GCD AI RADAR
incidents/AIR-2020-0032

Evasion of Deep Learning Detector for Malware C&C Traffic

Attack on AI1 Research / proof of conceptAdversarial evasion

Researchers crafted adversarial HTTP traffic samples to successfully evade a deep learning model designed for malware command and control detection. The exercise highlighted robustness flaws in AI-based network traffic analysis.

Details

type
Attack on AI
An AI model, agent or AI-powered product was the thing exploited, abused or leaked.
categories
  • Adversarial evasion
    Inputs crafted to make a model misclassify or miss detections (perturbations, evasion of AI-based defences).
owasp llm top 10
no entry for these categories
mitre atlas
Both rows are derived from the type and categories (OWASP 2025 edition, ATLAS release 2026.09), not classified per record. How
severity
1 · Research / proof of concept
Demonstrated by researchers; no known real-world exploitation.
targets
Enterprise deployment
products
Palo Alto Networks malware detection system
incident date
1 Jan 2020
first reported
15 Dec 2020
origin
MITRE ATLAS
Derived from MITRE ATLAS (Apache 2.0, © MITRE).
classifier confidence
0.95 · published at or above 0.80; see the methodology
tags
adversarial-evasion · deep-learning · cnc-traffic
record
created 2026-10-18 06:55Z · updated 2026-10-18 06:55Z · json

Sources · 2

  1. 2020-12-15
    Evasion of Deep Learning Detector for Malware C&C Traffic
    MITRE ATLAS · atlas.mitre.org/studies/AML.CS0000
  2. 2020-12-15
    Le, Hung, et al. "URLNet: Learning a URL representation with deep learning for malicious URL detection." arXiv preprint arXiv:1802.03162 (2018).
    arXiv · arxiv.org/abs/1802.03162

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