Language Model Security Database

Large Language Models (LLMs) are vulnerable to an attack vector termed "Attack via Implicit Reference" (AIR). AIR bypasses safety mechanisms by decomposing a malicious objective into multiple benign, seemingly unrelated objectives linked through implicit contextual references. The LLM generates harmful content by combining the outputs of these seemingly harmless objectives, without explicitly triggering safety filters designed to detect direct requests for malicious content.

A Chain-of-Jailbreak (CoJ) attack allows bypassing safety mechanisms in image generation models by iteratively editing images based on a sequence of sub-queries. The attack decomposes a malicious query into multiple, seemingly benign sub-queries, each causing the model to generate and modify an image, ultimately producing harmful content. Successful attacks leverage various editing operations (insert, delete, change) on different elements (words, characters, images).

Large Language Models (LLMs) are vulnerable to a novel jailbreak attack that exploits resource limitations. By overloading the model with a computationally intensive preliminary task (e.g., a complex character map lookup and decoding), the attacker prevents the activation of the LLM's safety mechanisms, enabling the generation of unsafe outputs from subsequent prompts. The attack's strength is scalable and adjustable by modifying the complexity of the preliminary task.

Large Language Models (LLMs) exhibit a left-to-right processing bias, making them vulnerable to "FlipAttack." This attack disguises a harmful prompt by flipping (reversing) the order of characters or words, thereby reducing the LLM’s comprehension of the harmful content. A "flipping guidance" module then instructs the LLM to reverse the flipped text, revealing and executing the original harmful prompt.

Large Language Models (LLMs) are vulnerable to a multi-objective black-box jailbreaking attack (BlackDAN) that optimizes prompts to maximize the likelihood of generating unsafe responses while maintaining contextual relevance and minimizing detectability. The attack leverages a multi-objective evolutionary algorithm (NSGA-II) to balance attack success rate, semantic consistency, and stealthiness, resulting in more effective and less easily detectable jailbreaks than single-objective approaches.

A multi-round attack against Large Language Models (LLMs) allows bypassing safety mechanisms by iteratively refining prompts to elicit undesired behavior. The attack leverages the LLM's tendency to adjust its response based on preceding interactions, circumventing single-round prompt filtering defenses.

Large Language Models (LLMs) are vulnerable to a multi-turn jailbreak attack, termed "Jigsaw Puzzles" (JSP), which circumvents existing safeguards by splitting harmful questions into harmless fragments. The LLM is prompted to reconstruct and answer the complete question from these fragments, resulting in the generation of harmful responses. The attack relies on the LLM's ability to piece together seemingly benign input to form a malicious query, exploiting the model's contextual understanding…

Large Language Models (LLMs) are vulnerable to multi-turn adversarial attacks where malicious users obscure harmful intents across multiple queries. The ActorAttack method leverages the LLM's own knowledge base to discover semantically linked "actors" related to a harmful target. By posing seemingly innocuous questions about these actors, the attacker guides the LLM towards revealing harmful information step-by-step, accumulating knowledge until the desired malicious output is obtained, even…

Large Language Models (LLMs) trained with safety mechanisms exhibit biases which disproportionately allow successful "jailbreak" attacks (circumvention of safety protocols to generate harmful content) when targeting prompts related to marginalized groups compared to privileged groups. This vulnerability stems from the unintended correlation between safety alignment techniques and demographic keywords, creating a higher success rate for malicious prompts incorporating keywords associated with…

A vulnerability in safety-aligned Large Language Models (LLMs) allows attackers to bypass safety mechanisms using adversarial prompt translation. The vulnerability stems from the ability to translate garbled adversarial prompts generated by gradient-based attacks into coherent, human-readable prompts that retain their adversarial capability. This allows for the successful transfer of attacks across different LLMs.