From Noise to Clarity: Unraveling the Adversarial Suffix of Large Language Model Attacks via Translation of Text Embeddings

Original Paper: https://arxiv.org/abs/2402.16006

By: Hao Wang, Hao Li, Minlie Huang, Lei Sha

Abstract:

The safety defense methods of Large language models(LLMs) stays limited because the dangerous prompts are manually curated to just few known attack types, which fails to keep pace with emerging varieties. Recent studies found that attaching suffixes to harmful instructions can hack the defense of LLMs and lead to dangerous outputs. This method, while effective, leaves a gap in understanding the underlying mechanics of such adversarial suffix due to the non-readability and it can be relatively easily seen through by common defense methods such as perplexity

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Summary Notes

Artificial Intelligence, especially through Large Language Models (LLMs) like ChatGPT and LLaMa, has reshaped our digital interactions.

Yet, these AI marvels are prone to attacks where malicious inputs can manipulate them to produce harmful content. Traditional defenses often fall short against these sophisticated threats.

Enter the Adversarial Suffixes Embedding Translation Framework (ASETF), a cutting-edge solution designed to strengthen our defense against these vulnerabilities.

Unpacking ASETF

ASETF marks a pivotal advancement in safeguarding AI-generated content.

It focuses on converting adversarial suffixes—essentially, tricky inputs designed to corrupt AI outputs—into understandable text.

This not only aids in spotting these harmful inputs but also deepens our grasp on how LLMs process such data. By transforming these suffixes into meaningful text, ASETF retains attack effectiveness while significantly improving the clarity of the output.

How ASETF Works

The ASETF approach is built on two main steps:

• Identifying Adversarial Suffixes:
• Through discrete optimization, this phase pinpoints the adversarial suffixes that are most likely to trick LLMs into harmful outputs, using a method that balances effectiveness with clarity.
• Translating Suffixes into Coherent Text:
• This phase takes the identified adversarial inputs and converts them into clear, semantically meaningful text, using a self-supervised learning technique to ensure the translations match the intended harmful instructions.

ASETF in Action: Results and Discoveries

Experiments with ASETF have shown noteworthy successes across various LLMs:

• ASETF has achieved higher success rates in generating undetected adversarial content, surpassing other methods in textual clarity and prompt diversity.
• The framework can create universal adversarial suffixes, effective across multiple LLMs, including those not directly studied (black-box models).
• It enhances the semantic variety in generated prompts, crucial for evading detection.

Challenges and Ethical Considerations

Despite its advances, ASETF faces hurdles such as the high computational demand of discrete optimization and the intricate balance between relevance and clarity. Ethically, ASETF is developed with a focus on defense, aiming to protect against malicious AI use. Training materials are public, and the code will be accessible on GitHub to ensure transparency and foster community involvement.

Conclusion: A Step Forward in AI Security

Supported by the National Natural Science Foundation of China, ASETF represents a significant leap in securing LLMs against adversarial threats. By improving our understanding of AI's vulnerability to harmful inputs, ASETF paves the way for more robust defense mechanisms. Its success heralds a new phase in the secure application of AI, ensuring that technological advancements are coupled with strong safeguards against misuse.