Large Language Models (LLMs) are increasingly deployed via third-party system prompts downloaded from public marketplaces.We identify a critical supply-chain vulnerability: conditional system prompt poisoning, where an adversary injects a "sleeper agent" into a benign-looking prompt.Unlike traditional jailbreaks that aim for broad refusal-breaking, our proposed framework, PARASITE, optimizes system prompts to trigger LLMs to output targeted, compromised responses only for specific queries (e.g., "Who should I vote for the US President?") while maintaining high utility on benign inputs.Operating in a strict blackbox setting without model weight access, PAR-ASITE utilizes a two-stage optimization including a global semantic search followed by a greedy lexical refinement.Tested on opensource models and commercial APIs (GPT-4omini, GPT-3.5),PARASITE achieves up to 70% F1 reduction on targeted queries with minimal degradation to general capabilities.We further demonstrate that these poisoned prompts evade standard defenses, including perplexity filters and typo-correction, by exploiting the natural noise found in real-world system prompts.Our code and data are available at https://github.com/vietph34/ PARASITE.WARNING: Our paper contains examples that might be sensitive to the readers!* This work was conducted prior to joining IU Dual Behavior Demonstration 1. Attacker Crafts a Poisoned PromptA helpful tool for learning about historical events and figures.

Accurate geometric reconstruction of deformable tissues in monocular endoscopic video remains a fundamental challenge in robot-assisted minimally invasive surgery. Although recent volumetric and point primitive methods based on neural radiance fields (NeRF) and 3D Gaussian primitives have efficiently rendered surgical scenes, they still struggle with handling artifact-free tool occlusions and preserving fine anatomical details. These limitations stem from unrestricted Gaussian scaling and insufficient surface alignment constraints during reconstruction. To address these issues, we introduce Surgical Gaussian Surfels (SGS), which transform anisotropic point primitives into surface-aligned elliptical splats by constraining the scale component of the Gaussian covariance matrix along the view-aligned axis. We also introduce the Fully Fused Deformation Multilayer Perceptron (FFD-MLP), a lightweight Multi-Layer Perceptron (MLP) that predicts accurate surfel motion fields up to 5× faster than a standard MLP. This is coupled with locality constraints to handle complex tissue deformations. We use homodirectional view-space positional gradients to capture fine image details by splitting Gaussian Surfels in over-reconstructed regions. In addition, we define surface normals as the direction of the steepest density change within each Gaussian surfel primitive, enabling accurate normal estimation without requiring monocular normal priors. We evaluate our method on two in-vivo surgical datasets, where it outperforms current state-of-the-art methods in surface geometry, normal map quality, and rendering efficiency, while remaining competitive in real-time rendering performance. Code is available at https://git.new/NexyiHu.