Language Model Security Database

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.

A vulnerability in multimodal large language models (MLLMs) allows for efficient jailbreaking attacks by leveraging visual input to bypass safety mechanisms. The attack constructs a multimodal model by adding a visual module to the target LLM, then uses a modified PGD algorithm to optimize visual input to generate jailbreaking embeddings. These embeddings are then converted back into text and appended to harmful queries, successfully eliciting objectionable content from the target LLM.

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.

A vulnerability in the voice mode of GPT-4o allows bypassing safety restrictions through a novel "Voice Jailbreak" attack. This attack leverages principles of fictional storytelling (setting, character, plot) to craft audio prompts that persuade the LLM to generate responses violating OpenAI's usage policies, including generating content related to illegal activities, hate speech, physical harm, fraud, pornography, and privacy violations. The attack's success rate is significantly higher than…

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.

Leading safety-aligned Large Language Models (LLMs) are vulnerable to simple adaptive jailbreaking attacks. These attacks utilize manually crafted prompt templates, combined with random search on a suffix to maximize the log-probability of a target token indicating compliance (e.g., "Sure"). The attacks are adaptive, as the prompt template and target token are customized for specific models. Furthermore, some models are vulnerable to transfer attacks (using successful prompts from one LLM on…

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.

Large Language Models (LLMs) are vulnerable to adversarial prompting attacks, where a crafted suffix appended to an instruction causes the LLM to generate unsafe or harmful content. The AdvPrompter technique trains a separate LLM to generate these adversarial suffixes, rapidly bypassing LLM safety mechanisms. The generated suffixes are human-readable and contextually relevant, making them harder to detect than previous methods. The attack is effective against both open-source and closed-source…

Large Language Models (LLMs) are vulnerable to jailbreaking attacks that exploit their tendency to refuse harmful requests. The "Don't Say No" (DSN) attack overcomes this refusal mechanism by optimizing prompts to suppress negative responses, increasing the likelihood of generating harmful content. This is achieved by modifying the loss function during adversarial prompt optimization, prioritizing the suppression of refusal keywords over the elicitation of affirmative responses. The attack…