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NiyamAI - An Intent-Bound AI Agent with Cryptographically Verifiable Guardrails using Zero-Knowledge Proofs

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TL;DR - Niyam-AI is a guardrail framework for autonomous LLM agents that locks permitted tools/constraints into a SHA-256-committed "Intent Contract" and gates every tool call behind an isolated Judge model whose verdict is attested by a zk-SNARK proof. It matters because it shifts agent safety from unverifiable in-process checks to cryptographically verifiable enforcement that third parties can audit without seeing model weights.

  • Architecture: session-start Intent Contract (SHA-256 commitment) + interception of every tool call, validated by an isolated Judge model; zk-SNARK proof generated via EZKL, and the tool executes only after proof verification.
  • Results on 2,000 Agent-SafetyBench scenarios (5-fold stratified CV): F1 88.5%, 1.1% false-positive rate, bootstrap 95% CI [85.19%, 91.88%] (N=1000).
  • McNemar's exact paired tests favor Niyam-AI over NeMo Guardrails (390 wins vs 20), Llama Prompt Guard 2 (115 vs 13), and GPT-OSS-Safeguard (384 vs 19), all p < 0.0001.
  • Cost: ~2260.6 ± 218.4 ms proof generation per approved action vs ~53.1 ± 11.8 ms verification; authors caveat that their classifier is adapted to Agent-SafetyBench while baselines are zero-shot (Section IV.C).

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NiyamAI - An Intent-Bound AI Agent with Cryptographically Verifiable Guardrails using Zero-Knowledge Proofs

arXiv cs.AI Aditya Katkar, Om Karkele, Kartik Mandhane, Manisha More, Yash Kashid 2026-08-07 arXiv:2608.07167
Public signals Semantic Scholar citations 1 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 1 · Influential citations 0 X · N/A Fetched 2026-09-02 14:24:14.707012 UTC

TL;DR - Niyam-AI is a guardrail framework for autonomous LLM agents that locks permitted tools/constraints into a SHA-256-committed "Intent Contract" and gates every tool call behind an isolated Judge model whose verdict is attested by a zk-SNARK proof. It matters because it shifts agent safety from unverifiable in-process checks to cryptographically verifiable enforcement that third parties can audit without seeing model weights.

  • Architecture: session-start Intent Contract (SHA-256 commitment) + interception of every tool call, validated by an isolated Judge model; zk-SNARK proof generated via EZKL, and the tool executes only after proof verification.
  • Results on 2,000 Agent-SafetyBench scenarios (5-fold stratified CV): F1 88.5%, 1.1% false-positive rate, bootstrap 95% CI [85.19%, 91.88%] (N=1000).
  • McNemar's exact paired tests favor Niyam-AI over NeMo Guardrails (390 wins vs 20), Llama Prompt Guard 2 (115 vs 13), and GPT-OSS-Safeguard (384 vs 19), all p < 0.0001.
  • Cost: ~2260.6 ± 218.4 ms proof generation per approved action vs ~53.1 ± 11.8 ms verification; authors caveat that their classifier is adapted to Agent-SafetyBench while baselines are zero-shot (Section IV.C).
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