#black-box-testing News & Analysis

3 articles tagged with #black-box-testing. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBearisharXiv – CS AI · May 297/10

🧠

KBF: Knowledge Boundary as Fingerprint for Language Model and Black-Box API Auditing

Researchers introduce KBF, a black-box auditing protocol that detects fraudulent LLM API substitutions by analyzing model behavior at knowledge boundaries. Testing across 16 production endpoints revealed all economically relevant model swaps without false positives, and identified inconsistencies in 7 of 27 model cells across major AI platforms, particularly affecting Claude premium endpoints.

🧠 Claude

AINeutralarXiv – CS AI · Jun 96/10

🧠

Auditing Proprietary Alignment in Large Language Models: A Comparative Framework Without a Ground-Truth Standard

Researchers propose a statistical framework to detect proprietary alignment—intentional, undisclosed policies—in large language models by comparing their behavioral outputs against baseline models. The approach enables systematic auditing of black-box LLMs without requiring ground-truth standards, addressing growing concerns about model censorship and bias embedded by providers.

AINeutralarXiv – CS AI · Apr 156/10

🧠

Robust Explanations for User Trust in Enterprise NLP Systems

Researchers propose a black-box robustness evaluation framework for NLP explanations, revealing that decoder-based LLMs produce 73% more stable explanations than encoder models like BERT. The study establishes practical cost-robustness tradeoffs that help organizations select models for compliance-sensitive applications before deployment.

🧠 Llama