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🤖 AI × Crypto🟢 BullishImportance 7/10
IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation
arXiv – CS AI|Yanpei Guo, Wenjie Qu, Linyu Wu, Shengfang Zhai, Lionel Z. Wang, Ming Xu, Yue Liu, Binhang Yuan, Dawn Song, Jiaheng Zhang||3 views
🤖AI Summary
Researchers introduce IMMACULATE, a framework that audits commercial large language model API services to detect fraud like model substitution and token overbilling without requiring access to internal systems. The system uses verifiable computation to audit a small fraction of requests, achieving strong detection guarantees with less than 1% throughput overhead.
Key Takeaways
- →IMMACULATE can detect economically motivated fraud in LLM services including model substitution, quantization abuse, and token overbilling.
- →The framework operates without requiring trusted hardware or access to model internals, making it practical for real-world deployment.
- →By selectively auditing only a small fraction of requests, the system maintains strong detection guarantees while minimizing performance impact.
- →Experiments demonstrate reliable distinction between benign and malicious executions with under 1% throughput overhead.
- →The open-source code availability enables broader adoption and verification of the auditing framework.
#llm-auditing#verifiable-computation#ai-security#model-verification#cryptographic-auditing#api-fraud-detection#blockchain-ai#zero-knowledge#ai-transparency
Read Original →via arXiv – CS AI
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