VirusTotal Poisoning - AI Case Study
AI Case StudyMcAfee Advanced Threat Research noticed an increase in reports of a certain ransomware family that was out of the ordinary. Case investigation revealed that many samples of that particular ransomware family were submitted through a popular virus-sharing platform within a short amount of time. Further investigation revealed that based on string similarity the samples were all equivalent, and based on code similarit...
Overview
Risk patterns
Patterns found in the case record and its linked vulnerabilities.
- 1Dominant ATLAS tactic. Resource Development appears in 1 case steps.
- 2Multiple attack methods. The case connects to 4 unique AI attack methods.
Procedure timeline
Search the case steps or filter them by attacker goal.
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Resource Development The actor obtained metame, a simple metamorphic code engine for arbitrary executables.
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AI Attack Staging
Step 2
Craft Adversarial Data
The actor used a malware sample from a prevalent ransomware family as a start to create "mutant" variants.
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Initial Access
Step 3
Data
The actor uploaded "mutant" samples to the platform.
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Persistence
Step 4
Poison Training Data
Several vendors started to classify the files as the ransomware family even though most of them won't run. The "mutant" samples poisoned the dataset the ML model(s) use to identify and classify this ransomware family.
Mitigations
Defenses connected to the attack methods in this case.
AI Bill of Materials
An AI Bill of Materials (AI BOM) contains a full listing of artifacts and resources that were used in building the AI. The AI BOM can help mitigate supply chain risks and enable rapid response to reported vulnerabilities.
This can include maintaining dataset provenance, i.e. a detailed history of datasets used for AI applications. The history can include information about the dataset source as well as well as a complete record of any modifications.
Adversarial Input Detection
Detect and block adversarial inputs or atypical queries that deviate from known benign behavior, exhibit behavior patterns observed in previous attacks or that come from potentially malicious IPs. Incorporate adversarial detection algorithms into the AI system prior to the AI model.
Control Access to AI Models and Data at Rest
Establish access controls on internal model registries and limit internal access to production models. Limit access to training data only to approved users.
Control Access to AI Models and Data in Production
Require users to verify their identities before accessing a production model. Require authentication for API endpoints and monitor production model queries to ensure compliance with usage policies and to prevent model misuse.
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Source evidence
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Original source
Original source links
Open the MITRE ATLAS data and public references used for this case study.