paperSeptember 2026Unreviewed

Architecting the Secure AI-SOC: A Neurosymbolic Framework for Pipeline Integrity and Threat Mitigation

Anna Gazani, Spyridon Kounoupidis, Panagiotis Katsaros, Nikolaos Kekatos, Grigorios Tsoumakas, Georgios Koutidis

Abstract

The integration of Large Language Models (LLMs) into Security Operations Centers (SOCs) streamlines threat intelligence but introduces critical vulnerabilities, notably indirect prompt injection via log poisoning. Adversaries exploit this vector to execute multistep ``promptware'' kill chains by embedding malicious payloads within system logs to hijack the LLM's operational logic. Securing this pipeline presents a dichotomy: deterministic defenses are computationally efficient yet semantically b

Framework mappings

OWASP Top 10 for LLM Applications

LLM01Prompt Injection
LLM04Data and Model Poisoning

MITRE ATLAS

AML.T0020Poison Training Data
AML.T0051LLM Prompt Injection

Suggested from the entry's categories.

Cite

@misc{gazani2026architecting,
  title = {{Architecting the Secure AI-SOC: A Neurosymbolic Framework for Pipeline Integrity and Threat Mitigation}},
  author = {Anna Gazani and Spyridon Kounoupidis and Panagiotis Katsaros and Nikolaos Kekatos and Grigorios Tsoumakas and Georgios Koutidis},
  year = {2026},
  month = sep,
  eprint = {2609.10707},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.10707}
}