Research Compliance

IAMRAGBENCH: Benchmarking Authorization Expansions in LLM-Generated AWS IAM Policies under RAG Poisoning

Nov 2, 2026 Source ↗

Key Insights

Large language models (LLMs) using retrieval-augmented generation (RAG) can synthesize Amazon Web Services (AWS) Identity and Access Management (IAM) policies from tasks and operational documents.
Att.

Cite this synthesis DOI: 10.48550/arXiv.2611.03431 ↗

Show formatted citation

@misc{ acaciacompliance-research-iamragbench-benchmarking-authorization-expansions-in-llm-gen,
  title = { IAMRAGBENCH: Benchmarking Authorization Expansions in LLM-Generated AWS IAM Policies under RAG Poisoning },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2611.03431 },
  url = { http://arxiv.org/abs/2611.03431v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - IAMRAGBENCH: Benchmarking Authorization Expansions in LLM-Generated AWS IAM Policies under RAG Poisoning
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2611.03431
UR  - http://arxiv.org/abs/2611.03431v1
ER  -

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Overview

Large language models (LLMs) using retrieval-augmented generation (RAG) can synthesize Amazon Web Services (AWS) Identity and Access Management (IAM) policies from tasks and operational documents. Att

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This item adds to the AML and compliance knowledge base. Practitioners can use it to stay current on IAMRAGBENCH: Benchmarking Authorization Expansions in LLM-Generated AWS IAM Policies under RAG Poisoning, but should validate its claims against primary sources and more recent work before relying on it in production decisions.

Key Takeaways

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Article Metadata

Evergreen Synthesized

aml cloud-infrastructure llm vector-search

Infrastructure

SQI 0.750