{
 "id": "AIR-2023-0007",
 "slug": "palo-alto-networks-security-team-demonstrates-evasion-of-dee",
 "title": "Palo Alto Networks security team demonstrates evasion of deep learning malware detector",
 "summary": "Researchers built a replica deep learning model for malware traffic detection and crafted adversarial HTTP samples to successfully evade it. The finding highlights vulnerability to adversarial perturbation.",
 "type": "attack_on_ai",
 "categories": [
  "adversarial_evasion"
 ],
 "severity": 1,
 "targets": [
  "enterprise_deployment"
 ],
 "products": [
  "Palo Alto Networks C&C Traffic Detector"
 ],
 "incident_date": "2023-03-31",
 "incident_date_precision": "day",
 "first_reported": "2023-03-31",
 "sources": [
  {
   "url": "https://github.com/avidml/avid-db/blob/main/vulnerabilities/2023/AVID-2023-V001.json",
   "publisher": "AVID",
   "title": "AVID-2023-V001: The Palo Alto Networks Security AI research team tested a deep learning model for malware command and control (C&C) traffic detection in HTTP traffic.",
   "published_at": "2023-03-31"
  },
  {
   "url": "https://atlas.mitre.org/studies/AML.CS0000",
   "publisher": "MITRE ATLAS",
   "title": "Evasion of Deep Learning Detector for Malware C&C Traffic",
   "published_at": "2023-03-31"
  },
  {
   "url": "https://arxiv.org/abs/1802.03162",
   "publisher": "arXiv",
   "title": "Le, Hung, et al. \"URLNet: Learning a URL representation with deep learning for malicious URL detection.\" arXiv preprint arXiv:1802.03162 (2018).",
   "published_at": "2023-03-31"
  }
 ],
 "source_count": 3,
 "confidence": 0.95,
 "status": "published",
 "origin": "seed_avid",
 "tags": [
  "adversarial-evasion",
  "malware-detection"
 ],
 "created_at": "2026-10-18T06:56:23.100Z",
 "updated_at": "2026-10-18T06:56:23.100Z"
}