{
  "slug": "cleverhans",
  "name": "Cleverhans",
  "description": "Cleverhans is a research lab dedicated to advancing the security and privacy of machine learning. It conducts research and releases open-source code artifacts to empower ML developers and engineers to design and develop secure and trustworthy ML systems.",
  "url": "https://optimly.ai/brand/cleverhans",
  "websiteUrl": "https://cleverhans.io/",
  "logoUrl": "https://logo.clearbit.com/cleverhans.io",
  "baiScore": null,
  "bai_tier_status": "active",
  "bai_score_status": "active",
  "archetype": null,
  "archetype_status": "active",
  "category": "AI Safety and Alignment Research",
  "categorySlug": null,
  "keyFacts": [],
  "aiReadiness": [],
  "competitors": [],
  "competitorsProse": null,
  "inboundCompetitors": [],
  "aiAlternatives": [],
  "parentBrand": null,
  "subBrands": [],
  "updatedAt": "2026-08-23T08:12:52.507Z",
  "verifiedVitals": {
    "website": "https://cleverhans.io",
    "category": "Research Lab",
    "what_it_does": "Cleverhans is a research lab focused on advancing the security and privacy of machine learning. It empowers ML developers and engineers to develop and design secure ML systems by conducting research, publishing papers, and releasing code artifacts.",
    "primary_audience": "ML developers and engineers",
    "core_product": "Open-source library for benchmarking machine learning model vulnerability to adversarial examples, and research outputs including publications and code artifacts related to ML security and privacy.",
    "pricing_model": {
      "kind": "free",
      "detail": "Funded by sponsors"
    },
    "parent_ownership": "University of Toronto and the Vector Institute"
  },
  "intentTags": {
    "problemIntents": [
      "Fragile ML predictions",
      "Untrustworthy ML systems",
      "Lack of ML system accountability",
      "ML security vulnerabilities",
      "ML privacy concerns",
      "Dangerous ML errors",
      "Unsecure ML systems"
    ],
    "solutionIntents": [
      "Secure machine learning development",
      "Privacy-preserving machine learning",
      "Robust AI systems",
      "Adversarial machine learning research",
      "ML trustworthiness solutions",
      "Empowering secure ML design"
    ],
    "evaluationIntents": [
      "Evaluating ML security",
      "Assessing ML privacy",
      "Benchmarking adversarial robustness",
      "Auditing ML systems for trust",
      "ML model interpretability assessment",
      "ML system accountability evaluation"
    ]
  },
  "businessProfileClaims": [],
  "timestamp": 1787551892524
}