{
 "id": "AIR-2025-0058",
 "slug": "attempted-evasion-of-machine-learning-phishing-website-detec",
 "title": "Attempted Evasion of Machine Learning Phishing Website Detection",
 "summary": "Adversaries attempted to evade commercial machine learning phishing detectors by crafting adversarial examples. The evasion technique was identified in logs from endpoint security products.",
 "type": "attack_on_ai",
 "categories": [
  "adversarial_evasion"
 ],
 "severity": 1,
 "targets": [
  "enterprise_deployment"
 ],
 "products": [
  "phishing website detection system"
 ],
 "incident_date": "2022-12",
 "incident_date_precision": "month",
 "first_reported": "2025-09-29",
 "sources": [
  {
   "url": "https://atlas.mitre.org/studies/AML.CS0032",
   "publisher": "MITRE ATLAS",
   "title": "Attempted Evasion of ML Phishing Webpage Detection System",
   "published_at": "2025-09-29"
  },
  {
   "url": "https://arxiv.org/abs/2212.14315",
   "publisher": "arXiv",
   "title": "\"Real Attackers Don't Compute Gradients\": Bridging the Gap Between Adversarial ML Research and Practice",
   "published_at": "2025-09-29"
  },
  {
   "url": "https://real-gradients.github.io/",
   "publisher": "real-gradients.github.io",
   "title": "Real Attackers Don't Compute Gradients Supplementary Resources",
   "published_at": "2025-09-29"
  }
 ],
 "source_count": 3,
 "confidence": 0.9,
 "status": "published",
 "origin": "seed_atlas",
 "tags": [
  "adversarial-evasion",
  "phishing-detection",
  "ml-security"
 ],
 "created_at": "2026-10-18T06:55:08.239Z",
 "updated_at": "2026-10-18T06:55:08.239Z"
}