catalog / Cybersecurity / Counterfit — ML Model Security Testing CLI
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Counterfit — ML Model Security Testing CLI

Microsoft Azure's CLI for adversarial robustness testing of ML models: evasion, poisoning, extraction attacks.

Instalaciones17k
⟳ upstream v1.1.0 · updated 3y ago
Repositorio fuente
! Grade B · 75/100 · ReviewSecurity assessment
No compromise signals12capabilities surfaced1known CVE9of 20 OWASP controls clear
Potentially unbounded loopVulnerable dependenciesExternal endpoints declared · expectedExternal endpoints declared · expected
scanned 18d ago · partialosv · gitleaks · opengrep · picklescan + heuristicsfull breakdown in the Security tab ↓

Counterfit — ML Model Security Testing CLI

Counterfit is a Microsoft Azure open-source CLI that provides a generic automation layer for assessing AI/ML model security. It wraps popular attack libraries (ART, TextAttack, Augly) behind a single interface so red teams can probe any model — whether it's a REST endpoint, a local PyTorch model, or a cloud API.

Key features

  • Unified interface over 20+ attack algorithms (evasion, poisoning, model extraction, inference)
  • Works against black-box REST endpoints — no source code required
  • Replay attack logs for reproducible security reports
  • Out-of-the-box target adapters for image classifiers and NLP models
  • Built-in logging to Azure Monitor or local JSON

Quick start

pip install counterfit
cf # launch interactive CLI
# Inside the CLI:
list targets
set_target common-cartpole
list attacks
set_attack art-boundary
run
npx ai-supply add counterfit-ml-security-cli

Curated mirror of the open-source Counterfit (MIT). Get it from the source.

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