Language Model Security Database

Large Language Models (LLMs) are vulnerable to jailbreak attacks by crafted prompts that bypass safety mechanisms, causing the model to generate harmful or unethical content. This vulnerability stems from the inherent tension between the LLM's instruction-following and safety constraints. The JBFuzz technique demonstrates the ability to efficiently and effectively discover such prompts through a fuzzing-based approach leveraging novel seed prompt templates and a synonym-based mutation strategy.

Large Language Models (LLMs) are vulnerable to an adversarial encoding attack where English instructions are obfuscated using valid but visually nonsensical UTF-8 byte sequences. By manipulating multi-byte UTF-8 encoding schemes—specifically by fixing the last 8 bits of a code point to match a target ASCII character and rotating the remaining bits—attackers can generate sequences (e.g., Byzantine musical symbols) that appear incomprehensible to humans and standard text filters but are…

Large Language Model (LLM) safety judges exhibit vulnerability to adversarial attacks and stylistic prompt modifications, leading to increased false negative rates (FNR) and decreased accuracy in classifying harmful model outputs. Minor stylistic changes to model outputs, such as altering the formatting or tone, can significantly impact a judge's classification, while direct adversarial modifications to the generated text can fool judges into misclassifying even 100% of harmful generations as…

A vulnerability in safeguarded text-to-image models allows bypassing of safety filters and alignment methods through the use of adversarial prompts generated by a fine-tuned large language model (LLM). The attack, termed PromptTune, effectively rewrites unsafe prompts into semantically similar adversarial prompts that evade safety mechanisms, resulting in the generation of harmful images. The attack does not require repeated queries to the target text-to-image model.

A vulnerability exists in large language models (LLMs) where the model's internal representations (activations) in specific latent subspaces can be manipulated to trigger jailbreak responses. By calculating a perturbation vector based on the difference between the mean activations of "safe" and "jailbroken" states, an attacker can introduce a targeted perturbation to the model's activations, causing it to generate unsafe outputs even when presented with a safe prompt. This manipulates the…

Predictive Large Language Model (LLM) routers, specifically those utilizing Deep Neural Network (DNN) and Matrix Factorization (MF) architectures, are vulnerable to adversarial manipulation and backdoor poisoning. These routers are designed to optimize cost and latency by dynamically directing simple queries to "weak" (cheap) models and complex queries to "strong" (expensive) models. Attackers can exploit this mechanism in two ways: 1. Inference-time Attacks: By appending specific adversarial…

A vulnerability exists in Retrieval-Augmented Generation (RAG) systems that allows for black-box adversarial attacks known as "CtrlRAG." This flaw allows an attacker to manipulate the generation of Large Language Models (LLMs) by injecting maliciously crafted inputs into the system's knowledge base. Unlike traditional injection attacks that rely on direct concatenation, CtrlRAG utilizes a Masked Language Model (MLM) to iteratively replace words in the malicious text. This optimization ensures…

Large Language Models (LLMs) are vulnerable to a novel jailbreaking attack leveraging adversarial metaphors. The attack, termed AVATAR, induces the LLM to reason about benign metaphors related to harmful tasks, ultimately leading to the generation of harmful content either directly or through calibration of metaphorical and professional harmful content. The attack exploits the LLM's cognitive mapping process, bypassing standard safety mechanisms.

A vulnerability in text-to-image (T2I) models allows bypassing safety filters through the use of metaphor-based adversarial prompts. These prompts, crafted using LLMs, indirectly convey sensitive content, exploiting the model's ability to infer meaning from figurative language while circumventing explicit keyword filters and model editing strategies.

Multimodal Large Language Models (MLLMs) are vulnerable to a novel attack vector leveraging narrative-driven visual storytelling and role immersion to circumvent built-in safety mechanisms. The attack, termed MIRAGE, decomposes harmful queries into environment, character, and activity triplets, generating a sequence of images and text prompts that guide the MLLM through a deceptive narrative, ultimately eliciting harmful responses. The attack successfully exploits the MLLM's cross-modal…