Language Model Security Database

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.

Large Language Models (LLMs) are susceptible to automated jailbreak attacks using a fuzzing framework that generates variations of existing jailbreak prompts. This vulnerability allows bypassing built-in safety mechanisms, leading to the generation of harmful or unintended outputs. The vulnerability stems from the LLMs' inability to consistently recognize and reject semantically similar, but subtly different prompt variations generated through automated mutation techniques.

Large Language Models (LLMs) are vulnerable to a "Chain of Utterances" (CoU) based prompt injection attack. This attack exploits the LLM's ability to engage in multi-turn conversations and role-playing, tricking it into providing harmful or unsafe responses even when presented with safety guidelines. The attack leverages a crafted conversation between two agents ("Red-LM," a malicious agent, and "Base-LM," a seemingly helpful agent) to elicit unethical responses from the Base-LM by subtly…

The MASTER KEY framework exploits timing-based characteristics of Large Language Model (LLM) chatbot responses to infer internal defense mechanisms and automatically generate jailbreak prompts. This allows bypassing safety restrictions and eliciting responses violating usage policies, including generation of illegal, harmful, privacy-violating, and adult content. The framework utilizes a three-step process: reverse-engineering defenses via time-based analysis, creating proof-of-concept…

Aligned large language models (LLMs) are vulnerable to a universal and transferable adversarial suffix attack. Appending a specific, automatically generated suffix to a wide range of prompts, even those requesting objectionable content, causes the models to generate harmful or objectionable responses instead of refusing the request. The attack's success rate is significantly higher on GPT-based models.

Large Language Models (LLMs), specifically ChatGPT versions 3.5 and 4.0, are vulnerable to prompt engineering attacks that circumvent built-in content restrictions. Attackers can craft malicious prompts, categorized into "pretending," "attention shifting," and "privilege escalation" techniques, to elicit responses containing prohibited content (e.g., instructions for illegal activities, generation of harmful content). The vulnerability stems from the LLM's inability to reliably distinguish…