RedSOC is an open-source framework designed to automate the detection and mitigation of adversarial attacks on Large Language Models (LLMs). This project addresses critical vulnerabilities in AI infrastructure, directly aligning with the 2023 Executive Order on AI Safety and the 2025 "America's AI Action Plan."
- National Importance: RedSOC provides the "digital shields" necessary for securing U.S. critical infrastructure against prompt injection and data exfiltration.
- Technical Authority: This repository serves as a benchmark for AI Red Teaming workflows, utilized by researchers to stress-test model integrity.
- Automated Injection Testing: Proactively identifies security gaps in RAG-based systems.
- SOC Integration: Plugs into existing Security Operations Centers to provide real-time AI threat intelligence.
RedSOC is an open-source framework that systematically evaluates how AI-powered security assistants fail under adversarial conditions — and whether detection methods can catch those failures.
As two-thirds of organizations now deploy AI in their SOC environments, no unified framework exists to red-team these systems. RedSOC fills that gap.
- Prompt Injection (direct, indirect, multi-turn)
- RAG Poisoning (corpus poisoning, backdoor attacks)
- Multi-Agent Hijacking
- Concept Drift under adversarial conditions
- SOC-RAG Pipeline — simulated AI security assistant
- Attack Simulator — benchmarked attack modules
- Detection Layer — semantic anomaly + provenance tracking
- Benchmark Runner — automated results and charts
🚧 Active development — April 2026
Paper: https://doi.org/10.6084/m9.figshare.32016498 Benchmark Dataset: https://doi.org/10.6084/m9.figshare.32016534 Benchmark Charts: https://doi.org/10.6084/m9.figshare.32016564 Zenodo Record: https://zenodo.org/records/19519927
If you use RedSOC in your research, please cite:
Krishnakaanth Reddy, Y. (2026). RedSOC: An Adversarial Evaluation Framework for LLM-Integrated Security Operations Centers. arXiv preprint.
Krishnakaanth Reddy — IEEE Professional Member | ACM Professional Member | AI Security Researcher
MIT License