We propose Obfuscated Semantic Null space Injection for Privacy (OSNIP), a lightweight client-side encryption framework for privacy-preserving LLM inference. Generalizing the geometric intuition of linear kernels to the high-dimensional latent space of LLMs, we formally define the ``Obfuscated Semantic Null Space'', a high-dimensional regime that preserves semantic fidelity while enforcing near-orthogonality to the original embedding. By injecting perturbations that project the original embedding into this space, OSNIP ensures privacy without any post-processing. Furthermore, OSNIP employs a key-dependent stochastic mapping that synthesizes individualized perturbation trajectories unique to each user. Evaluations on 12 generative and classification benchmarks show that OSNIP achieves state-of-the-art performance, sharply reducing attack success rates while maintaining strong model utility under strict security constraints.

Diffusion models have recently achieved remarkable success in image generation, yet growing evidence shows their vulnerability to backdoor attacks, where adversaries implant covert triggers to manipulate outputs. While existing defenses can detect many such attacks via visual inspection and neural network-based analysis, we identify a more lightweight and stealthy threat, termed BadBlocks. BadBlocks selectively contaminates specific blocks within the UNet architecture while preserving the normal behavior of the remaining components. Compared with prior methods, it requires only about 30% of the computation and 20% of the GPU time, yet achieves high attack success rates with minimal perceptual degradation. Extensive experiments demonstrate that BadBlocks can effectively evade state-of-the-art defenses, particularly attention-based detection frameworks. Ablation studies further reveal that effective backdoor injection does not require fine-tuning the entire network and highlight the critical role of certain layers in backdoor mapping. Overall, BadBlocks substantially lowers the barrier for backdooring large-scale diffusion models, even on consumer-grade GPUs.