Language Model Security Database

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.

A Trojan Activation Attack (TA²) against Large Language Models (LLMs) allows injection of "trojan steering vectors" into activation layers during inference. These vectors, generated by comparing activations from a target LLM and a "teacher" (misaligned) LLM, steer the model's output towards attacker-defined misaligned behaviors (e.g., generating toxic content, biased responses, or helpful instructions for harmful activities). The attack does not require retraining or modifying model weights.

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.

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…

Multimodal Large Language Models (MLLMs) are vulnerable to a novel attack vector where query-relevant images, generated using techniques like Stable Diffusion and typography, bypass safety mechanisms and elicit unsafe responses even when the underlying LLM is safety-aligned. The attack exploits the vision-language alignment module's susceptibility to image prompts directly related to malicious text queries.

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…

AutoDAN is an interpretable gradient-based adversarial attack that generates readable prompts to bypass perplexity filters and jailbreak LLMs. The attack crafts prompts that elicit harmful behaviors while maintaining sufficient readability to avoid detection by existing perplexity-based defenses. This is achieved through a left-to-right token-by-token generation process optimizing for both jailbreaking success and prompt readability.

Large Language Models (LLMs) are vulnerable to Compositional Instruction Attacks (CIA), where malicious prompts are embedded within seemingly harmless instructions. This allows attackers to bypass safety mechanisms and elicit harmful responses from the model, even if the individual components of the prompt would be flagged as safe. The attack exploits the model's inability to correctly identify underlying malicious intent within composite instructions.

Large Language Models (LLMs) are vulnerable to In-Context Attacks (ICA) and susceptible to mitigation via In-Context Defense (ICD). ICA leverages a small number of harmful demonstration examples within a prompt to elicit harmful responses from the LLM, even if it is otherwise safety-aligned. ICD counteracts ICA by prepending safe demonstration examples to the prompt, effectively reducing the likelihood of harmful output. The effectiveness of both ICA and ICD is demonstrated across multiple LLMs.

A vulnerability in large language models (LLMs) allows attackers to craft malicious prompts that induce the LLM to generate harmful content, such as fraudulent material, racist remarks, or instructions for illegal activities. The vulnerability arises from the LLM's inability to reliably distinguish between benign and malicious instructions disguised within seemingly innocuous prompts. Attackers can exploit this by leveraging techniques like obfuscation, code injection/payload splitting, and…