Pre-Print Repositories - AI Security Technique
AI Security TechniquePre-Print repositories, such as arXiv, contain the latest academic research papers that haven't been peer reviewed. They may contain research notes, or technical reports that aren't typically published in journals or conference proceedings. Pre-print repositories also serve as a central location to share papers that have been accepted to journals. Searching pre-print repositories provide adversaries with a relativ...
Overview
A source-backed snapshot of this AI security technique.
Pre-Print repositories, such as arXiv, contain the latest academic research papers that haven't been peer reviewed. They may contain research notes, or technical reports that aren't typically published in journals or conference proceedings. Pre-print repositories also serve as a central location to share papers that have been accepted to journals. Searching pre-print repositories provide adversaries with a relatively up-to-date view of what researchers in the victim organization are working on.
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0000.001
- Maturity
- demonstrated
- Priority score
- 30
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 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.
Evasion of Deep Learning Detector for Malware C&C Traffic
The Palo Alto Networks Security AI research team tested a deep learning model for malware command and control (C&C) traffic detection in HTTP traffic. Based on the publicly available paper by Le et al., we built a model that was trained on a similar dataset as our production model and had similar performance. Then we crafted adversarial samples, queried the model, and adjusted the adversarial sample accordingly until the model was evaded.
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.