NetInjectBench: Benchmarking Indirect Prompt Injection in Tool-Using Large Language Model Agents for Network Operations
NetInjectBench provides 130 scenarios benchmarking indirect prompt injection in LLM agents used for network operations — directly relevant for SOC teams adopting AI-assisted triage of tickets, alerts, and logs.
Summary written by editorial AI · Source link below
arXiv:2607.10490v1 Announce Type: new Abstract: Tool-using large language model (LLM) agents are attractive for network operations, but tickets, alerts, logs, runbooks, and ChatOps messages can carry indirect prompt injections. We present NetInjectBench, a 130-scenario benchmark that separates untrusted artifact text, trusted policy metadata, and evaluation labels for network-operation tool use. The sample contains 40 benign, 40 weak-attack, 40 strong-attack, and 10 approved high-impact change
Editorial Analysis
SOC and NetOps teams increasingly feed untrusted data into LLM agents; without injection-resilience testing, adversaries can manipulate automated responses.
Red-team any LLM-based network operations tooling against indirect prompt injection using structured benchmarks before production use.
AI-assisted network operations tools face prompt injection risks from the very tickets and logs they process.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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