Open-source framework to benchmark adversarial attacks on AI-powered SOCs

The article introduces RedSOC, an open-source framework to evaluate the resilience of AI-powered Security Operations Centers (SOCs) against adversarial attacks. It benchmarks three attack types: corpus poisoning, direct prompt injection, and indirect prompt injection, achieving a 100% detection rate across 15 scenarios.

Details

The article highlights the need to systematically test the resilience of AI-powered SOCs against adversarial attacks, as most organizations are now relying on large language models (LLMs) for alert triage, threat intelligence, and incident response. RedSOC is an open-source framework designed to evaluate the robustness of these AI-powered SOC systems. It implements three types of attacks: corpus poisoning (injecting malicious documents to steer analyst responses), direct prompt injection (embedding override instructions in user queries), and indirect prompt injection (hiding adversarial instructions in retrieved documents). The detection layer uses a combination of semantic anomaly scoring, provenance tracking, and response consistency checking to identify these attacks without requiring access to the model internals. The benchmark results show an 80% overall attack success rate, but the detection mechanisms achieve a 100% detection rate across 15 scenarios tested using the Llama 3.2 model. The article also mentions a survey paper that maps the broader adversarial threat landscape for RAG-based LLM systems.