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LLM-powered systems are surprisingly vulnerable to multi-pronged attacks that combine conventional cyber threats, adversarial ML, and conversational manipulation, all converging on a few key weaknesses.
LLM-powered healthcare systems are vulnerable to complex attack paths combining prompt injection and conventional cyberattacks, demanding a new goal-driven risk assessment approach.
Multimodal LLMs can be hijacked by adversarial instructions hidden inside seemingly innocuous images, achieving a 64% success rate in manipulating model outputs.
