This series is a part of the Adversarial Attacks Against LLMs, a Multi-part Series, breaking down the current landscape of LLM and GenAI Security against Adversarial Attacks.
LLMs are changing how we interact with technology, but they come with risks. These powerful models can lead to market share losses or even costly lawsuits. Adversarial attacks, and deliberate attempts to make machines malfunction, have become a significant concern in the ML community.
Join me as I try to understand and break down adversarial attacks and the measures that can be taken to prevent such attacks in this multi-part series where we talk about prevention against Adversarial Attacks for LLM Powered systems, which comes complete with Theory, Practicals, and Studies and Tutorials.
Understanding Adversarial Attacks
These attacks can cause anything from information leaks to incorrect outputs. Unlike image recognition systems, detecting and mitigating these attacks in text models is trickier due to the discrete nature of text data.
Types of Attacks
Token Manipulation: Feeding faulty tokens into an LLM can result in irrelevant or false information. Ensuring models like LLaMA and GPT handle these gracefully is crucial.
Token Insertion: Adding extra tokens can change the context and produce nonsense.
Token Substitution: Replacing tokens might mislead the model.
Token Deletion: Removing tokens can lead to missing information and incorrect outputs.
Prompt Attacks: Manipulating the model via prompts, common in models like GPT-3, but mitigated in GPT-4 with better guardrails.
Guarding Against Attacks
Techniques like prompt injection and jailbreaking confuse the model with mixed prompts or false contexts, leading to undesirable outputs. For instance, giving LLaMA a prompt that combines trusted and untrusted information can make it output incorrect translations or sensitive information.
Conclusion
Making LLMs robust against adversarial attacks is crucial. Follow me as I continue this suit and dive deeper into preventive measures that can be taken against Adversarial Attacks.
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