Pre-Launch Security Assessment for LLM Features

• CTO or VP Engineering
• CISO or Head of Information Security
• Product Manager for AI features
• Application Security Lead

Fintech teams are adding LLM assistants to apps and merchant dashboards: transaction search, spend summaries, support chat and dispute help. These features read customer data and sometimes call internal APIs, so a well-crafted prompt can become a data breach. Traditional penetration tests and scanners do not test how the model behaves.

We run a time-boxed security assessment of your LLM feature in a staging environment before it reaches customers. First we map the feature: model, system prompt, retrieval sources, tools and the data each can reach. Then we test it against the OWASP Top 10 for LLM Applications with automated attack libraries and manual, multi-turn attempts written for payments: injection through transaction notes and merchant names, attempts to read other accounts, system prompt extraction, jailbreaks towards financial advice and unsafe rendering of model output in your UI. Every finding comes with the exact prompts and responses, a severity rating and a practical fix. After you fix, we retest.

• Architecture and data-flow review of prompts, retrieval sources, tools and permissions
• Automated attack runs for prompt injection, jailbreaks and system prompt extraction
• Manual multi-turn testing with payments-specific scenarios in English, Hindi and Hinglish
• Authorisation testing of every tool and API the model can call, using paired test accounts
• Output-handling checks for script injection, link injection and markdown rendering in your UI
• Findings report mapped to OWASP Top 10 for LLM Applications, followed by a retest

1. 01Scope and threat modelWe agree the feature, environment, test accounts and rules of engagement, and map what data and tools the model can reach.
2. 02Automated attack runsAttack libraries send large sets of injection, jailbreak and extraction prompts to the staging endpoint.
3. 03Manual scenario testingTesters run multi-turn, payments-specific attacks, including indirect injection through data fields the model reads.
4. 04Validate and rateEach suspected issue is reproduced, confirmed and rated by impact and likelihood.
5. 05Report and briefYou receive findings with evidence and fixes, plus a briefing for engineering and risk.
6. 06RetestAfter fixes, we rerun the failed cases and record which issues are closed.

1. Attack engine
   • Automated prompt attack library
   • Payments scenario playbooks
   • Seeded indirect-injection test data
2. Target in staging
   • LLM feature endpoint
   • Tools and internal APIs the model calls
   • Paired test customer and merchant accounts
3. Observation
   • Prompt and response capture
   • Tool-call audit logging
   • Detectors for leaked account identifiers
4. Analysis
   • Finding reproduction and validation
   • Severity rating against OWASP LLM categories
5. Reporting
   • Evidence report with remediation guidance
   • Retest tracker

A human tester reproduces and rates every automated finding before it reaches the report. Your security and engineering leads decide which fixes ship and whether the feature launches.

• Staging environment of your app or merchant dashboard
• LLM gateway or provider logs such as Azure OpenAI, OpenAI, Anthropic, Gemini or self-hosted models
• Jira or Linear for findings
• CI pipeline for optional regression tests
• Secrets manager for test credentials

• Testing only in staging or an agreed environment, with synthetic customer data
• Written rules of engagement covering scope, timing and stop conditions
• No real customer data extracted or kept; any accidental exposure reported at once
• Agreed rate limits to avoid cost spikes or provider throttling
• Evidence stored encrypted and deleted after the agreed period

• Helps you evidence pre-deployment testing of AI systems, in line with the testing and governance recommendations of RBI's FREE-AI framework.
• Findings map to the OWASP Top 10 for LLM Applications and can feed a NIST AI RMF or ISO/IEC 42001 risk register.

• Fintech & Payments

  AI Agent Permission and Tool-Use Security Review

  Find out what your AI agents can actually do: which tools, credentials and money-moving actions they reach, where approvals sit, and whether injected text can make them misuse tools.

• Fintech & Payments

  DPDP-Aligned AI Data Handling Assessment

  Map where personal data flows through your AI features, prompts, logs, vector stores and vendors, and get a gap register and evidence pack against DPDP Act duties for your DPO.

• SaaS & AI Product Companies

  LLM Application Penetration Test (OWASP LLM Top 10)

  A scoped pentest of your LLM feature and the app around it: prompt injection, data disclosure, output handling, excessive agency and system-prompt leakage, with reproducible evidence.

• Banking

  Red-Teaming for Bank Chatbots and Voice Agents

  Adversarial testing of bank chatbots and voice agents for prompt injection, data leakage, account-takeover social engineering and unsafe advice, in English, Hindi and Hinglish.