Manipulating Energy Consumption and Latency of Multi-modal Large Language Models through Verbose Samples

Core Concepts

High energy consumption and latency time can be induced in multi-modal large language models (MLLMs) by crafting verbose samples, including verbose images and verbose videos, which maximize the length of generated sequences.

Abstract

The paper investigates the vulnerability of MLLMs, particularly image-based and video-based ones, to energy-latency manipulation. It is observed that energy consumption and latency time exhibit an approximately positive linear relationship with the length of generated sequences in MLLMs. To induce high energy-latency cost, the authors propose verbose samples, including verbose images and verbose videos. For both modalities, two modality non-specific losses are designed: 1) Delayed EOS loss to delay the occurrence of the end-of-sequence (EOS) token, and 2) Uncertainty loss to enhance output uncertainty and break the original output dependency. Additionally, modality-specific losses are proposed. For verbose images, a Token Diversity loss is introduced to promote diverse hidden states among all generated tokens. For verbose videos, a Frame Feature Diversity loss is proposed to increase the diversity of frame features, introducing inconsistent semantics. A temporal weight adjustment algorithm is used to balance these losses during optimization. Experiments demonstrate that the proposed verbose samples can significantly extend the length of generated sequences, thereby inducing high energy-latency cost in MLLMs.

Stats

Energy consumption and latency time exhibit an approximately positive linear relationship with the length of generated sequences in MLLMs. Our verbose images can increase the maximum length of generated sequences by 7.87x and 8.56x on MS-COCO and ImageNet datasets, respectively. Our verbose videos can increase the maximum length of generated sequences by 4.04x and 4.14x on MSVD and TGIF datasets, respectively.

Quotes

"Once attackers maliciously induce high energy consumption and latency time (energy-latency cost) during the inference stage, it can exhaust computational resources and reduce the availability of MLLMs." "The energy consumption is the amount of energy used on hardware during an inference, while the latency time represents the response time taken for the inference."

Key Insights Distilled From

Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples

by Kuofeng Gao,... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16557.pdf

Deeper Inquiries

How can the proposed verbose samples be extended to other multi-modal tasks beyond language generation, such as visual question answering or multi-modal reasoning

The proposed verbose samples can be extended to other multi-modal tasks beyond language generation by adapting the loss objectives and optimization techniques to suit the specific requirements of the task. For tasks like visual question answering (VQA) or multi-modal reasoning, the following adaptations can be made: Loss Objectives: For VQA, the loss objectives can focus on generating diverse and informative answers based on the visual input and question. For multi-modal reasoning, the loss objectives can emphasize capturing complex relationships between different modalities and generating coherent reasoning processes. Optimization Techniques: The optimization process can be tailored to prioritize relevant information fusion across modalities for tasks like VQA. Techniques to encourage multi-modal interactions and reasoning can be incorporated to ensure the model effectively integrates information from different sources. Evaluation Metrics: Evaluation metrics can be adjusted to measure the performance of the model in tasks beyond language generation, such as accuracy in VQA or logical reasoning in multi-modal tasks. By customizing the loss objectives, optimization techniques, and evaluation metrics, the verbose samples can be effectively applied to a wide range of multi-modal tasks, enhancing the performance and robustness of MLLMs in various applications.

What countermeasures can be developed to mitigate the energy-latency manipulation threat posed by verbose samples in real-world deployment of MLLMs

To mitigate the energy-latency manipulation threat posed by verbose samples in real-world deployment of MLLMs, the following countermeasures can be developed: Anomaly Detection: Implement anomaly detection algorithms to identify unusual energy consumption and latency patterns during inference, which may indicate malicious manipulation. Regular Monitoring: Regularly monitor energy consumption and latency metrics to detect any sudden spikes or deviations from normal behavior. Dynamic Resource Allocation: Implement dynamic resource allocation strategies to allocate resources based on real-time energy consumption and latency requirements, preventing exhaustion of computational resources. Adversarial Training: Train MLLMs with adversarial examples generated by verbose samples to enhance their robustness against energy-latency manipulation attacks. Security Protocols: Implement strict security protocols to prevent unauthorized access to MLLMs and ensure that only legitimate users can interact with the models. Model Verification: Conduct thorough model verification and validation processes to ensure the integrity and security of MLLMs before deployment. By implementing these countermeasures, organizations can effectively mitigate the risks associated with energy-latency manipulation of MLLMs and ensure the reliability and security of their AI systems.

What are the potential implications of energy-latency manipulation on the broader landscape of AI safety and security, especially as models become larger and more capable

The potential implications of energy-latency manipulation on the broader landscape of AI safety and security are significant, especially as models become larger and more capable. Some implications include: Resource Exhaustion: Energy-latency manipulation can lead to the exhaustion of computational resources, impacting the availability and performance of AI systems. Service Disruption: Malicious actors exploiting energy-latency vulnerabilities can disrupt AI services, leading to downtime and loss of productivity. Security Risks: Manipulation of energy consumption and latency can pose security risks, allowing attackers to compromise the integrity and confidentiality of AI systems. Financial Loss: Increased energy consumption and latency can result in higher operational costs for organizations deploying MLLMs, leading to financial implications. Ethical Concerns: Energy-latency manipulation can raise ethical concerns related to the responsible use of AI technology and the potential misuse of powerful models for malicious purposes. Regulatory Compliance: Organizations may face challenges in complying with regulatory requirements related to energy efficiency and data security in AI systems. Addressing these implications requires a comprehensive approach to AI safety and security, including robust defense mechanisms, continuous monitoring, and adherence to ethical guidelines in AI development and deployment.