Language Model Security Database

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 black-box identity verification attacks using Targeted Random Adversarial Prompts (TRAP). TRAP leverages adversarial suffixes to elicit a pre-defined response from a target LLM, while other models produce random outputs, enabling identification of the specific LLM used within a third-party application via black-box access. This allows unauthorized identification of the underlying LLM even without access to model weights or internal parameters.

Large Language Models (LLMs) exhibit vulnerability to a novel jailbreak attack, "ArtPrompt," which leverages the models' poor ability to recognize ASCII art representations of words. By replacing sensitive words in a prompt with their ASCII art equivalents, the attacker bypasses safety filters designed to prevent the generation of harmful content.

Large Language Models (LLMs) with advanced reasoning capabilities are vulnerable to jailbreaking attacks using novel, complex, and layered custom encryption schemes. LLMs' ability to decipher these ciphers, exceeding the capabilities of less sophisticated models, enables attackers to bypass existing safety mechanisms by encoding malicious prompts.

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…

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) 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) 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.

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…