CNN-Based Botnet DGA Detector Bypassed via Domain Mutation
Attack on AI1 Research / proof of conceptAdversarial evasion
Researchers bypassed a convolutional neural network-based botnet domain generation algorithm detector using a generic domain name mutation technique. The method successfully evaded the ML-based detection module in testing.
Details
- type
- Attack on AIAn AI model, agent or AI-powered product was the thing exploited, abused or leaked.
- categories
- Adversarial evasionInputs crafted to make a model misclassify or miss detections (perturbations, evasion of AI-based defences).
- Adversarial evasion
- 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 conceptDemonstrated by researchers; no known real-world exploitation.
- targets
- Enterprise deployment
- products
- Palo Alto Networks DGA Detector
- incident date
- 31 Mar 2023
- first reported
- 31 Mar 2023
- origin
- AVID
- classifier confidence
- 0.90 · published at or above 0.80; see the methodology
- tags
- adversarial_evasion · dga · botnet
- record
- created 2026-10-18 06:56Z · updated 2026-10-18 06:56Z · json
Sources · 4
- 2023-03-31AVID-2023-V002: The Palo Alto Networks Security AI research team was able to bypass a Convolutional Neural Network based botnet Domain Generation Algorithm (DGA) detector using a generic domain name mutation technique.AVID · github.com/avidml/avid-db/blob/main/vulnerabilities/2023/AVID-2023-V002.json
- 2023-03-31Botnet Domain Generation Algorithm (DGA) Detection EvasionMITRE ATLAS · atlas.mitre.org/studies/AML.CS0001
- 2023-03-31Yu, Bin, Jie Pan, Jiaming Hu, Anderson Nascimento, and Martine De Cock. "Character level based detection of DGA domain names." In 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-8. IEEE, 2018.faculty.washington.edu · faculty.washington.edu/mdecock/papers/byu2018a.pdf
- 2023-03-31Degas source codeGitHub · github.com/matthoffman/degas
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