AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

Claire Radar

NetInjectBench

NetInjectBench evaluates LLM agents for network operations under indirect prompt injection. It comprises 130 scenarios: 40 benign, 40 weak-attack, 40 strong-attack, and 10 approved high-impact changes, with separated untrusted artifact text, trusted policy metadata, and evaluation labels. Scoring measures unsafe tool-action rate, usefulness, and overblocking across models and defenses.

Evidence available for this benchmark

Numeric results have not been collected for this source record. Use the paper, code or dataset below to check the task and experiments; this does not imply that the original work contains no results.

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Cited documents and dates (1)

Catalog collected on 2026-10-14; resources and fields reflect that saved source.

Evaluation context and evidence

Source
Claire Radar
Publisher
Not recorded
Benchmark release
2026-07-11
Collected
2026-10-14
Modality
Not recorded

These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.

Reported results 0

This source record has no numeric results in our collection. That does not mean a score of zero or that no public results exist. Consult the original resources above.