Adversarial Evasion of Kaspersky Antimalware Neural Networks
Attack on AI1 Research / proof of conceptAdversarial evasion
Researchers exploited feature knowledge under a gray-box scenario to successfully evade detection for adversarially modified malware files. The work demonstrated how feature knowledge alone is sufficient for adversarial attacks on security ML models.
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
- incident date
- 23 Jun 2021
- first reported
- 3 Feb 2022
- origin
- MITRE ATLASDerived from MITRE ATLAS (Apache 2.0, © MITRE).
- classifier confidence
- 0.95 · published at or above 0.80; see the methodology
- tags
- adversarial-evasion · malware-detection · gray-box
- record
- created 2026-10-18 06:55Z · updated 2026-10-18 06:55Z · json
Sources · 2
- 2022-02-03Confusing Antimalware Neural NetworksMITRE ATLAS · atlas.mitre.org/studies/AML.CS0014
- 2022-02-03Article, "How to confuse antimalware neural networks. Adversarial attacks and protection"Kaspersky Securelist · securelist.com/how-to-confuse-antimalware-neural-networks-adversarial-attacks-an
Titles and links belong to their publishers. The summary above is written by GCD AI RADAR in its own words; data is CC BY-SA 4.0.
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