Data from Information Repositories - AI Security Technique
AI Security TechniqueAdversaries may leverage information repositories to mine valuable information. Information repositories are tools that allow for storage of information, typically to facilitate collaboration or information sharing between users, and can store a wide variety of data that may aid adversaries in further objectives, or direct access to the target information. Information stored in a repository may vary based on the s...
Overview
A source-backed snapshot of this AI security technique.
Adversaries may leverage information repositories to mine valuable information. Information repositories are tools that allow for storage of information, typically to facilitate collaboration or information sharing between users, and can store a wide variety of data that may aid adversaries in further objectives, or direct access to the target information.
Information stored in a repository may vary based on the specific instance or environment. Specific common information repositories include SharePoint, Confluence, and enterprise databases such as SQL Server.
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0036
- Maturity
- realized
- ATT&CK external ID
- T1213
- Priority score
- 40
Attack flow
How to read the public records connected to this technique.
Impact
Why this technique may deserve attention in the current dataset.
- Evidence levelrealized
- Mapped defenses0 ATLAS mitigation records
- Public examples1 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.
ClearviewAI Misconfiguration
Clearview AI makes a facial recognition tool that searches publicly available photos for matches. This tool has been used for investigative purposes by law enforcement agencies and other parties.
Clearview AI's source code repository, though password protected, was misconfigured to allow an arbitrary user to register an account. This allowed an external researcher to gain access to a private code repository that contained Clearview AI production credentials, keys to cloud storage buckets containing 70K video samples, and copies of its applications and Slack tokens. With access to training data, a bad actor has the ability to cause an arbitrary misclassification in the deployed model. These kinds of attacks illustrate that any attempt to secure ML system should be on top of "traditional" good cybersecurity hygiene such as locking down the system with least privileges, multi-factor authentication and monitoring and auditing.
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.