jev-phishing-bench

Name: jev-phishing-bench
Availability: InStock
Author: anisselbd

Jev-powered: The signal result above was challenged on three points: no non-AI baseline, selection and evaluation on the same emails, and no equivalent decomposition for the LLM. Three controls were a

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Last updated: September 23, 2026

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git clone https://github.com/anisselbd/jev-phishing-bench.git

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jev-phishing-bench

The signal result above was challenged on three points: no non-AI baseline, selection and evaluation on the same emails, and no equivalent decomposition for the LLM. Three controls were added (

bench/heuristics.py

bench/protocol.py

run_llm_signals.py

); nothing above was changed. Full tables in

results/report.md

, chart in

results/controls.png

Where Jev makes the decision

Jev returns a structured decision for the local program; consult the source for the exact decision policy.

Why it matters

Adds structured choices or scores to the workflow; performance and cost benefits have not been independently verified.

Claims & evidence

Based on repository metadata and README with rule-based classification; pending human review, with no independent runtime or performance verification.

About Jev

Jev is TypeSafe AI's System One decision model: it returns typed scores and choices (not free text), so agents can route, filter and gate at low latency and cost.

Source: anisselbd/jev-phishing-bench — 5 stars. Listed by Awesome Jev.

jev-phishing-bench

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jev-phishing-bench

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