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First reported arxiv.org
Decoding the Threat Landscape : ChatGPT, FraudGPT, and WormGPT in Social Engineering Attacks
An arXiv paper by Polra Victor Falade, 'Decoding the Threat Landscape: ChatGPT, FraudGPT, and WormGPT in Social Engineering Attacks,' uses a blog-mining technique to survey how generative AI models empower attackers to craft personalized phishing lures, produce deepfakes, and exploit cognitive biases. The paper also outlines defensive strategies including traditional and AI-powered security measures. Details →First reported arxiv.org
Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models
Researchers present MIND, a cognitive jailbreak framework that models a text-to-image system's latent defense mechanisms as a belief-state inference problem, interpreting multi-modal feedback (textual refusal, visual blocking, semantic sanitization) to iteratively craft adversarial prompts that produce NSFW content. Using a Multi-modal Judge, Defense Profiler, and Meta-Memory module, MIND reports a 95.62% attack success rate against defended Stable Diffusion v1.5 and up to 91.58% against commercial T2I systems like Wan-2.5. Details →How the wire is made
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