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This survey provides a unified analysis of how LLMs amplify web vulnerabilities across client-side, server-side, and pipeline layers while evaluating defenses and their limitations, and outlines future directions for secure AI-enabled web systems.
JITterFlip is presented, the first BFA targeting the host-side JIT serving control plane of GPU-based LLM inference, and develops a decision-guided fault-vulnerable code analysis that enables both gibberish output generation and a correct-output sponge attack.
This work establishes a theoretical framework that proves that privacy leakage accumulates as more ODMM models are exposed, and proposes PRIME (Privacy Amplification RIsk from One-Dataset-Multiple-Model Exposure) to systematically assess this risk and quantify the resulting leakage using membership inference attacks (M...
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