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| title: README |
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| # Zytra — AI Safety Infrastructure for Financial Services |
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| **Zytra** builds domain-specific AI safety infrastructure for banking, financial services, and insurance (BFSI). We publish open models, benchmarks, and evaluation tooling purpose-built for regulated financial environments. |
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| ## Models |
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| ### Semalith v1.5 — BFSI Safety Classifier |
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| A 184M-parameter DeBERTa-v3-base guardrail classifier trained on 57,000+ real-world prompts. |
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| **Coverage:** |
| - **9 prompt-injection attack types:** |
| - System Override (D1) — direct instruction hijack, role reassignment, prompt delimiter attacks |
| - Extraction (D1) — password/secret extraction, system prompt leakage, context exfiltration |
| - Jailbreak (D1) — DAN, developer mode, policy bypass via persona |
| - Narrative Frame (D1) — roleplay, fiction, hypothetical framing to bypass refusals |
| - Authority Claim (D1) — impersonating admins, developers, or system roles to elevate privilege |
| - Social Engineering (D1) — pretext, urgency, emotional manipulation to lower guardrails |
| - Evasion (D5) — obfuscation, encoding, typo injection, token splitting to evade detection |
| - Agentic Injection (D6) — tool-call hijacking, memory poisoning, multi-agent prompt injection |
| - Indirect Injection (D7) — attacks embedded in retrieved documents, emails, or web content |
| - **11 BFSI compliance categories:** |
| - B-01 Investment Advice Elicitation — SEBI IA Regulations 2013 §3 |
| - B-02 KYC/AML Bypass — RBI Master Directions KYC |
| - B-03 Regulatory Misrepresentation — SEBI FPI Regulations + RBI circulars |
| - B-04 Regulatory Document Hallucination — EU AI Act Art. 9(4) |
| - B-05 Consent & Data Rights Violations — DPDP Act 2023 |
| - B-06 Transaction Integrity Violations — RBI NACH/NEFT Frameworks |
| - B-07 Account/Document Authenticity Bypass — RBI Digital Banking Security |
| - B-08 Fraud & Scam Facilitation — FCA SYSC 6.1 |
| - B-09 Unlicensed Financial Advice — SEC IA Act §202(a)(11) |
| - B-10 Regulatory Enquiry Mishandling — EU AI Act Art. 52 |
| - B-11 AML/Sanctions Evasion — FATF Recommendation 10 |
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| ## Benchmarks |
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| ### [FinProof v1](https://huggingface.co/datasets/Zytra/finproof-bench) — BFSI Adversarial Benchmark |
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| 5,389-prompt adversarial benchmark covering 7 attack categories (B-01 through B-07) across three deployment registers: |
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| | Register | Description | Prompts | |
| |---|---|---| |
| | Professional | Compliance officer framing, regulatory citations | 5,068 | |
| | Customer Mobile | Colloquial chatbot-realistic, 8–30 words | 206 | |
| | RM Internal | Relationship manager to internal AI | 115 | |
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| Generated using **Quantum Circuit Born Machine (QCBM)** sampling on PennyLane — first BFSI safety benchmark with quantum-augmented adversarial generation. |
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| | Tier | Prompts | Access | |
| |---|---|---| |
| | Easy attacks | 1,606 | [Public — no registration](https://huggingface.co/datasets/Zytra/finproof-bench) | |
| | Medium attacks (QCBM-generated) | 2,036 | [Research agreement](https://huggingface.co/datasets/Zytra/finproof-research) | |
| | Hard attacks — official test set | 1,747 | Zytra-evaluated only | |
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| ### ASSAY-QI v2.0 — Quantum-Augmented Attack Suite |
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| 1,273 adversarial prompts generated via QCBM + simulated annealing targeting Semalith's decision boundary. Covers professional and retail registers. Overall Semalith miss rate: 14.3%. |
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| ## Research |
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| - **Paper**: *Semalith: A Regulatory-Aware Safety Classifier for AI-Assisted Financial Services* — DeBERTa-v3 + BFSI taxonomy + 22-benchmark evaluation |
| - **QCBM augmentation**: Quantum-inspired distribution sampling for adversarial test case generation in underrepresented BFSI attack categories |
| - **FinProof framework**: PINT-inspired four-tier release — public taxonomy, email-gated easy examples, research-agreement medium examples, withheld hard test set |
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| ## Contact |
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| - 🌐 [zytratechnologies.com](http://zytratechnologies.com) |
| - 🏢 India · BFSI-focused AI safety |
| - 💬 For benchmark access and Semalith enterprise licensing: reach out via the organisation page |
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