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RedEvoAgent evolves red-teaming skills in real-time, outpacing traditional methods and enhancing safety against sophisticated LLM threats.
MLLMs can leak sensitive personal information when visual evidence is lacking, but the Dynamic Relational Unlearning Framework (DRUF) significantly mitigates this risk without sacrificing performance.
ForensicsTok achieves unprecedented precision in image tampering localization by directly generating spatially grounded token sequences, bypassing traditional bottlenecks.
Even state-of-the-art AI-generated image detectors struggle when images are cropped, resized, or compressed, revealing a critical gap in real-world robustness.
MLLMs can now spot subtle image forgeries with SOTA accuracy by strategically using forensic tools to expose hidden inconsistencies, outperforming traditional text-centric approaches.
