Which of the following AI system vulnerabilities is MOST easily exploited by adversaries?

Weak controls for access to the AI model represent a fundamental cybersecurity vulnerability. Exploiting poor access controls (e.g., default credentials, lack of multi-factor authentication, insecure APIs) typically requires less specialized knowledge and fewer resources than executing AI-specific attacks. An adversary can leverage common hacking tools and techniques to gain unauthorized access, which can lead to model theft, data poisoning, or system manipulation. This makes it the most accessible and, therefore, most easily exploited vulnerability compared to the other, more technically complex options.

A. Exploiting inaccurate generalizations requires feeding the model specifically crafted data over time to influence its learning, which is a complex and often slow process.

C. While common, launching a denial-of-service (DoS) attack that successfully overwhelms modern, resilient infrastructure often requires significant computational resources and coordination.

D. Creating subtle input modifications (adversarial examples) to cause inappropriate outputs is technically sophisticated and typically requires significant knowledge of the model's architecture and parameters.

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1. ISACA

"Auditing Artificial Intelligence

" 2019. This guide emphasizes that foundational IT controls are critical for AI systems. It states

"Traditional IT general controls (ITGCs) are still relevant and applicable to AI systems. These controls

such as access management

change management and IT operations

are the foundation on which AI-specific controls are built." (Page 31

Section: AI-Specific Controls). This supports the premise that a failure in a foundational control like access management is a primary and exploitable risk.

2. ENISA (European Union Agency for Cybersecurity)

"AI Cybersecurity Challenges: A Taxonomy of Threats and Vulnerabilities

" December 2020. This report provides a threat taxonomy for AI systems. It lists "Unauthorized Access" as a key threat

which can be caused by "insufficient access control." This is presented as a foundational threat that enables other attacks

distinct from more complex

AI-specific attacks like "Evasion" (adversarial examples). (Page 16

Table 1).

3. NIST

"AI Risk Management Framework (AI RMF 1.0)

" NIST AI 100-1

January 2023. The framework's core practices for managing AI risks include the sub-category "Govern 4: Policy and Procedures

" which explicitly mentions "access control policies and procedures." The document stresses that securing AI systems involves robust cybersecurity hygiene

where access control is a cornerstone

indicating its critical and primary nature as a vulnerability. (Page 23

Section 4.1).