Signal-based Model Access Risk Analysis for AI System Operations Security

Computer Science > Cryptography and Security
[Submitted on 17 Jul 2026]
Title:Signal-based Model Access Risk Analysis for AI System Operations Security
View PDF HTML (experimental)Abstract:Artificial intelligence (AI) systems are now ubiquitous across domains such as security, finance, healthcare, consumer technology, and large-scale cloud services, where they process massive volumes of data and make consequential decisions daily. This widespread adoption has created a broad attack surface through which adversaries can manipulate, evade, extract information from, or otherwise subvert deployed models. Depending on system design and exposure, attackers may have very different forms of access: some observe only final decisions, while others receive confidence scores, intermediate representations, or even full model parameters. While previous surveys typically organize evasion attacks into white-box, gray-box, and black-box categories based on the attacker's knowledge of model internals (architecture, parameters, gradients), this taxonomy often conflates different deployment scenarios that provide vastly different output signals, all labeled as ``black-box'' despite enabling fundamentally different attack strategies. Understanding how evasion attack strategies adapt to the specific information signals returned by deployed systems is critical for organizations making procurement and deployment decisions. To address this gap, we introduce the Signal-based Model Access Risk Taxonomy (SMART), a deployment-oriented framework that classifies attacker access according to the nature and richness of the information signals available from deployed AI systems. Using this taxonomy, we provide a structured overview of evasion attacks across progressively richer levels of information exposure, highlighting how deployment interfaces influence attack capabilities and informing more secure AI deployment and procurement decisions.
References & Citations

Loading...
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)


Connected Papers (What is Connected Papers?)


Litmaps (What is Litmaps?)


scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)


CatalyzeX Code Finder for Papers (What is CatalyzeX?)


DagsHub (What is DagsHub?)


Gotit.pub (What is GotitPub?)


Hugging Face (What is Huggingface?)


ScienceCast (What is ScienceCast?)

Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)


CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.