Operational logs create a need for private, resource-efficient incident analysis, but aggregate detection scores can conceal severe prediction bias. We present TriCalRAG, a reproducible benchmark for log-anomaly detection with generated root-cause and remediation outputs across BGL, HDFS, Thunderbird, and OpenStack. Th...
Rohit Patel, S. K. Mohanty, Jeenal Chaudhary· 1 citation
Root cause analysis at a remote site is slow: evidence is scattered across pod logs, Kubernetes events and cluster-level objects, and many operators cannot send production logs to a hosted model at all. On-premise inference removes the second constraint but raises a question live-cluster benchmarks have not addressed:...
Rohit Patel, S. K. Mohanty, Jeenal Chaudhary· 0 citations
This work presents TriCalRAG, a benchmark evaluating open-weight LLMs served locally via vLLM on a single high-memory workstation GPU against a classical LSTM-based log anomaly detector (DeepLog), across four real, publicly available log datasets (BGL, HDFS, Thun-derbird, OpenStack).
Rohit Patel, S. K. Mohanty, Jeenal Chaudhary· 0 citations
TriShieldRAG is proposed, a three-layered framework: an Ingest Guard for document-level screening, a Retrieval Scorer for trust-aware re-ranking, and a Cross-LLM Consensus over three diverse models to give complementary protection, limiting the ability of poisoned documents to succeed through any single failure.
S. K. Mohanty, Rohit Patel, K. Yuvaraj et al.· 0 citations
Evaluated against the non-adaptive attacker described in the original PoisonedRAG paper, the full pipeline reduces attack success rate from roughly 91% to roughly 13%, while preserving accuracy on benign, unpoisoned queries.
S. K. Mohanty, Rohit Patel, K. Yuvaraj et al.· arXiv.org· 0 citations
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