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Source note2 min read

Rethinking Penetration Testing for AI-Enabled Systems: From Resource Compromise to Behavioral Objective Violation

Source

arXiv

arxiv.org/abs/2607.14006v1

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Source

Plain-Language Brief

The authors propose a new approach to penetration testing for AI-enabled systems. Instead of focusing on direct compromises of infrastructure, they suggest evaluating if adversaries can manipulate system behavior through various means such as prompt injection or data poisoning to violate operational objectives.

What the Document Says

  • The paper suggests a shift in focus for penetration testing in AI-enabled systems.
  • They define an AI-enabled system as one where learned models affect operational outcomes.
  • AI-enabled penetration is defined as the feasible induction of AI-governed behavior that violates one or more operational objectives under a specific threat model.
  • The new approach includes a testing workflow with steps like identifying operational objectives, mapping AI-governed behavior, and reporting evidence linking adversarial action to objective violation.
  • The authors provide an example of an AI-enabled security operations center assistant being potentially compromised through behavioral influence rather than infrastructure compromise.

Key Concepts Mentioned

  • AI-enabled systems
  • penetration testing
  • objective-driven behavioral evaluation
  • adversarial pathways
  • prompt injection
  • data poisoning
  • sensor manipulation
  • retrieval poisoning
  • tool misuse
  • agentic misalignment

Claims Made by the Document

  • Traditional penetration testing is not sufficient for AI-enabled systems.
  • Adversaries can influence AI-enabled system behavior without directly compromising infrastructure.

Evidence Used

  • The authors state that adversaries may influence prompts, retrieved content, sensor inputs, training data, memory, tools, or human-AI interaction loops to alter system behavior.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Penetration testing traditionally evaluates whether adversaries can exploit weaknesses in software, infrastructure, configurations, or operational controls to achieve security-relevant compromise. This paradigm remains necessary for AI-enabled systems, but it is no longer sufficient. In such systems, adversaries may influence prompts, retrieved content, sensor inputs, training data, memory, tools, or human-AI interaction loops to alter system behavior without directly compromising the underlying infrastructure. This paper reframes penetration testing for AI-enabled systems as objective-driven behavioral evaluation. We define an AI-enabled system as one in which learned models materially influence behavior affecting operational outcomes, and we define AI-enabled penetration as the feasible induction of AI-governed behavior that violates one or more operational objectives under an explicit threat model. This definition preserves conventional penetration testing while extending it to adversarial pathways such as prompt injection, indirect prompt injection, data poisoning, sensor manipulation, retrieval poisoning, tool misuse, and agentic misalignment. We further propose a testing workflow that identifies operational objectives, maps AI-governed behavior, analyzes adversarial influence surfaces, defines behavioral failure criteria, executes scenario-based tests, and reports evidence linking adversarial action to objective violation. A running example involving an AI-enabled security operations center assistant illustrates how penetration may occur through behavioral influence rather than infrastructure compromise. Together, the definitions, workflow, and example provide a technical framework for evaluating adversarial success in deployed AI-enabled systems.