Discover AI Artifacts - AI Security Technique
AI Security TechniqueAdversaries may search private sources to identify AI learning artifacts that exist on the system and gather information about them. These artifacts can include the software stack used to train and deploy models, training and testing data management systems, container registries, software repositories, and model zoos. This information can be used to identify targets for further collection, exfiltration, or disrupt...
Overview
A source-backed snapshot of this AI security technique.
Adversaries may search private sources to identify AI learning artifacts that exist on the system and gather information about them. These artifacts can include the software stack used to train and deploy models, training and testing data management systems, container registries, software repositories, and model zoos.
This information can be used to identify targets for further collection, exfiltration, or disruption, and to tailor and improve attacks.
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0007
- Maturity
- demonstrated
- Priority score
- 71
Attack flow
How to read the public records connected to this technique.
Impact
Why this technique may deserve attention in the current dataset.
- Evidence leveldemonstrated
- Mapped defenses2 ATLAS mitigation records
- Public examples2 linked case study records
- Research risks5 related MIT AI Risk records above the confidence threshold
- Vulnerabilities0 linked CVE records
Mitigations
Defenses that may help against this attack.
AML.M0005 - Control Access to AI Models and Data at Rest
Access controls can limit an adversary's ability to identify AI models, datasets, and other artifacts on a system.
AML.M0012 - Encrypt Sensitive Information
Encrypting AI artifacts can protect against adversary attempts to discover sensitive information.
Case studies
Examples from public reports and exercises.
AI Model Tampering via Supply Chain Attack
Researchers at Trend Micro, Inc. used service indexing portals and web searching tools to identify over 8,000 misconfigured private container registries exposed on the internet. Approximately 70% of the registries also had overly permissive access controls that allowed write access. In their analysis, the researchers found over 1,000 unique AI models embedded in private container images within these open registries that could be pulled without authentication.
This exposure could allow adversaries to download, inspect, and modify container contents, including sensitive AI model files. This is an exposure of valuable intellectual property which could be stolen by an adversary. Compromised images could also be pushed to the registry, leading to a supply chain attack, allowing malicious actors to compromise the integrity of AI models used in production systems.
Organization Confusion on Hugging Face
threlfall_hax, a security researcher, created organization accounts on Hugging Face, a public model repository, that impersonated real organizations. These false Hugging Face organization accounts looked legitimate so individuals from the impersonated organizations requested to join, believing the accounts to be an official site for employees to share models. This gave the researcher full access to any AI models uploaded by the employees, including the ability to replace models with malicious versions. The researcher demonstrated that they could embed malware into an AI model that provided them access to the victim organization's environment. From there, threat actors could execute a range of damaging attacks such as intellectual property theft or poisoning other AI models within the victim's environment.
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.
