Language Model Security Database

A vulnerability in several open-source Large Language Models (LLMs) allows for efficient jailbreaking via Adaptive Dense-to-Sparse Constrained Optimization (ADC). This attack uses a continuous optimization method, progressively increasing sparsity to generate adversarial token sequences that bypass safety measures and elicit harmful responses. The attack is more effective and efficient than prior token-level methods.

Multimodal Large Language Models (LLMs) processing speech input are vulnerable to adversarial attacks. Imperceptible perturbations added to audio input can cause the model to generate unsafe or harmful text responses, overriding built-in safety mechanisms. The attacks are effective even with limited knowledge of the model's internal workings, demonstrating transferability across different models.

A vulnerability allows attackers to bypass Large Language Model (LLM) moderation guardrails by using specially crafted prompts containing "cipher characters." These characters, strategically placed within the prompt's output, alter the LLM's response to reduce its "harm" score, enabling the generation of content that would otherwise be blocked. The attack leverages a jailbreak prefix combined with a malicious question and cipher characters to bypass both input and output level filters. This…

Large Language Models (LLMs) exhibit vulnerabilities when processing complex or ambiguous prompts containing malicious intent. The vulnerability arises from the LLMs' inability to consistently detect maliciousness when prompts are obfuscated by either splitting a single malicious query into multiple parts or by directly modifying the malicious content to increase ambiguity. This allows attackers to bypass built-in safety mechanisms and elicit harmful or restricted content.

Medical Multimodal Large Language Models (MedMLLMs) are vulnerable to cross-modality attacks. Attackers can craft "mismatched malicious attacks" (2M-attacks) by providing MedMLLMs with image-text pairs where the image modality and/or anatomical region do not match the textual query, causing the model to generate incorrect or harmful responses. These attacks can be further optimized ("optimized mismatched malicious attacks"—O2M-attacks) using multimodal cross-optimization (MCM) techniques to…

A vulnerability in large language models (LLMs) allows for near-perfect jailbreaking via iterative prompt refinement and self-explanation. The attacker uses the LLM itself to iteratively refine adversarial prompts by requesting self-explanations of failed attempts, ultimately generating prompts that bypass safety mechanisms and elicit harmful content. A subsequent "Rate+Enhance" step further maximizes the harmfulness of the generated output.

Large language models (LLMs) are vulnerable to enhanced jailbreak attacks by appending multiple end-of-sentence (EOS) tokens to malicious prompts. This bypasses internal safety mechanisms, causing the LLM to respond to harmful queries that it would otherwise reject. The EOS tokens subtly shift the LLM’s internal representation of the prompt, making it appear less harmful without significantly altering the semantic meaning of the malicious content.

Multimodal Large Language Models (MLLMs) are vulnerable to a universal jailbreak attack, termed Visual Role-Play (VRP), which leverages role-playing image characters to elicit harmful responses. VRP generates images depicting high-risk characters (e.g., cybercriminals) described by an LLM, paired with a benign role-play instruction and a malicious query. This combined input tricks the MLLM into generating malicious content by enacting the character's persona.

Large Language Models (LLMs) are vulnerable to a novel jailbreaking attack, "WordGame," which leverages simultaneous query and response obfuscation to bypass safety mechanisms. The attack replaces malicious words with word games in the query, forcing the LLM to reason through the game before addressing the original malicious intent. This, coupled with auxiliary tasks or questions (WordGame+), creates a context absent in the LLM's safety training data, enabling the generation of harmful content.

Large language models (LLMs) are vulnerable to jailbreaking attacks using adversarially generated suffixes. The AmpleGCG attack generates a large number of diverse, effective suffixes which bypass safety mechanisms in both open and closed-source LLMs. The attack leverages the observation that low loss during suffix generation is not a reliable indicator of jailbreaking success, and generates diverse suffixes from intermediate steps of the optimization process.