Language Model Security Database

A bimodal adversarial attack, PBI-Attack, can manipulate Large Vision-Language Models (LVLMs) into generating toxic or harmful content by iteratively optimizing both textual and visual inputs in a black-box setting. The attack leverages a surrogate LVLM to inject malicious features from a harmful corpus into a benign image, then iteratively refines both image and text perturbations to maximize the toxicity of the model’s output as measured by a toxicity detection model (Perspective API or…

Multimodal Large Language Models (MLLMs) are vulnerable to a heuristic-induced multimodal risk distribution jailbreak attack. The attack successfully circumvents safety mechanisms by distributing malicious prompts across text and image modalities, preventing detection of harmful intent within either modality alone. An auxiliary LLM generates prompts to guide the target MLLM into reconstructing the malicious prompt and producing the desired harmful output.

Large Language Models (LLMs) across multiple modalities (text, vision, audio) are vulnerable to a "Best-of-N" (BoN) jailbreaking attack. This attack repeatedly submits slightly modified versions of a harmful prompt (e.g., text with altered capitalization, images with modified text style, audio with altered pitch or speed) until a safety mechanism is bypassed and a harmful response is elicited. The effectiveness of the attack scales with the number of attempts (N). While individual…

Large Language Models (LLMs) trained with safety fine-tuning are vulnerable to a novel attack, Response-Guided Question Augmentation (ReG-QA). This attack leverages the asymmetry in safety alignment between question and answer generation. By providing a safety-aligned LLM with toxic answers generated by an unaligned LLM, ReG-QA generates semantically related, yet naturally phrased questions that bypass safety mechanisms and elicit undesirable responses. The attack does not require adversarial…

Large Language Models (LLMs) are vulnerable to multi-step "moralized" jailbreak prompts that bypass their safety guardrails. These prompts, while appearing ethical individually, cumulatively create a context that elicits verbally aggressive and harmful content generation. The attack leverages the LLMs' inability to fully understand the cumulative context and intent across multiple prompts.

Large Language Models (LLMs) exhibit a bias towards authoritative sources, allowing attackers to bypass safety mechanisms by crafting prompts that include fabricated citations mimicking credible sources (e.g., research papers, GitHub repositories). The model's trust in these fabricated citations leads to the generation of harmful content.

A vulnerability exists in several Large Vision-Language Models (LVLMs) where seemingly safe images, when combined with additional safe images and prompts using a specific attack methodology (Safety Snowball Agent), can trigger the generation of unsafe and harmful content. The vulnerability exploits the models' universal reasoning abilities and a "safety snowball effect," where an initial unsafe response leads to progressively more harmful outputs.

Large Language Models (LLMs) are vulnerable to jailbreak attacks using language games, which manipulate input prompts through structured linguistic alterations (e.g., Ubbi Dubbi, custom letter insertion rules) to bypass safety mechanisms. These games obfuscate malicious intent while maintaining human readability, causing LLMs to generate unsafe content.

Large Language Models (LLMs) are vulnerable to "SequentialBreak," a jailbreak attack where embedding a harmful prompt within a chain of benign prompts in a single query can bypass LLM safety features. The LLM's attention mechanism prioritizes the benign prompts, allowing the harmful prompt to be processed without triggering safety mitigations.

Large Language Models (LLMs) are vulnerable to a jailbreak attack that leverages the model's ability to generate diverse and obfuscated prompts to bypass safety constraints. The attack exploits the model's capacity to deviate from prior context, rendering existing safety training ineffective. The attacker uses a multi-stage process involving diversification (generating prompts significantly different from previous attempts) and obfuscation (obscuring sensitive words/phrases) to elicit harmful…