Language Model Security Database

Multimodal Large Language Models (LLMs) processing speech input are vulnerable to adversarial attacks. Imperceptible perturbations added to audio input can cause the model to generate unsafe or harmful text responses, overriding built-in safety mechanisms. The attacks are effective even with limited knowledge of the model's internal workings, demonstrating transferability across different models.

Large Language Models (LLMs) are vulnerable to a novel prompting technique, "conditional Variational-autoencoder-Like Prompt" (VLPrompt), which enables the generation of highly convincing fake news articles. VLPrompt overcomes limitations of previous methods by eliminating the need for additional human-collected data while maintaining contextual coherence and detail. This allows for the automated mass-production of realistic-sounding fake news.

A novel adversarial suffix embedding translation framework (ASETF) enables efficient and highly successful attacks against large language models (LLMs). ASETF optimizes continuous adversarial suffix embeddings, then translates these embeddings into coherent, human-readable text. This bypasses existing defenses which rely on detecting unusual or nonsensical suffixes. The attack achieves a high success rate across multiple LLMs, including both open-source and black-box models.

Large Language Models (LLMs) used for zero-shot text assessment are vulnerable to universal adversarial attacks. Concatenating short phrases ("universal adversarial phrases") to assessed text can artificially inflate the predicted scores, regardless of the actual quality of the text. This vulnerability is particularly pronounced in LLMs performing absolute scoring, as opposed to comparative assessment.

Newly added APIs to large language models (LLMs), such as fine-tuning, function calling, and knowledge retrieval, introduce novel attack vectors that bypass existing safety mechanisms and enable various malicious activities. Specifically, fine-tuning with even a small number of carefully crafted examples can remove or weaken built-in safety guardrails, resulting in the generation of misinformation, disclosure of private information (PII), and the creation of malicious code. Function calling…

Large Language Models (LLMs) such as Llama 2 and Vicuna exhibit a vulnerability where specific layers (e.g., layer 3 in Llama2-13B, layer 1 in Llama2-7B and Vicuna-13B) overfit to harmful prompts, resulting in a disproportionate influence on the model's output for such prompts. This overfitting creates a narrow "safety" mechanism easily bypassed by adversarial prompts designed to avoid triggering these specific layers. Additionally, a single neuron (e.g., neuron 2100 in Llama2 and Vicuna)…

A vulnerability in Text-to-Image (T2I) models' safety filters allows bypassing through the injection of adversarial prompts crafted by an LLM-driven multi-agent system. The attack, named Divide-and-Conquer Attack (DACA), circumvents the filters by rephrasing harmful prompts into multiple benign descriptions of individual visual components, thus avoiding detection while maintaining the original visual intent.

Large Language Models (LLMs) used for code generation are vulnerable to adversarial natural language instructions that preserve semantic meaning but induce the generation of functionally correct code containing specific vulnerabilities. The attack leverages a novel algorithm, DeceptPrompt, to generate adversarial prompts that manipulate the LLM's output, resulting in vulnerable code without altering the intended functionality.

Large Language Model (LLM)-based agents, due to their multi-agent architecture and role-based interactions, are vulnerable to adversarial attacks that exploit the system's design and agent roles. Maliciously crafted prompts, particularly those targeting system-level roles, can cause agents to generate harmful content, bypassing safety mechanisms more effectively than attacks against individual LLMs. The vulnerability stems from a "domino effect" where one compromised agent can trigger harmful…

A vulnerability exists in large language models (LLMs) utilizing in-context learning (ICL). Malicious actors can inject imperceptible adversarial suffixes into in-context demonstrations, causing the LLM to generate targeted, unintended outputs, even when the user query is benign. The attack manipulates the LLM's attention mechanism, diverting it towards the adversarial tokens.