Document-image PII detection can miss identifiers despite improving average localization scores. LeakageBench evaluates 500 pages with 11,954 annotations.
LeakageBench: Document-Level Leakage Risk for Redacting Personally Identifiable Information in Document Images
Evaluated models: GLiNER-base, GLiNER-multi-PII, NVIDIA GLiNER PII+5 more
Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…
Aegis: Towards Governance, Integrity, and Security of AI Voice Agents
Evaluated models: GPT-4o, GPT-4o Mini, Gemini 1.5 Pro+4 more
FC-Attack leverages automatically generated flowcharts containing step-by-step descriptions derived or rephrased from harmful queries, combined with a benign textual prompt, to jailbreak Large Vision-Language Models (LVLMs). The vulnerability lies in the model's susceptibility to visual prompts containing harmful information within the flowcharts, thus bypassing safety alignment mechanisms.
FC-Attack: Jailbreaking Large Vision-Language Models via Auto-Generated Flowcharts
Evaluated models: Claude 3.5 Sonnet 20240620, Gemini 1.5 Flash, GPT-4o 2024-08-06+4 more