The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative

• URL: http://arxiv.org/abs/2402.14859v2
• Date: Mon, 3 Jun 2024 03:29:07 GMT
• Title: The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative
• Authors: Zhen Tan, Chengshuai Zhao, Raha Moraffah, Yifan Li, Yu Kong, Tianlong Chen, Huan Liu,
• Abstract summary: Multimodal Large Language Models (MLLMs) are constantly defining the new boundary of Artificial General Intelligence (AGI) Our paper explores a novel vulnerability in MLLM societies - the indirect propagation of malicious content.
• Score: 55.08395463562242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to their unprecedented ability to process and respond to various types of data, Multimodal Large Language Models (MLLMs) are constantly defining the new boundary of Artificial General Intelligence (AGI). As these advanced generative models increasingly form collaborative networks for complex tasks, the integrity and security of these systems are crucial. Our paper, ``The Wolf Within'', explores a novel vulnerability in MLLM societies - the indirect propagation of malicious content. Unlike direct harmful output generation for MLLMs, our research demonstrates how a single MLLM agent can be subtly influenced to generate prompts that, in turn, induce other MLLM agents in the society to output malicious content. Our findings reveal that, an MLLM agent, when manipulated to produce specific prompts or instructions, can effectively ``infect'' other agents within a society of MLLMs. This infection leads to the generation and circulation of harmful outputs, such as dangerous instructions or misinformation, across the society. We also show the transferability of these indirectly generated prompts, highlighting their possibility in propagating malice through inter-agent communication. This research provides a critical insight into a new dimension of threat posed by MLLMs, where a single agent can act as a catalyst for widespread malevolent influence. Our work underscores the urgent need for developing robust mechanisms to detect and mitigate such covert manipulations within MLLM societies, ensuring their safe and ethical utilization in societal applications.

Related papers

• When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations [58.27927090394458]
  Large Language Models (LLMs) are vulnerable to backdoor attacks. In this paper, we investigate backdoor functionality through the novel lens of natural language explanations.
  arXiv  Detail & Related papers  (2024-11-19T18:11:36Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems [6.480532634073257]
  We introduce Prompt Infection, a novel attack where malicious prompts self-replicate across interconnected agents. This attack poses severe threats, including data theft, scams, misinformation, and system-wide disruption. To address this, we propose LLM Tagging, a defense mechanism that, when combined with existing safeguards, significantly mitigates infection spread.
  arXiv  Detail & Related papers  (2024-10-09T11:01:29Z)
• CoCA: Regaining Safety-awareness of Multimodal Large Language Models with Constitutional Calibration [90.36429361299807]
  multimodal large language models (MLLMs) have demonstrated remarkable success in engaging in conversations involving visual inputs. The integration of visual modality has introduced a unique vulnerability: the MLLM becomes susceptible to malicious visual inputs. We introduce a technique termed CoCA, which amplifies the safety-awareness of the MLLM by calibrating its output distribution.
  arXiv  Detail & Related papers  (2024-09-17T17:14:41Z)
• Can LLMs be Fooled? Investigating Vulnerabilities in LLMs [4.927763944523323]
  The advent of Large Language Models (LLMs) has garnered significant popularity and wielded immense power across various domains within Natural Language Processing (NLP) This paper will synthesize the findings from each vulnerability section and propose new directions of research and development. By understanding the focal points of current vulnerabilities, we can better anticipate and mitigate future risks.
  arXiv  Detail & Related papers  (2024-07-30T04:08:00Z)
• Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities [28.244283407749265]
  We investigate the security implications of large language models (LLMs) in multi-agent systems. We propose a novel two-stage attack method involving Persuasiveness Injection and Manipulated Knowledge Injection. We demonstrate that our attack method can successfully induce LLM-based agents to spread both counterfactual and toxic knowledge.
  arXiv  Detail & Related papers  (2024-07-10T16:08:46Z)
• Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security [5.077261736366414]
  The pursuit of reliable AI systems like powerful MLLMs has emerged as a pivotal area of contemporary research. In this paper, we endeavor to demostrate the multifaceted risks associated with the incorporation of image modalities into MLLMs.
  arXiv  Detail & Related papers  (2024-04-08T07:54:18Z)
• Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation [98.02846901473697]
  We propose ECSO (Eyes Closed, Safety On), a training-free protecting approach that exploits the inherent safety awareness of MLLMs. ECSO generates safer responses via adaptively transforming unsafe images into texts to activate the intrinsic safety mechanism of pre-aligned LLMs.
  arXiv  Detail & Related papers  (2024-03-14T17:03:04Z)
• Exploring the Adversarial Capabilities of Large Language Models [25.7847594292453]
  Large language models (LLMs) can craft adversarial examples out of benign samples to fool existing safe rails. Our experiments, which focus on hate speech detection, reveal that LLMs succeed in finding adversarial perturbations, effectively undermining hate speech detection systems.
  arXiv  Detail & Related papers  (2024-02-14T12:28:38Z)
• On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
  We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of Open-Domain Question Answering (ODQA) systems.
  arXiv  Detail & Related papers  (2023-05-23T04:10:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.