Language Model Security Database

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.

The COLD-Attack framework allows for the generation of stealthy and controllable adversarial prompts that can bypass safety mechanisms in various Large Language Models (LLMs). The attack leverages an energy-based constrained decoding method to generate fluent and contextually coherent prompts designed to elicit harmful or unintended responses from the targeted LLM, even under constraints like specific sentiment or phrasing. This allows attacks to evade detection mechanisms solely relying on…

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.

Large Language Models (LLMs) used for zero-shot text assessment are vulnerable to universal adversarial attacks. Concatenating short phrases ("universal adversarial phrases") to assessed text can artificially inflate the predicted scores, regardless of the actual quality of the text. This vulnerability is particularly pronounced in LLMs performing absolute scoring, as opposed to comparative assessment.

Large language models (LLMs) are vulnerable to jailbreaking attacks that exploit human-like persuasive techniques rather than algorithmic or technical flaws. Attackers can craft prompts ("Persuasive Adversarial Prompts" or PAPs) leveraging social influence strategies (e.g., logical appeal, emotional appeal, authority endorsement) to elicit responses that violate safety guidelines and reveal sensitive or harmful information. The effectiveness of these attacks surpasses traditional…