Language Model Security Database

Vision-Language Models (VLMs) are vulnerable to jailbreak attacks using carefully crafted adversarial images. Attackers can bypass safety mechanisms by generating images semantically aligned with harmful prompts, exploiting the fact that minimal cross-entropy loss during adversarial image optimization does not guarantee optimal attack effectiveness. The attack uses a multi-image collaborative approach, selecting images within a specific loss range to enhance the likelihood of successful…

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).

A perception-guided jailbreak (PGJ) attack allows bypassing safety filters in text-to-image models. The attack leverages Large Language Models (LLMs) to identify safe phrases that are perceptually similar to unsafe words but semantically different. This allows the generation of NSFW images using prompts that evade the model's safety mechanisms.

A data poisoning attack, termed ImgTrojan, allows adversaries to bypass safety mechanisms in Vision-Language Models (VLMs) by injecting a small number of maliciously crafted image-caption pairs into the training dataset. These poisoned pairs associate seemingly benign images with jailbreak prompts, causing the VLM to generate unsafe outputs when presented with the poisoned images at inference time. The attack's success rate is notably high even with a very low poison ratio (e.g., one poisoned…

Large Vision-Language Models (VLMs) are vulnerable to jailbreaking attacks via typographically rendered visual prompts. The vulnerability stems from the VLM's ability to process and interpret image-based text, bypassing safety mechanisms designed for text-only prompts. Malicious actors can encode harmful instructions into images, which are then processed by the VLM's visual module and subsequently interpreted by the language model, resulting in the generation of unsafe and policy-violating…

A vulnerability exists in multimodal Large Language Models (LLMs) integrated with external tools. Adversarial images, visually indistinguishable from benign images, can manipulate the LLM to execute unintended tool commands, compromising the confidentiality and integrity of user resources. The attack is effective across diverse prompts, remaining stealthy both in the image itself and in the generated text response.

A vulnerability in multi-modal large language models (LLMs) allows adversaries to bypass safety mechanisms through compositional adversarial attacks. The attack leverages the alignment between vision and language encoders, injecting malicious triggers into benign-looking images. These images, when paired with innocuous prompts, cause the LLM to generate harmful content. The attack requires access only to the vision encoder (e.g., CLIP), not the LLM itself, lowering the barrier to attack.

A vulnerability in vision-integrated Large Language Models (VLMs) allows an attacker to circumvent safety mechanisms through the use of adversarially crafted visual examples. A single, carefully constructed image can universally "jailbreak" the model, causing it to generate harmful content in response to a wide range of subsequent prompts, even those not included in the adversarial example's training data. This vulnerability extends beyond simple misclassification to encompass the execution of…