Adversarial Manipulation of Autonomous SOC Pipelines

A sophisticated attacker who knows the defender runs an autonomous SOC stops evading signatures and starts evading the pipeline itself: crafting telemetry to manipulate the LLM triage analyst, constructing behavior statistically indistinguishable from baseline, flooding the alert queue to bury a true positive, and weaponizing the SOC's autonomous responder to quarantine production systems. This threat lab analyzes the autonomous SOC detection-triage-investigation-response pipeline as an adversarial attack surface — the threat model for each vector, how each attack degrades SOC effectiveness, and the countermeasures and architectural controls that raise the cost. Grounded in MITRE ATLAS, OWASP Agentic Security Initiative, NIST AI RMF, and the adversarial machine-learning literature.