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Fine-Tuning LLMs to Resist Indirect Prompt Injection Attacks

A fine-tuning approach was developed to enhance Llama3-8B's resistance to indirect prompt injection attacks. The method uses data delimiters in the system prompt to help the model ignore malicious instructions within user-provided content. The fine-tuned model achieved a 100% pass rate in resisting tested prompt injection attacks. The model and training scripts have been publicly released.

When your AI Assistant has an evil twin

An indirect prompt injection attack against Google Gemini Advanced demonstrates how malicious emails can manipulate the AI assistant into displaying social engineering messages. The attack tricks users into revealing confidential information by exploiting Gemini's email summarization capabilities. The vulnerability highlights potential security risks in AI assistants with data access capabilities.

Generative AI - An Attacker's View

Generative AI is increasingly being used by threat actors for cyber attacks. Attackers can leverage AI for reconnaissance, gathering personal information quickly and creating targeted phishing emails. The technology enables sophisticated social engineering through deepfakes, voice cloning, and malicious code generation, with potential for more advanced attacks in the near future.

Domain-specific prompt injection detection

A domain-specific machine learning approach was developed to detect prompt injection attacks in job application contexts using a fine-tuned DistilBERT classifier. The model was trained on a custom dataset of job applications and prompt injection examples, achieving approximately 80% accuracy in identifying potential injection attempts. The research highlights the challenges of detecting prompt injection in large language models and emphasizes that such detection methods are just one part of a comprehensive security strategy.

Should you let ChatGPT control your browser?

This article explores the security risks of granting Large Language Models (LLMs) control over web browsers. Two attack scenarios demonstrate how prompt injection vulnerabilities can be exploited to hijack browser agents and perform malicious actions. The article highlights critical security challenges in LLM-driven browser automation and proposes potential defense strategies.