Skip to main content

poisoning-inference-extraction

Plan ART poisoning, privacy inference, model inversion/reconstruction, extraction, and detector/mitigation workflows.

Zur Installation springen

Quellinformationen

Repository
VectorSpaceLab/AREX-Skill
Letzte Quellaktivität
26. August 2026 um 16:31
Erkannte Sprache von SKILL.md
Englisch
Sterne
12
Forks
2

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

Datei-Explorer
5 Dateien

SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
poisoning-inference-extraction
description
Plan ART poisoning, privacy inference, model inversion/reconstruction, extraction, and detector/mitigation workflows.
disable-model-invocation
true
metadata
{"disco-role":"operating"}
license
MIT
# Poisoning, inference, and extraction Use this sub-skill when the task is to plan or reason about poisoning/backdoor attacks, privacy inference attacks, model inversion/reconstruction, model extraction/stealing, or poison/backdoor detectors and mitigations. ## Route here - Poisoning and backdoor attack planning for SVMs, feature collision, backdoors, clean-label backdoors, hidden triggers, adversarial embedding, gradient matching, and sleeper-agent-style workflows. - Privacy inference workflows for membership inference, attribute inference, model inversion, and reconstruction. - Model extraction / stealing workflows such as Copycat CNN, Knockoff Nets, and Functionally Equivalent Extraction. - Poisoning detectors and mitigations such as Activation Defence, Spectral Signature Defense, Provenance Defense, RONI Defense, Neural Cleanse, and STRIP. - Questions about train/validation splits, label format, trigger construction, classifier capabilities, or query budgets for these attack families. ## Route elsewhere - Ordinary evasion attacks, preprocessing defences, and adversarial training belong to [`../evasion-and-preprocessing/SKILL.md`](../evasion-and-preprocessing/SKILL.md). - Robustness metrics, certification, tree verification, security curves, and gradient checks belong to [`../evaluation-and-certification/SKILL.md`](../evaluation-and-certification/SKILL.md). - Estimator wrapping, `clip_values`, `input_shape`, label encoding, and gradient availability belong to [`../estimators-and-models/SKILL.md`](../estimators-and-models/SKILL.md). - Installation, import readiness, and backend selection belong to [`../setup-and-backends/SKILL.md`](../setup-and-backends/SKILL.md). - Object detection, audio, malware, and other special-purpose attack families are outside this bundled runtime scope unless a reference note explicitly says otherwise. ## Operating sequence 1. Identify the attack family and the minimum estimator/data capability from [references/attack-families.md](references/attack-families.md). 2. Check detector and mitigation routing, plus backend constraints, in [references/defences-and-mitigations.md](references/defences-and-mitigations.md). 3. Use [references/troubleshooting.md](references/troubleshooting.md) for split, label, trigger, query-budget, and import issues. 4. Run [scripts/inspect_privacy_poisoning_apis.py](scripts/inspect_privacy_poisoning_apis.py) to confirm available imports and signatures before drafting a recipe. ## Bundled checks - `python scripts/inspect_privacy_poisoning_apis.py --help` - `python scripts/inspect_privacy_poisoning_apis.py --json` The helper only imports and inspects signatures; it does not train models or download data.
Auf GitHub ansehen