Xingjun Ma

Adversarial techniques traditionally seen as threats are now being repurposed by content owners to proactively safeguard their visual assets from misuse.

Environment evolution can reveal 17% more safety failures in complex tasks compared to static benchmarks, reshaping our understanding of agent vulnerabilities.

Vera reveals that existing LLM agents exhibit up to 93.9% vulnerability to multi-channel attacks, highlighting a significant gap in current safety evaluations.

ShutterMuse redefines photography guidance by combining composition and pose recommendations, outperforming existing models while cutting inference costs.

Self-evolving LLMs can amplify adversarial threats, making every known attack lineage-persistent and exposing critical vulnerabilities that static defenses can't address.

SentGuard detects 90.5% of unsafe content within two sentences, revolutionizing real-time moderation for large language models.

Guard models trained with BraveGuard can detect safety threats in computer-use agents with over 82% accuracy, a significant leap from conventional methods.

Even state-of-the-art multimodal LLMs like GPT-5.2 and Claude 4.5 can be jailbroken nearly half the time using OpenRT's diverse suite of attacks, revealing a critical lack of generalization across attack paradigms.