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"Securing GenAI"

This report explores prompt attacks on generative AI, offering a taxonomy of goal hijacking, guardrail bypass, information leakage, and infrastructure attacks. Research reveals major vulnerabilities in LLMs, with some attacks reaching 88% success rates. Download the white paper to learn about emerging threats and defenses.

Organizations adopting generative AI face growing security challenges as adversarial prompt attacks expose vulnerabilities in language models. Evaluations show attack success rates over 50%, with some techniques reaching 88% effectiveness across models.

This white paper offers a taxonomy to understand and defend against prompt-based threats, covering:

• Three attack vectors: guardrail bypass, information leakage, and goal hijacking
• Techniques like prompt engineering, social engineering, and obfuscation
• Real-world scenarios showing how adversaries exploit AI systems

Learn strategies to detect and prevent these threats. Read the full white paper for securing your AI applications.

Offered Free by: Palo Alto Networks
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