What question did this study set out to answer?

The research aims to develop a protocol that identifies and mitigates specification vulnerabilities in AI systems before code generation.

April 30, 20260 citationsOpen Access

Contract Adversarial Protocol: A Framework for Bulletproof AI Specifications

CAP: The Contract Adversarial Protocol — Making AI Specifications Bulletproof

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Introduces a framework that improves AI code specification security by identifying loopholes, enhancing system robustness.

The research aims to develop a protocol that identifies and mitigates specification vulnerabilities in AI systems before code generation.
Introduced a three-role adversarial framework involving Challenger, Attacker, and Arbiter.
Implemented five advanced attack dimensions to test AI specification robustness: code-level probing, domain-specific black swan injection, multimodal adversarial scenarios, collusion detection, and attention budget 2.0.
Analyzed potential loopholes systematically to strengthen AI specifications.
Identified critical specification loopholes through a comprehensive adversarial approach.
Enhanced the security of AI specifications by introducing five advanced attack dimensions.
Demonstrated effectiveness in closing gaps before AI code generation.

Yi Fu (2026) studied this question.

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April 30, 20260 citationsOpen Access

CAP: The Contract Adversarial Protocol — Making AI Specifications Bulletproof

View Full Paper

Ask AI

Bookmark

Authors

Introduces a framework that improves AI code specification security by identifying loopholes, enhancing system robustness.

The research aims to develop a protocol that identifies and mitigates specification vulnerabilities in AI systems before code generation.
Introduced a three-role adversarial framework involving Challenger, Attacker, and Arbiter.
Implemented five advanced attack dimensions to test AI specification robustness: code-level probing, domain-specific black swan injection, multimodal adversarial scenarios, collusion detection, and attention budget 2.0.
Analyzed potential loopholes systematically to strengthen AI specifications.
Identified critical specification loopholes through a comprehensive adversarial approach.
Enhanced the security of AI specifications by introducing five advanced attack dimensions.
Demonstrated effectiveness in closing gaps before AI code generation.

Yi Fu (2026) studied this question.

View Full Paper

Ask AI

Bookmark