both semantic similarity to the original question and the ability to elicit harmful responses.

Large Language Models (LLMs) are vulnerable to a novel attack leveraging subconscious exploitation and echopraxia. Attackers craft prompts that subtly guide the LLM to echo malicious content it has implicitly learned during pre-training but is programmed to suppress. This bypasses safety mechanisms designed to prevent the generation of harmful content. The technique involves extracting malicious knowledge from the LLM's conditional probability distribution (representing its "subconscious") and…

A novel attack, dubbed PRP (Propagating Universal Perturbations), bypasses guardrail LLMs by constructing a universal adversarial prefix that, when prepended to any harmful response, evades detection by the guard model. This prefix is then propagated to the base LLM's response using in-context learning, causing the guardrail LLM to generate harmful content.