AI Artifacts - AI Security Technique
AI Security TechniqueAdversaries may achieve full system compromise by introducing malicious AI artifacts, such as models or data, that contain embedded malware or other malicious commands. AI artifacts are often stored in model registries or data stores and may affect many systems that pull these resources. Malicious content stored in AI artifacts may be executed as a result of unsafe serialization formats (e.g. Python pickle) or by...
Overview
A source-backed snapshot of this AI security technique.
Adversaries may achieve full system compromise by introducing malicious AI artifacts, such as models or data, that contain embedded malware or other malicious commands. AI artifacts are often stored in model registries or data stores and may affect many systems that pull these resources.
Malicious content stored in AI artifacts may be executed as a result of unsafe serialization formats (e.g. Python pickle) or by other bundled scripts or notebooks.
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0112.001
- Maturity
- feasible
- Priority score
- 10
Attack flow
How to read the public records connected to this technique.
Impact
Why this technique may deserve attention in the current dataset.
- Evidence levelfeasible
- Mapped defenses0 ATLAS mitigation records
- Public examples0 linked case study records
- Research risks0 related MIT AI Risk records above the confidence threshold
- Vulnerabilities0 linked CVE records
Mitigations
Defenses that may help against this attack.
Case studies
Examples from public reports and exercises.
Source evidence
Original public records and references for this page.
Original source
Original source links
Open the public records and source datasets used for this page.