run-attacks

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Mis à jour24 mars 2026 à 15:51

Run the pac_clip_sum membership inference attack test suite and summarize results.

Installation

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

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Source

cwida

cwida/privacy

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Téléchargement

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Analystes en sécurité de l'informationProfessions informatiques et mathématiques·SOC 15-1212

SKILL.md

readonly

name	run-attacks
description	Run the pac_clip_sum membership inference attack test suite and summarize results.

Context

PAC (Probably Approximately Correct) privacy privatizes SQL aggregates via 64 parallel SWAR bitslice counters with MI-bounded noise. pac_clip_sum adds per-user contribution clipping using magnitude-level decomposition (4x bands, 2-bit shift) with distinct-contributor bitmaps. Unsupported outlier levels are hard-zeroed to prevent variance side-channel attacks.

Instructions

Build if needed: GEN=ninja make 2>&1 | tail -5
Run the main attack suite: bash attacks/clip_attack_test.sh 2>/dev/null
Run the multi-row attack: bash attacks/clip_multirow_test.sh 2>/dev/null
Run stress tests if available: bash attacks/clip_hardzero_stress.sh 2>/dev/null

Summarize results as a table:

Attack scenario, clip_support value, attack accuracy, std_in, std_out, std ratio
Flag any accuracy above 60% as a potential regression
Compare to baselines in attacks/clip_attack_results.md

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Build and run pac_clip_sum unit tests.

2026-03-2418

name	run-attacks
description	Run the pac_clip_sum membership inference attack test suite and summarize results.

Context

Instructions

Build if needed: GEN=ninja make 2>&1 | tail -5
Run the main attack suite: bash attacks/clip_attack_test.sh 2>/dev/null
Run the multi-row attack: bash attacks/clip_multirow_test.sh 2>/dev/null
Run stress tests if available: bash attacks/clip_hardzero_stress.sh 2>/dev/null

Summarize results as a table:

Attack scenario, clip_support value, attack accuracy, std_in, std_out, std ratio
Flag any accuracy above 60% as a potential regression
Compare to baselines in attacks/clip_attack_results.md