Journal of Embedded Systems, Security and Intelligent Systems
Vol 7 No 2 (2026): June 2026

Secure Automated Reconnaissance Using LLM Agents and a Layered Cryptographic Protection Pipeline

Ikhwan Ruslianto (Universitas Tanjungpura)
Wijang Widhiarso (Universitas Teknologi Digital Indonesia)
Hafiz Muhardi (Universitas Tanjungpura)



Article Info

Publish Date
17 Jun 2026

Abstract

Purpose – This study aims to design and evaluate a secure reconnaissance platform that integrates Large Language Model (LLM) agents for dynamic tool orchestration with a layered cryptographic protection pipeline to accelerate penetration-testing information gathering while protecting sensitive artefacts. Design/methods/approach – The platform unifies Nmap, WHOIS, and theHarvester under an LLM controller that generates command-line parameters through schema-constrained orchestration. Each output is validated against a strict JSON schema before execution. The protection pipeline applies AES-256-GCM with envelope keys for confidentiality, HMAC-SHA256 hash chaining for tamper-evident logs, Ed25519 signatures for report-level non-repudiation, and Argon2id-derived session keys. Evaluation was conducted on three public domains across thirty runs each, measuring latency, cryptographic overhead, verification integrity, signature validation, and an internal CVSS-informed triage score. Findings - The prototype showed that automated reconnaissance and cryptographic auditability can be combined with limited performance cost. A full pass over untan.ac.id completed in 14.97 seconds and produced an internal triage-heuristic score of 78/100. Cryptographic operations added 312 ms on average, equal to about 2.08% of total latency. All hash-chain links were verified, and Ed25519 signatures were validated in 71 µs. Research implications/limitations – The findings support red-team and blue-team workflows requiring faster, auditable reconnaissance reporting. However, the evidence is limited to three public domains under one network condition; therefore, the results should be interpreted as feasibility evidence, not generalisable performance claims. The risk score is an internal prioritisation heuristic, not a validated severity instrument. Originality/value – The study contributes a secure LLM-orchestrated reconnaissance framework that integrates structured command orchestration with cryptographic safeguards for confidentiality, integrity, and non-repudiation.

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Journal Info

Abbrev

JESSI

Publisher

Subject

Computer Science & IT

Description

The Journal of Embedded System Security and Intelligent System (JESSI), ISSN/e-ISSN 2745-925X/2722-273X covers all topics of technology in the field of embedded system, computer and network security, and intelligence system as well as innovative and productive ideas related to emerging technology ...