RIVA: Leveraging LLM Agents for Reliable Configuration Drift Detection
2026-03-04T19:55:30Z•951dff64641fcf1367e6c71fc08bcb2b2b3b4e0e54c1c1d2030aa214ef01e585
C-to-Rust-migrationGUI-regression-detectionLLM-agentsautomated-migrationchange-aware-testingconfiguration-driftinfrastructure-as-codememory-safetymicroservice-fuzzingneural-network-verificationretrieval-augmented-generationsustainabilitytechnical-debt-managementtool-misbehavioruncertainty-driven-testing
What happened
This collection of recent software-engineering papers highlights advances with direct security impact: RIVA presents a multi-agent LLM system for robust Infrastructure-as-Code (IaC) verification that cross-validates tool outputs to detect configuration drift even when tools return incorrect data, reducing missed drift/false alarms. A paper on fuzzing microservices proposes an ‘uncertainty-driven’ system-level fuzzing architecture to surface nondeterministic runtime faults and fault-propagation that can lead to security and availability issues. His2Trans describes a build-aware, history-augment
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_se
- Record identifier
- 951dff64641fcf1367e6c71fc08bcb2b2b3b4e0e54c1c1d2030aa214ef01e585
- Enrichment time
- 2026-03-04T19:55:30Z
- AI-assisted enrichment
- Yes
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