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title: Neuralchemy
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Neuralchemy
AI Security • Prompt Defense • LLM Safety
Building secure, reliable AI systems focused on prompt security, adversarial robustness, and practical safety tooling.
Featured Project — PromptShield & Threat Matrix
A comprehensive prompt injection and adversarial intent detection framework, classifying malicious jailbreak patterns across real-world and massive synthetic attack typologies.
Core Resources
SOTA Datasets: neuralchemy/prompt-injection-Threat-Matrix A highly curated, leakage-free classification dataset mapping 32,000+ entries across a 5-dimensional security ontology (Intent, Technique, Severity).
neuralchemy/prompt-injection-dataset 6000+ prompt injection and benign samples collected from realistic attack scenarios.
DeBERTa Fine-Tuned Model: neuralchemy/prompt-injection-deberta Transformer-based prompt injection classifier.
DistilBERT Base Model: neuralchemy/distilbert-base-threat-matrix A 99.4% F1-scoring Transformer defense gateway, optimized for high-speed, accurate prompt intent gating.
Classical ML Models: neuralchemy/prompt-injection-detector Ultra-lightweight machine learning classifiers (RF, LR) for legacy/offline prompt risk detection.
Live Demo Space: Prompt-injection-DeBERTa Interactive inference demo for prompt safety classification.
Research & Architecture
- AI In The Loop (AITL): Pioneering an inherently secure, multi-agent orchestration loop designed strictly to mitigate Prompt Injection (PI) bypass methodologies, enforce JSON-structured constraints, and evaluate autonomous systemic risks. https://zenodo.org/records/19551173
Mission
Advancing AI security through enterprise open-source datasets, robust model deployment, and adversarial safety research.
Connectivity
- Website: https://www.neuralchemy.in
Building safer AI systems through open security research. 🚀