Prompt Fuzzing Tears Through LLM Guardrails — Evasion Hits Highs Across Open and Closed Models
Evasion rates spiked into high levels for key model combos. Turns out, five years of safety tweaks haven't hardened LLMs against scalable fuzzing attacks.
⚡ Key Takeaways
- Prompt fuzzing scales jailbreaks, turning small evasion rates into mass breaches.
- Open and closed LLMs show similar fragility under meaning-preserving rephrasing.
- Defend with layered controls, output validation, and automated adversarial testing.
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Originally reported by Palo Alto Unit 42
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