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FakeMark is presented, a gradient-guided false-claim attack for image classifiers that uses a white-box surrogate but never queries or accesses the victim model during attack construction, to motivate provenance-aware, multi-factor ownership protocols.
Large language models (LLMs) are high-value assets that can be derived through redeployment, fine-tuning, quantization, or further alignment. Because deployed LLMs are commonly exposed only through query APIs, ownership verification must often rely on black-box text responses. This setting is difficult: generations are...
This paper introduces the concept of instruction-dense visual jailbreaks, in which image-generation models produce detailed, readable, and actionable harmful instructions within images, and proposes TYPO, a black-box framework that exploits this safety gap by automatically generating adversarial TYPOgraphy prompts.
CloakDiff is proposed, the first framework for reversible, high fidelity privacy protection against text-based query attacks in VLMs and EDM Heuristic Sampling, a principled diffusion schedule for adversarial guidance.
The proposed LLM agent early-stopping cascade outperforms the best single-gate baseline in every model-environment pair, saving 1.5-8.8 times more compute at a 90% recall target.
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