Protecting Large Language Model Intellectual Property Against Model Merging Attacks

Belangrijkste concepten

Model merging techniques can effectively undermine watermark-based IP protection for large language models, but model fingerprinting remains robust against such attacks.

Samenvatting

The paper investigates the robustness of two state-of-the-art IP protection techniques, Quantization Watermarking and Instructional Fingerprint, against various model merging algorithms such as Task Arithmetic, TIES-MERGING, and DARE.

Key highlights:

• Experimental results show that current LLM watermarking techniques cannot survive in merged models, while model fingerprinting techniques can.
• Attackers can successfully generate high-quality merged models that possess multiple capabilities by combining different expert models, but the watermark information is lost in the process.
• In contrast, the fingerprint information remains intact in the merged models, even when their performance matches or exceeds the baseline.
• The authors advocate for including model merging as a necessary consideration in assessing the robustness of LLM IP protection methods to promote the healthy development of the open-source LLM community.

Statistieken

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Janet eats 3 eggs for breakfast every day.
Janet bakes muffins with 4 eggs every day.
The price of a fresh duck egg is $2.

Citaten

"Model merging is a promising lightweight model empowerment technique that does not rely on expensive computing devices (e.g., GPUs) or require the collection of specific training data."
"Uncertified model merging can infringe upon the Intellectual Property (IP) rights of the original upstream models."
"Experimental results indicate that current Large Language Model (LLM) watermarking techniques cannot survive in the merged models, whereas model fingerprinting techniques can."

Belangrijkste Inzichten Gedestilleerd Uit

Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging

by Tianshuo Con... om arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05188.pdf

Diepere vragen

How can model merging techniques be leveraged to expand the capabilities of LLMs in a way that respects the IP rights of the original models?

Model merging techniques can be utilized to enhance the capabilities of Large Language Models (LLMs) while still upholding the Intellectual Property (IP) rights of the original models through several strategies:

Collaborative Merging: Instead of merging models without consent, model developers can collaborate and agree to merge their models to create a more powerful and versatile LLM. This collaboration ensures that all parties involved are aware of the merging process and agree to it, respecting each other's IP rights.

Legal Agreements: Before merging models, developers can establish legal agreements that outline the terms of the merging process, including how the IP rights of each model will be protected and acknowledged in the merged model. This ensures that all parties have legal protection for their contributions.

Transparent Attribution: Implementing a system where the contributions of each model to the merged LLM are transparently attributed can help in respecting IP rights. By clearly identifying the source of different capabilities within the merged model, the original model owners' rights can be acknowledged.

IP Protection Mechanisms: Incorporating robust IP protection mechanisms, such as watermarking and fingerprinting, directly into the merging process can help safeguard the original models' IP rights. These techniques can help track and identify the origin of specific components within the merged model.

By adopting these approaches, model merging can be leveraged to enhance LLM capabilities while ensuring that the IP rights of the original models are respected and protected.

What are the potential legal and ethical implications of model merging attacks that infringe on IP rights, and how can these be addressed?

Model merging attacks that infringe on IP rights can have significant legal and ethical implications, including:

IP Violations: Unauthorized model merging can lead to intellectual property violations, where the original model owners' rights are infringed upon. This can result in legal disputes and challenges regarding ownership and usage rights of the merged model.

Ethical Concerns: From an ethical standpoint, unauthorized model merging attacks raise questions about fairness, transparency, and accountability. It can lead to misrepresentation of the original model owners' work and undermine trust within the AI community.

Commercial Exploitation: Infringing on IP rights through model merging can enable attackers to exploit the commercial value of the merged model without proper attribution or compensation to the original creators. This can harm the financial interests of the rightful owners.

To address these implications, several measures can be taken:

Legal Action: Model owners can pursue legal action against perpetrators of IP violations through copyright infringement claims or other legal avenues to protect their rights.

Enhanced Security Measures: Implementing advanced security measures, such as encryption, access controls, and monitoring systems, can help prevent unauthorized access and manipulation of models.

Ethical Guidelines: Establishing clear ethical guidelines and standards for model merging practices can promote responsible behavior and discourage unethical actions in the AI community.

Education and Awareness: Increasing awareness about the importance of respecting IP rights and the implications of model merging attacks can help deter malicious activities and foster a culture of ethical AI development.
By addressing these legal and ethical considerations proactively, the AI community can mitigate the risks associated with model merging attacks and uphold the integrity of intellectual property rights.

What other emerging technologies or techniques, beyond watermarking and fingerprinting, could be explored to protect the IP of LLMs in the face of model merging threats?

In addition to watermarking and fingerprinting, several emerging technologies and techniques can be explored to enhance the protection of Intellectual Property (IP) for Large Language Models (LLMs) against model merging threats:

Homomorphic Encryption: Homomorphic encryption allows computations to be performed on encrypted data without decrypting it. By applying this technique to model parameters, the privacy and integrity of the model can be preserved during merging processes.

Differential Privacy: Incorporating differential privacy mechanisms into LLMs can help protect sensitive information and prevent unauthorized access to model details during merging. This ensures that the privacy of the original models is maintained.

Blockchain Technology: Utilizing blockchain technology for tracking and verifying the ownership and usage rights of LLM components can establish a transparent and immutable record of model contributions. This can prevent unauthorized model merging and IP infringements.

Secure Multi-Party Computation: Secure multi-party computation protocols enable multiple parties to jointly compute a function over their inputs while keeping those inputs private. This can be applied to model merging to ensure that each party retains control over their contributions.

Zero-Knowledge Proofs: Zero-knowledge proofs allow one party to prove to another that a statement is true without revealing any information beyond the validity of the statement. By integrating zero-knowledge proofs into model merging processes, the IP of the original models can be protected without disclosing sensitive details.

By exploring these advanced technologies and techniques, the protection of IP for LLMs can be strengthened, mitigating the risks associated with model merging threats and ensuring the integrity and ownership of AI models.