Language Model Security Database

Refusal-trained Large Language Models (LLMs) show decreased safety when deployed as browser agents compared to their performance in chatbot settings. Attack methods effective at jailbreaking LLMs in chat contexts also successfully bypass safety mechanisms in browser agents, leading to the execution of harmful behaviors. This vulnerability stems from a lack of generalization of safety training to agentic, real-world interaction scenarios and the increased context available to the agent (browser…

Large Language Models (LLMs) are vulnerable to transferable ensemble black-box jailbreak attacks. The vulnerability allows an attacker to bypass safety mechanisms and elicit undesired or harmful responses from the LLM by using an ensemble of LLM-as-attacker methods that optimize malicious prompts, adaptively adjusting resources based on prompt difficulty, and strategically modifying prompt semantics to evade detection.

Large language models (LLMs) controlling robots are vulnerable to jailbreaking attacks. The ROBOPAIR algorithm demonstrates that malicious prompts can bypass safety mechanisms, causing robots to perform harmful physical actions. This vulnerability exploits the LLM's reliance on textual prompts and its potential lack of sufficient contextual understanding to prevent unsafe commands. The attack is effective across different access levels.

Jailbreaking vulnerabilities in Large Language Models (LLMs) used in Retrieval-Augmented Generation (RAG) systems allow escalation of attacks from entity extraction to full document extraction and enable the propagation of self-replicating malicious prompts ("worms") within interconnected RAG applications. Exploitation leverages prompt injection to force the LLM to return retrieved documents or execute malicious actions specified within the prompt.

Large Language Models (LLMs) used in role-playing systems are vulnerable to character hallucination attacks, a form of jailbreak exploiting "query sparsity" and "role-query conflict". Query sparsity occurs when prompts fall outside the model's training data distribution, causing it to generate out-of-character responses. Role-query conflict arises when the prompt contradicts the established character persona, leading to inconsistent behavior. These vulnerabilities allow attackers to elicit…

The ALERT-Motion framework demonstrates a vulnerability in text-to-motion (T2M) models where an attacker can craft subtly modified text prompts (adversarial prompts) that cause the model to generate motions significantly different from those intended by the benign prompt, yet semantically similar to a target motion specified by the attacker. The attack leverages a large language model (LLM) to autonomously generate these adversarial prompts, bypassing simple keyword-based detection mechanisms…

A vulnerability allows bypassing safety filters in text-to-image (T2I) models using a multi-agent framework ("Atlas") powered by Large Language Models (LLMs). Atlas iteratively generates and refines prompts, leveraging a Vision-Language Model (VLM) to assess filter activation and an LLM to select effective prompts that maintain semantic similarity to the original, malicious prompt while evading the filter. This enables the generation of images containing unsafe content.

LLM-based autonomous agents are vulnerable to malfunction amplification attacks. These attacks exploit the inherent instability of agents by inducing repetitive or irrelevant actions through various methods including prompt injection and adversarial perturbations, leading to agent malfunction and task failure. The attacks do not rely on overtly harmful actions, making them harder to detect with standard LLM safety mechanisms.

Embodied Large Language Models (LLMs) are vulnerable to manipulation via voice-based interactions, leading to the execution of harmful physical actions. Attacks exploit three vulnerabilities: (1) cascading LLM jailbreaks resulting in malicious robotic commands; (2) misalignment between linguistic outputs (verbal refusal) and physical actions (command execution); and (3) conceptual deception, where seemingly benign instructions lead to harmful outcomes due to incomplete world knowledge within…

A vulnerability in Retrieval-Augmented Generation (RAG)-based Large Language Model (LLM) agents allows attackers to inject malicious demonstrations into the agent's memory or knowledge base. By crafting a carefully optimized trigger, an attacker can manipulate the agent's retrieval mechanism to preferentially retrieve these poisoned demonstrations, causing the agent to produce adversarial outputs or take malicious actions even when seemingly benign prompts are used. The attack, termed…