Kaspersky Antimalware ML Model Evaded via Feature Knowledge
Attack on AI1 Research / proof of conceptAdversarial evasion
Researchers demonstrated that feature knowledge is sufficient to conduct adversarial attacks against ML-based malware detectors. The technique successfully evaded detection for most modified malware files without white-box access.
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
- Kaspersky Antimalware ML Model
- 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 · malware_detection · research
- record
- created 2026-10-18 06:56Z · updated 2026-10-18 06:56Z · json
Sources · 3
- 2023-03-31AVID-2023-V014: Cloud storage and computations have become popular platforms for deploying ML malware detectors.AVID · github.com/avidml/avid-db/blob/main/vulnerabilities/2023/AVID-2023-V014.json
- 2023-03-31Confusing Antimalware Neural NetworksMITRE ATLAS · atlas.mitre.org/studies/AML.CS0014
- 2023-03-31Article, "How to confuse antimalware neural networks. Adversarial attacks and protection"Kaspersky Securelist · securelist.com/how-to-confuse-antimalware-neural-networks-adversarial-attacks-an
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